query: As a data scientist, I am currently developing an early detection system designed to identify the onset of progressive neurodegenerative diseases through the analysis of brain scans. My current dataset consists of a large volume of unlabeled data, supplemented by a limited number of labeled positive examples. My goal is to create a model that can effectively learn from this data to ensure accurate detection. Rather than relying solely on importance reweighting during the model training phase, I am aiming to create a self-learning model. This would involve the model setting its own learning pace, determining its own loss, and gaining insights from its own performance during the training process. As such, the incorporation of some form of reinforcement learning might be necessary. If I go down the route of using RL, given the nature of my dataset, I am particularly interested in reinforcement learning algorithms for hyperparameter selection that can learn from sparse reward signals. Ultimately, my goal is for my trained model to outperform other methods when applied to specific datasets related to progressive neurodegenerative diseases, particularly those containing brain images.

positive:
Deep learning is attracting significant interest in the neuroimaging
community as a means to diagnose psychiatric and neurological disorders from
structural magnetic resonance images. However, there is a tendency amongst
researchers to adopt architectures optimized for traditional computer vision
tasks, rather than design networks customized for neuroimaging data. We address
this by introducing NEURO-DRAM, a 3D recurrent visual attention model tailored
for neuroimaging classification. The model comprises an agent which, trained by
reinforcement learning, learns to navigate through volumetric images,
selectively attending to the most informative regions for a given task. When
applied to Alzheimer's disease prediction, NEURODRAM achieves state-of-the-art
classification accuracy on an out-of-sample dataset, significantly
outperforming a baseline convolutional neural network. When further applied to
the task of predicting which patients with mild cognitive impairment will be
diagnosed with Alzheimer's disease within two years, the model achieves
state-of-the-art accuracy with no additional training. Encouragingly, the agent
learns, without explicit instruction, a search policy in agreement with
standardized radiological hallmarks of Alzheimer's disease, suggesting a route
to automated biomarker discovery for more poorly understood disorders.

positive:
Purpose: AI in radiology is hindered chiefly by: 1) Requiring large annotated
data sets. 2) Non-generalizability that limits deployment to new scanners /
institutions. And 3) Inadequate explainability and interpretability. We believe
that reinforcement learning can address all three shortcomings, with robust and
intuitive algorithms trainable on small datasets. To the best of our knowledge,
reinforcement learning has not been directly applied to computer vision tasks
for radiological images. In this proof-of-principle work, we train a deep
reinforcement learning network to predict brain tumor location.
  Materials and Methods: Using the BraTS brain tumor imaging database, we
trained a deep Q network on 70 post-contrast T1-weighted 2D image slices. We
did so in concert with image exploration, with rewards and punishments designed
to localize lesions. To compare with supervised deep learning, we trained a
keypoint detection convolutional neural network on the same 70 images. We
applied both approaches to a separate 30 image testing set.
  Results: Reinforcement learning predictions consistently improved during
training, whereas those of supervised deep learning quickly diverged.
Reinforcement learning predicted testing set lesion locations with 85%
accuracy, compared to roughly 7% accuracy for the supervised deep network.
  Conclusion: Reinforcement learning predicted lesions with high accuracy,
which is unprecedented for such a small training set. We believe that
reinforcement learning can propel radiology AI well past the inherent
limitations of supervised deep learning, with more clinician-driven research
and finally toward true clinical applicability.

negative:
Purpose Supervised deep learning in radiology suffers from notorious inherent
limitations: 1) It requires large, hand-annotated data sets, 2) It is
non-generalizable, and 3) It lacks explainability and intuition. We have
recently proposed Reinforcement Learning to address all threes. However, we
applied it to images with radiologist eye tracking points, which limits the
state-action space. Here we generalize the Deep-Q Learning to a gridworld-based
environment, so that only the images and image masks are required.
  Materials and Methods We trained a Deep Q network on 30 two-dimensional image
slices from the BraTS brain tumor database. Each image contained one lesion. We
then tested the trained Deep Q network on a separate set of 30 testing set
images. For comparison, we also trained and tested a keypoint detection
supervised deep learning network for the same set of training / testing images.
  Results Whereas the supervised approach quickly overfit the training data,
and predicably performed poorly on the testing set (11\% accuracy), the Deep-Q
learning approach showed progressive improved generalizability to the testing
set over training time, reaching 70\% accuracy.
  Conclusion We have shown a proof-of-principle application of reinforcement
learning to radiological images, here using 2D contrast-enhanced MRI brain
images with the goal of localizing brain tumors. This represents a
generalization of recent work to a gridworld setting, naturally suitable for
analyzing medical images.

negative:
Deep Reinforcement Learning (DRL) is a promising approach for teaching robots
new behaviour. However, one of its main limitations is the need for carefully
hand-coded reward signals by an expert. We argue that it is crucial to automate
the reward learning process so that new skills can be taught to robots by their
users. To address such automation, we consider task success classifiers using
visual observations to estimate the rewards in terms of task success. In this
work, we study the performance of multiple state-of-the-art deep reinforcement
learning algorithms under different types of reward: Dense, Sparse, Visual
Dense, and Visual Sparse rewards. Our experiments in various simulation tasks
(Pendulum, Reacher, Pusher, and Fetch Reach) show that while DRL agents can
learn successful behaviours using visual rewards when the goal targets are
distinguishable, their performance may decrease if the task goal is not clearly
visible. Our results also show that visual dense rewards are more successful
than visual sparse rewards and that there is no single best algorithm for all
tasks.

negative:
Parkinson's disease (PD) is a degenerative and progressive neurological
condition. Early diagnosis can improve treatment for patients and is performed
through dopaminergic imaging techniques like the SPECT DaTscan. In this study,
we propose a machine learning model that accurately classifies any given
DaTscan as having Parkinson's disease or not, in addition to providing a
plausible reason for the prediction. This is kind of reasoning is done through
the use of visual indicators generated using Local Interpretable Model-Agnostic
Explainer (LIME) methods. DaTscans were drawn from the Parkinson's Progression
Markers Initiative database and trained on a CNN (VGG16) using transfer
learning, yielding an accuracy of 95.2%, a sensitivity of 97.5%, and a
specificity of 90.9%. Keeping model interpretability of paramount importance,
especially in the healthcare field, this study utilises LIME explanations to
distinguish PD from non-PD, using visual superpixels on the DaTscans. It could
be concluded that the proposed system, in union with its measured
interpretability and accuracy may effectively aid medical workers in the early
diagnosis of Parkinson's Disease.

negative:
We systematically evaluate the performance of deep learning models in the
presence of diseases not labeled for or present during training. First, we
evaluate whether deep learning models trained on a subset of diseases (seen
diseases) can detect the presence of any one of a larger set of diseases. We
find that models tend to falsely classify diseases outside of the subset
(unseen diseases) as "no disease". Second, we evaluate whether models trained
on seen diseases can detect seen diseases when co-occurring with diseases
outside the subset (unseen diseases). We find that models are still able to
detect seen diseases even when co-occurring with unseen diseases. Third, we
evaluate whether feature representations learned by models may be used to
detect the presence of unseen diseases given a small labeled set of unseen
diseases. We find that the penultimate layer of the deep neural network
provides useful features for unseen disease detection. Our results can inform
the safe clinical deployment of deep learning models trained on a
non-exhaustive set of disease classes.

negative:
Alzheimer's disease (AD) is a neurodegenerative disease that affects nearly
50 million individuals across the globe and is one of the leading causes of
deaths globally. It is projected that by 2050, the number of people affected by
the disease would more than double. Consequently, the growing advancements in
technology beg the question, can technology be used to predict Alzheimer's for
a better and early diagnosis? In this paper, we focus on this very problem.
Specifically, we have trained both ML models and neural networks to predict and
classify participants based on their speech patterns. We computed a number of
linguistic variables using DementiaBank's Pitt Corpus, a database consisting of
transcripts of interviews with subjects suffering from multiple
neurodegenerative diseases. We then trained both binary classifiers, as well as
multiclass classifiers to distinguish AD from normal aging and other
neurodegenerative diseases. We also worked on establishing the link between
specific speech factors that can help determine the onset of AD. Confusion
matrices and feature importance graphs have been plotted model-wise to compare
the performances of our models. In both multiclass and binary classification,
neural networks were found to outperform the other models with a testing
accuracy of 76.44% and 92.05% respectively. For the feature importance, it was
concluded that '%_PRESP' (present participle), '%_3S' (3rd person present tense
markers) were two of the most important speech features for our classifiers in
predicting AD.

negative:
Alzheimer's disease (AD) is a progressive and incurable neurodegenerative
disease which destroys brain cells and causes loss to patient's memory. An
early detection can prevent the patient from further damage of the brain cells
and hence avoid permanent memory loss. In past few years, various automatic
tools and techniques have been proposed for diagnosis of AD. Several methods
focus on fast, accurate and early detection of the disease to minimize the loss
to patients mental health. Although machine learning and deep learning
techniques have significantly improved medical imaging systems for AD by
providing diagnostic performance close to human level. But the main problem
faced during multi-class classification is the presence of highly correlated
features in the brain structure. In this paper, we have proposed a smart and
accurate way of diagnosing AD based on a two-dimensional deep convolutional
neural network (2D-DCNN) using imbalanced three-dimensional MRI dataset.
Experimental results on Alzheimer Disease Neuroimaging Initiative magnetic
resonance imaging (MRI) dataset confirms that the proposed 2D-DCNN model is
superior in terms of accuracy, efficiency, and robustness. The model classifies
MRI into three categories: AD, mild cognitive impairment, and normal control:
and has achieved 99.89% classification accuracy with imbalanced classes. The
proposed model exhibits noticeable improvement in accuracy as compared to the
state-fo-the-art methods.

negative:
Deep reinforcement learning (RL) can acquire complex behaviors from low-level
inputs, such as images. However, real-world applications of such methods
require generalizing to the vast variability of the real world. Deep networks
are known to achieve remarkable generalization when provided with massive
amounts of labeled data, but can we provide this breadth of experience to an RL
agent, such as a robot? The robot might continuously learn as it explores the
world around it, even while deployed. However, this learning requires access to
a reward function, which is often hard to measure in real-world domains, where
the reward could depend on, for example, unknown positions of objects or the
emotional state of the user. Conversely, it is often quite practical to provide
the agent with reward functions in a limited set of situations, such as when a
human supervisor is present or in a controlled setting. Can we make use of this
limited supervision, and still benefit from the breadth of experience an agent
might collect on its own? In this paper, we formalize this problem as
semisupervised reinforcement learning, where the reward function can only be
evaluated in a set of "labeled" MDPs, and the agent must generalize its
behavior to the wide range of states it might encounter in a set of "unlabeled"
MDPs, by using experience from both settings. Our proposed method infers the
task objective in the unlabeled MDPs through an algorithm that resembles
inverse RL, using the agent's own prior experience in the labeled MDPs as a
kind of demonstration of optimal behavior. We evaluate our method on
challenging tasks that require control directly from images, and show that our
approach can improve the generalization of a learned deep neural network policy
by using experience for which no reward function is available. We also show
that our method outperforms direct supervised learning of the reward.

negative:
With the advents of deep learning, improved image classification with complex
discriminative models has been made possible. However, such deep models with
increased complexity require a huge set of labeled samples to generalize the
training. Such classification models can easily overfit when applied for
medical images because of limited training data, which is a common problem in
the field of medical image analysis. This paper proposes and investigates a
reinforced classifier for improving the generalization under a few available
training data. Partially following the idea of reinforcement learning, the
proposed classifier uses a generalization-feedback from a subset of the
training data to update its parameter instead of only using the conventional
cross-entropy loss about the training data. We evaluate the improvement of the
proposed classifier by applying it on three different classification problems
against the standard deep classifiers equipped with existing
overfitting-prevention techniques. Besides an overall improvement in
classification performance, the proposed classifier showed remarkable
characteristics of generalized learning, which can have great potential in
medical classification tasks.

negative:
In this paper, we propose a principled deep reinforcement learning (RL)
approach that is able to accelerate the convergence rate of general deep neural
networks (DNNs). With our approach, a deep RL agent (synonym for optimizer in
this work) is used to automatically learn policies about how to schedule
learning rates during the optimization of a DNN. The state features of the
agent are learned from the weight statistics of the optimizee during training.
The reward function of this agent is designed to learn policies that minimize
the optimizee's training time given a certain performance goal. The actions of
the agent correspond to changing the learning rate for the optimizee during
training. As far as we know, this is the first attempt to use deep RL to learn
how to optimize a large-sized DNN. We perform extensive experiments on a
standard benchmark dataset and demonstrate the effectiveness of the policies
learned by our approach.

negative:
Diffusion tensor imaging (DTI) has been used to study the effects of
neurodegenerative diseases on neural pathways, which may lead to more reliable
and early diagnosis of these diseases as well as a better understanding of how
they affect the brain. We introduce an intelligent visual analytics system for
studying patient groups based on their labeled DTI fiber tract data and
corresponding statistics. The system's AI-augmented interface guides the user
through an organized and holistic analysis space, including the statistical
feature space, the physical space, and the space of patients over different
groups. We use a custom machine learning pipeline to help narrow down this
large analysis space, and then explore it pragmatically through a range of
linked visualizations. We conduct several case studies using real data from the
research database of Parkinson's Progression Markers Initiative.

negative:
We model Alzheimer's disease (AD) progression by combining differential
equations (DEs) and reinforcement learning (RL) with domain knowledge. DEs
provide relationships between some, but not all, factors relevant to AD. We
assume that the missing relationships must satisfy general criteria about the
working of the brain, for e.g., maximizing cognition while minimizing the cost
of supporting cognition. This allows us to extract the missing relationships by
using RL to optimize an objective (reward) function that captures the above
criteria. We use our model consisting of DEs (as a simulator) and the trained
RL agent to predict individualized 10-year AD progression using baseline (year
0) features on synthetic and real data. The model was comparable or better at
predicting 10-year cognition trajectories than state-of-the-art learning-based
models. Our interpretable model demonstrated, and provided insights into,
"recovery/compensatory" processes that mitigate the effect of AD, even though
those processes were not explicitly encoded in the model. Our framework
combines DEs with RL for modelling AD progression and has broad applicability
for understanding other neurological disorders.

negative:
Machine learning applications in medical imaging are frequently limited by
the lack of quality labeled data. In this paper, we explore the self training
method, a form of semi-supervised learning, to address the labeling burden. By
integrating reinforcement learning, we were able to expand the application of
self training to complex segmentation networks without any further human
annotation. The proposed approach, reinforced self training (ReST), fine tunes
a semantic segmentation networks by introducing a policy network that learns to
generate pseudolabels. We incorporate an expert demonstration network, based on
inverse reinforcement learning, to enhance clinical validity and convergence of
the policy network. The model was tested on a pulmonary nodule segmentation
task in chest X-rays and achieved the performance of a standard U-Net while
using only 50% of the labeled data, by exploiting unlabeled data. When the same
number of labeled data was used, a moderate to significant cross validation
accuracy improvement was achieved depending on the absolute number of labels
used.

negative:
Dynamic treatment recommendation systems based on large-scale electronic
health records (EHRs) become a key to successfully improve practical clinical
outcomes. Prior relevant studies recommend treatments either use supervised
learning (e.g. matching the indicator signal which denotes doctor
prescriptions), or reinforcement learning (e.g. maximizing evaluation signal
which indicates cumulative reward from survival rates). However, none of these
studies have considered to combine the benefits of supervised learning and
reinforcement learning. In this paper, we propose Supervised Reinforcement
Learning with Recurrent Neural Network (SRL-RNN), which fuses them into a
synergistic learning framework. Specifically, SRL-RNN applies an off-policy
actor-critic framework to handle complex relations among multiple medications,
diseases and individual characteristics. The "actor" in the framework is
adjusted by both the indicator signal and evaluation signal to ensure effective
prescription and low mortality. RNN is further utilized to solve the
Partially-Observed Markov Decision Process (POMDP) problem due to the lack of
fully observed states in real world applications. Experiments on the publicly
real-world dataset, i.e., MIMIC-3, illustrate that our model can reduce the
estimated mortality, while providing promising accuracy in matching doctors'
prescriptions.

negative:
Background: Alzheimers disease is a progressive neurodegenerative disorder
and the main cause of dementia in aging. Hippocampus is prone to changes in the
early stages of Alzheimers disease. Detection and observation of the
hippocampus changes using magnetic resonance imaging (MRI) before the onset of
Alzheimers disease leads to the faster preventive and therapeutic measures.
Objective: The aim of this study was the segmentation of the hippocampus in
magnetic resonance (MR) images of Alzheimers patients using deep machine
learning method. Methods: U-Net architecture of convolutional neural network
was proposed to segment the hippocampus in the real MRI data. The MR images of
the 100 and 35 patients available in Alzheimers disease Neuroimaging Initiative
(ADNI) dataset, was used for the train and test of the model, respectively. The
performance of the proposed method was compared with manual segmentation by
measuring the similarity metrics. Results: The desired segmentation achieved
after 10 iterations. A Dice similarity coefficient (DSC) = 92.3%, sensitivity =
96.5%, positive predicted value (PPV) = 90.4%, and Intersection over Union
(IoU) value for the train 92.94 and test 92.93 sets were obtained which are
acceptable. Conclusion: The proposed approach is promising and can be extended
in the prognosis of Alzheimers disease by the prediction of the hippocampus
volume changes in the early stage of the disease.

negative:
Parkinson's Disease (PD) is a neurodegenerative disease that currently does
not have a cure. In order to facilitate disease management and reduce the speed
of symptom progression, early diagnosis is essential. The current clinical,
diagnostic approach is to have radiologists perform human visual analysis of
the degeneration of dopaminergic neurons in the substantia nigra region of the
brain. Clinically, dopamine levels are monitored through observing dopamine
transporter (DaT) activity. One method of DaT activity analysis is performed
with the injection of an Iodine-123 fluoropropyl (123I-FP-CIT) tracer combined
with single photon emission computerized tomography (SPECT) imaging. The tracer
illustrates the region of interest in the resulting DaTscan SPECT images. Human
visual analysis is slow and vulnerable to subjectivity between radiologists, so
the goal was to develop an introductory implementation of a deep convolutional
neural network that can objectively and accurately classify DaTscan SPECT
images as Parkinson's Disease or normal. This study illustrates the approach of
using a deep convolutional neural network and evaluates its performance on
DaTscan SPECT image classification. The data used in this study was obtained
through a database provided by the Parkinson's Progression Markers Initiative
(PPMI). The deep neural network in this study utilizes the InceptionV3
architecture, 1st runner up in the 2015 ImageNet Large Scale Visual Recognition
Competition (ILSVRC), as a base model. A custom, binary classifier block was
added on top of this base. In order to account for the small dataset size, a
ten fold cross validation was implemented to evaluate the model's performance.

negative:
Dementia is a group of irreversible, chronic, and progressive
neurodegenerative disorders resulting in impaired memory, communication, and
thought processes. In recent years, clinical research advances in brain aging
have focused on the earliest clinically detectable stage of incipient dementia,
commonly known as mild cognitive impairment (MCI). Currently, these disorders
are diagnosed using a manual analysis of neuropsychological examinations. We
measure the feasibility of using the linguistic characteristics of verbal
utterances elicited during neuropsychological exams of elderly subjects to
distinguish between elderly control groups, people with MCI, people diagnosed
with possible Alzheimer's disease (AD), and probable AD. We investigated the
performance of both theory-driven psycholinguistic features and data-driven
contextual language embeddings in identifying different clinically diagnosed
groups. Our experiments show that a combination of contextual and
psycholinguistic features extracted by a Support Vector Machine improved
distinguishing the verbal utterances of elderly controls, people with MCI,
possible AD, and probable AD. This is the first work to identify four clinical
diagnosis groups of dementia in a highly imbalanced dataset. Our work shows
that machine learning algorithms built on contextual and psycholinguistic
features can learn the linguistic biomarkers from verbal utterances and assist
clinical diagnosis of different stages and types of dementia, even with limited
data.

negative:
Purpose: Lesion segmentation in medical imaging is key to evaluating
treatment response. We have recently shown that reinforcement learning can be
applied to radiological images for lesion localization. Furthermore, we
demonstrated that reinforcement learning addresses important limitations of
supervised deep learning; namely, it can eliminate the requirement for large
amounts of annotated training data and can provide valuable intuition lacking
in supervised approaches. However, we did not address the fundamental task of
lesion/structure-of-interest segmentation. Here we introduce a method combining
unsupervised deep learning clustering with reinforcement learning to segment
brain lesions on MRI.
  Materials and Methods: We initially clustered images using unsupervised deep
learning clustering to generate candidate lesion masks for each MRI image. The
user then selected the best mask for each of 10 training images. We then
trained a reinforcement learning algorithm to select the masks. We tested the
corresponding trained deep Q network on a separate testing set of 10 images.
For comparison, we also trained and tested a U-net supervised deep learning
network on the same set of training/testing images.
  Results: Whereas the supervised approach quickly overfit the training data
and predictably performed poorly on the testing set (16% average Dice score),
the unsupervised deep clustering and reinforcement learning achieved an average
Dice score of 83%.
  Conclusion: We have demonstrated a proof-of-principle application of
unsupervised deep clustering and reinforcement learning to segment brain
tumors. The approach represents human-allied AI that requires minimal input
from the radiologist without the need for hand-traced annotation.

negative:
Rapid advances of hardware-based technologies during the past decades have
opened up new possibilities for Life scientists to gather multimodal data in
various application domains (e.g., Omics, Bioimaging, Medical Imaging, and
[Brain/Body]-Machine Interfaces), thus generating novel opportunities for
development of dedicated data intensive machine learning techniques. Overall,
recent research in Deep learning (DL), Reinforcement learning (RL), and their
combination (Deep RL) promise to revolutionize Artificial Intelligence. The
growth in computational power accompanied by faster and increased data storage
and declining computing costs have already allowed scientists in various fields
to apply these techniques on datasets that were previously intractable for
their size and complexity. This review article provides a comprehensive survey
on the application of DL, RL, and Deep RL techniques in mining Biological data.
In addition, we compare performances of DL techniques when applied to different
datasets across various application domains. Finally, we outline open issues in
this challenging research area and discuss future development perspectives.

negative:
Artificial Intelligence allows the improvement of our daily life, for
instance, speech and handwritten text recognition, real time translation and
weather forecasting are common used applications. In the livestock sector,
machine learning algorithms have the potential for early detection and warning
of problems, which represents a significant milestone in the poultry industry.
Production problems generate economic loss that could be avoided by acting in a
timely manner. In the current study, training and testing of support vector
machines are addressed, for an early detection of problems in the production
curve of commercial eggs, using farm's egg production data of 478,919 laying
hens grouped in 24 flocks. Experiments using support vector machines with a 5
k-fold cross-validation were performed at different previous time intervals, to
alert with up to 5 days of forecasting interval, whether a flock will
experience a problem in production curve. Performance metrics such as accuracy,
specificity, sensitivity, and positive predictive value were evaluated,
reaching 0-day values of 0.9874, 0.9876, 0.9783 and 0.6518 respectively on
unseen data (test-set). The optimal forecasting interval was from zero to three
days, performance metrics decreases as the forecasting interval is increased.
It should be emphasized that this technique was able to issue an alert a day in
advance, achieving an accuracy of 0.9854, a specificity of 0.9865, a
sensitivity of 0.9333 and a positive predictive value of 0.6135. This novel
application embedded in a computer system of poultry management is able to
provide significant improvements in early detection and warning of problems
related to the production curve.

negative:
The emergence of continuous health monitoring and the availability of an
enormous amount of time series data has provided a great opportunity for the
advancement of personal health tracking. In recent years, unsupervised learning
methods have drawn special attention of researchers to tackle the sparse
annotation of health data and real-time detection of anomalies has been a
central problem of interest. However, one problem that has not been well
addressed before is the early prediction of forthcoming negative health events.
Early signs of an event can introduce subtle and gradual changes in the health
signal prior to its onset, detection of which can be invaluable in effective
prevention. In this study, we first demonstrate our observations on the
shortcoming of widely adopted anomaly detection methods in uncovering the
changes prior to a negative health event. We then propose a framework which
relies on online clustering of signal segment representations which are
automatically learned by a specially designed LSTM auto-encoder. We show the
effectiveness of our approach by predicting Bradycardia events in infants using
MIT-PICS dataset 1.3 minutes ahead of time with 68\% AUC score on average,
using no label supervision. Results of our study can indicate the viability of
our approach in the early detection of health events in other applications as
well.

negative:
Early detection of lung nodules is of great importance in lung cancer
screening. Existing research recognizes the critical role played by CAD systems
in early detection and diagnosis of lung nodules. However, many CAD systems,
which are used as cancer detection tools, produce a lot of false positives (FP)
and require a further FP reduction step. Furthermore, guidelines for early
diagnosis and treatment of lung cancer are consist of different shape and
volume measurements of abnormalities. Segmentation is at the heart of our
understanding of nodules morphology making it a major area of interest within
the field of computer aided diagnosis systems. This study set out to test the
hypothesis that joint learning of false positive (FP) nodule reduction and
nodule segmentation can improve the computer aided diagnosis (CAD) systems'
performance on both tasks. To support this hypothesis we propose a 3D deep
multi-task CNN to tackle these two problems jointly. We tested our system on
LUNA16 dataset and achieved an average dice similarity coefficient (DSC) of 91%
as segmentation accuracy and a score of nearly 92% for FP reduction. As a proof
of our hypothesis, we showed improvements of segmentation and FP reduction
tasks over two baselines. Our results support that joint training of these two
tasks through a multi-task learning approach improves system performance on
both. We also showed that a semi-supervised approach can be used to overcome
the limitation of lack of labeled data for the 3D segmentation task.

negative:
In this paper we present a Recurrent neural networks (RNN) based architecture
that achieves an AUCROC of 0.9147 for predicting the onset of Congestive Heart
Failure (CHF) 15 months in advance using a 12-month observation window on a
large cohort of 216,394 patients. We believe this to be the largest study in
CHF onset prediction with respect to the number of CHF case patients in the
cohort and the test set (3,332 CHF patients) on which the AUC metrics are
reported. We explore the extent to which LSTM (Long Short Term Memory) based
model, a variant of RNNs, can accurately predict the onset of CHF when compared
to known linear baselines like Logistic Regression, Random Forests and deep
learning based models such as Multi-Layer Perceptron and Convolutional Neural
Networks. We utilize demographics, medical diagnosis and procedure data from
21,405 CHF and 194,989 control patients to as our features. We describe our
feature embedding strategy for medical diagnosis codes that accommodates the
sparse, irregular, longitudinal, and high-dimensional characteristics of EHR
data. We empirically show that LSTMs can capture the longitudinal aspects of
EHR data better than the proposed baselines. As an attempt to interpret the
model, we present a temporal data analysis-based technique on false positives
to attribute feature importance. A model capable of predicting the onset of
congestive heart failure months in the future with this level of accuracy and
precision can support efforts of practitioners to implement risk factor
reduction strategies and researchers to begin to systematically evaluate
interventions to potentially delay or avert development of the disease with
high mortality, morbidity and significant costs.

negative:
Most modern neural networks for classification fail to take into account the
concept of the unknown. Trained neural networks are usually tested in an
unrealistic scenario with only examples from a closed set of known classes. In
an attempt to develop a more realistic model, the concept of working in an open
set environment has been introduced. This in turn leads to the concept of
incremental learning where a model with its own architecture and initial
trained set of data can identify unknown classes during the testing phase and
autonomously update itself if evidence of a new class is detected. Some
problems that arise in incremental learning are inefficient use of resources to
retrain the classifier repeatedly and the decrease of classification accuracy
as multiple classes are added over time. This process of instantiating new
classes is repeated as many times as necessary, accruing errors. To address
these problems, this paper proposes the Classification Confidence Threshold
approach to prime neural networks for incremental learning to keep accuracies
high by limiting forgetting. A lean method is also used to reduce resources
used in the retraining of the neural network. The proposed method is based on
the idea that a network is able to incrementally learn a new class even when
exposed to a limited number samples associated with the new class. This method
can be applied to most existing neural networks with minimal changes to network
architecture.

negative:
In recent years deep reinforcement learning (RL) systems have attained
superhuman performance in a number of challenging task domains. However, a
major limitation of such applications is their demand for massive amounts of
training data. A critical present objective is thus to develop deep RL methods
that can adapt rapidly to new tasks. In the present work we introduce a novel
approach to this challenge, which we refer to as deep meta-reinforcement
learning. Previous work has shown that recurrent networks can support
meta-learning in a fully supervised context. We extend this approach to the RL
setting. What emerges is a system that is trained using one RL algorithm, but
whose recurrent dynamics implement a second, quite separate RL procedure. This
second, learned RL algorithm can differ from the original one in arbitrary
ways. Importantly, because it is learned, it is configured to exploit structure
in the training domain. We unpack these points in a series of seven
proof-of-concept experiments, each of which examines a key aspect of deep
meta-RL. We consider prospects for extending and scaling up the approach, and
also point out some potentially important implications for neuroscience.

negative:
Alzheimer's disease (AD) is an irreversible devastative neurodegenerative
disorder associated with progressive impairment of memory and cognitive
functions. Its early diagnosis is crucial for the development of possible
future treatment option(s). Structural magnetic resonance images (sMRI) plays
an important role to help in understanding the anatomical changes related to AD
especially in its early stages. Conventional methods require the expertise of
domain experts and extract hand-picked features such as gray matter
substructures and train a classifier to distinguish AD subjects from healthy
subjects. Different from these methods, this paper proposes to construct
multiple deep 2D convolutional neural networks (2D-CNNs) to learn the various
features from local brain images which are combined to make the final
classification for AD diagnosis. The whole brain image was passed through two
transfer learning architectures; Inception version 3 and Xception; as well as
custom Convolutional Neural Network (CNN) built with the help of separable
convolutional layers which can automatically learn the generic features from
imaging data for classification. Our study is conducted using cross-sectional
T1-weighted structural MRI brain images from Open Access Series of Imaging
Studies (OASIS) database to maintain the size and contrast over different MRI
scans. Experimental results show that the transfer learning approaches exceed
the performance of non-transfer learning based approaches demonstrating the
effectiveness of these approaches for the binary AD classification task.

negative:
While recent progress in deep reinforcement learning has enabled robots to
learn complex behaviors, tasks with long horizons and sparse rewards remain an
ongoing challenge. In this work, we propose an effective reward shaping method
through predictive coding to tackle sparse reward problems. By learning
predictive representations offline and using these representations for reward
shaping, we gain access to reward signals that understand the structure and
dynamics of the environment. In particular, our method achieves better learning
by providing reward signals that 1) understand environment dynamics 2)
emphasize on features most useful for learning 3) resist noise in learned
representations through reward accumulation. We demonstrate the usefulness of
this approach in different domains ranging from robotic manipulation to
navigation, and we show that reward signals produced through predictive coding
are as effective for learning as hand-crafted rewards.

negative:
Over the past decade a wide spectrum of machine learning models have been
developed to model the neurodegenerative diseases, associating biomarkers,
especially non-intrusive neuroimaging markers, with key clinical scores
measuring the cognitive status of patients. Multi-task learning (MTL) has been
commonly utilized by these studies to address high dimensionality and small
cohort size challenges. However, most existing MTL approaches are based on
linear models and suffer from two major limitations: 1) they cannot explicitly
consider upper/lower bounds in these clinical scores; 2) they lack the
capability to capture complicated non-linear interactions among the variables.
In this paper, we propose Subspace Network, an efficient deep modeling approach
for non-linear multi-task censored regression. Each layer of the subspace
network performs a multi-task censored regression to improve upon the
predictions from the last layer via sketching a low-dimensional subspace to
perform knowledge transfer among learning tasks. Under mild assumptions, for
each layer the parametric subspace can be recovered using only one pass of
training data. Empirical results demonstrate that the proposed subspace network
quickly picks up the correct parameter subspaces, and outperforms
state-of-the-arts in predicting neurodegenerative clinical scores using
information in brain imaging.

negative:
To study neurodegenerative diseases, longitudinal studies are carried on
volunteer patients. During a time span of several months to several years, they
go through regular medical visits to acquire data from different modalities,
such as biological samples, cognitive tests, structural and functional imaging.
These variables are heterogeneous but they all depend on the patient's health
condition, meaning that there are possibly unknown relationships between all
modalities. Some information may be specific to some modalities, others may be
complementary, and others may be redundant. Some data may also be missing. In
this work we present a neural network architecture for multimodal learning,
able to use imaging and clinical data from two time points to predict the
evolution of a neurodegenerative disease, and robust to missing values. Our
multimodal network achieves 92.5\% accuracy and an AUC score of 0.978 over a
test set of 57 subjects. We also show the superiority of the multimodal
architecture, for up to 37.5\% of missing values in test set subjects' clinical
measurements, compared to a model using only the clinical modality.

negative:
We introduce Disease Knowledge Transfer (DKT), a novel technique for
transferring biomarker information between related neurodegenerative diseases.
DKT infers robust multimodal biomarker trajectories in rare neurodegenerative
diseases even when only limited, unimodal data is available, by transferring
information from larger multimodal datasets from common neurodegenerative
diseases. DKT is a joint-disease generative model of biomarker progressions,
which exploits biomarker relationships that are shared across diseases. Our
proposed method allows, for the first time, the estimation of plausible,
multimodal biomarker trajectories in Posterior Cortical Atrophy (PCA), a rare
neurodegenerative disease where only unimodal MRI data is available. For this
we train DKT on a combined dataset containing subjects with two distinct
diseases and sizes of data available: 1) a larger, multimodal typical AD (tAD)
dataset from the TADPOLE Challenge, and 2) a smaller unimodal Posterior
Cortical Atrophy (PCA) dataset from the Dementia Research Centre (DRC), for
which only a limited number of Magnetic Resonance Imaging (MRI) scans are
available. Although validation is challenging due to lack of data in PCA, we
validate DKT on synthetic data and two patient datasets (TADPOLE and PCA
cohorts), showing it can estimate the ground truth parameters in the simulation
and predict unseen biomarkers on the two patient datasets. While we
demonstrated DKT on Alzheimer's variants, we note DKT is generalisable to other
forms of related neurodegenerative diseases. Source code for DKT is available
online: https://github.com/mrazvan22/dkt.

negative:
Alzheimer's disease is the most common dementia leading to an irreversible
neurodegenerative process. To date, subject revealed advanced brain structural
alterations when the diagnosis is established. Therefore, an earlier diagnosis
of this dementia is crucial although it is a challenging task. Recently, many
studies have proposed biomarkers to perform early detection of Alzheimer's
disease. Some of them have proposed methods based on inter-subject similarity
while other approaches have investigated framework using intra-subject
variability. In this work, we propose a novel framework combining both
approaches within an efficient graph of brain structures grading. Subsequently,
we demonstrate the competitive performance of the proposed method compared to
state-of-the-art methods.

negative:
This doctoral thesis covers some of my advances in electron microscopy with
deep learning. Highlights include a comprehensive review of deep learning in
electron microscopy; large new electron microscopy datasets for machine
learning, dataset search engines based on variational autoencoders, and
automatic data clustering by t-distributed stochastic neighbour embedding;
adaptive learning rate clipping to stabilize learning; generative adversarial
networks for compressed sensing with spiral, uniformly spaced and other fixed
sparse scan paths; recurrent neural networks trained to piecewise adapt sparse
scan paths to specimens by reinforcement learning; improving signal-to-noise;
and conditional generative adversarial networks for exit wavefunction
reconstruction from single transmission electron micrographs. This thesis adds
to my publications by presenting their relationships, reflections, and holistic
conclusions. This version of my thesis is typeset for online dissemination to
improve readability, whereas the thesis submitted to the University of Warwick
in support of my application for the degree of Doctor of Philosophy in Physics
is typeset for physical printing and binding.

negative:
While current machine learning models have impressive performance over a wide
range of applications, their large size and complexity render them unsuitable
for tasks such as remote monitoring on edge devices with limited storage and
computational power. A naive approach to resolve this on the model level is to
use simpler architectures, but this sacrifices prediction accuracy and is
unsuitable for monitoring applications requiring accurate detection of the
onset of adverse events. In this paper, we propose an alternative solution to
this problem by decomposing the predictive model as the sum of a simple
function which serves as a local monitoring tool, and a complex correction term
to be evaluated on the server. A sign requirement is imposed on the latter to
ensure that the local monitoring function is safe, in the sense that it can
effectively serve as an early warning system. Our analysis quantifies the
trade-offs between model complexity and performance, and serves as a guidance
for architecture design. We validate our proposed framework on a series of
monitoring experiments, where we succeed at learning monitoring models with
significantly reduced complexity that minimally violate the safety requirement.
More broadly, our framework is useful for learning classifiers in applications
where false negatives are significantly more costly compared to false
positives.

negative:
To enable a deep learning-based system to be used in the medical domain as a
computer-aided diagnosis system, it is essential to not only classify diseases
but also present the locations of the diseases. However, collecting
instance-level annotations for various thoracic diseases is expensive.
Therefore, weakly supervised localization methods have been proposed that use
only image-level annotation. While the previous methods presented the disease
location as the most discriminative part for classification, this causes a deep
network to localize wrong areas for indistinguishable X-ray images. To solve
this issue, we propose a spatial attention method using disease masks that
describe the areas where diseases mainly occur. We then apply the spatial
attention to find the precise disease area by highlighting the highest
probability of disease occurrence. Meanwhile, the various sizes, rotations and
noise in chest X-ray images make generating the disease masks challenging. To
reduce the variation among images, we employ an alignment module to transform
an input X-ray image into a generalized image. Through extensive experiments on
the NIH-Chest X-ray dataset with eight kinds of diseases, we show that the
proposed method results in superior localization performances compared to
state-of-the-art methods.

negative:
Cancer is a complex disease, the understanding and treatment of which are
being aided through increases in the volume of collected data and in the scale
of deployed computing power. Consequently, there is a growing need for the
development of data-driven and, in particular, deep learning methods for
various tasks such as cancer diagnosis, detection, prognosis, and prediction.
Despite recent successes, however, designing high-performing deep learning
models for nonimage and nontext cancer data is a time-consuming,
trial-and-error, manual task that requires both cancer domain and deep learning
expertise. To that end, we develop a reinforcement-learning-based neural
architecture search to automate deep-learning-based predictive model
development for a class of representative cancer data. We develop custom
building blocks that allow domain experts to incorporate the
cancer-data-specific characteristics. We show that our approach discovers deep
neural network architectures that have significantly fewer trainable
parameters, shorter training time, and accuracy similar to or higher than those
of manually designed architectures. We study and demonstrate the scalability of
our approach on up to 1,024 Intel Knights Landing nodes of the Theta
supercomputer at the Argonne Leadership Computing Facility.

negative:
To acquire a new skill, humans learn better and faster if a tutor, based on
their current knowledge level, informs them of how much attention they should
pay to particular content or practice problems. Similarly, a machine learning
model could potentially be trained better with a scorer that "adapts" to its
current learning state and estimates the importance of each training data
instance. Training such an adaptive scorer efficiently is a challenging
problem; in order to precisely quantify the effect of a data instance at a
given time during the training, it is typically necessary to first complete the
entire training process. To efficiently optimize data usage, we propose a
reinforcement learning approach called Differentiable Data Selection (DDS). In
DDS, we formulate a scorer network as a learnable function of the training
data, which can be efficiently updated along with the main model being trained.
Specifically, DDS updates the scorer with an intuitive reward signal: it should
up-weigh the data that has a similar gradient with a dev set upon which we
would finally like to perform well. Without significant computing overhead, DDS
delivers strong and consistent improvements over several strong baselines on
two very different tasks of machine translation and image classification.

negative:
We consider the problem of anomaly detection with a small set of partially
labeled anomaly examples and a large-scale unlabeled dataset. This is a common
scenario in many important applications. Existing related methods either
exclusively fit the limited anomaly examples that typically do not span the
entire set of anomalies, or proceed with unsupervised learning from the
unlabeled data. We propose here instead a deep reinforcement learning-based
approach that enables an end-to-end optimization of the detection of both
labeled and unlabeled anomalies. This approach learns the known abnormality by
automatically interacting with an anomaly-biased simulation environment, while
continuously extending the learned abnormality to novel classes of anomaly
(i.e., unknown anomalies) by actively exploring possible anomalies in the
unlabeled data. This is achieved by jointly optimizing the exploitation of the
small labeled anomaly data and the exploration of the rare unlabeled anomalies.
Extensive experiments on 48 real-world datasets show that our model
significantly outperforms five state-of-the-art competing methods.

negative:
Detection of Alzheimer's Disease (AD) from neuroimaging data such as MRI
through machine learning have been a subject of intense research in recent
years. Recent success of deep learning in computer vision have progressed such
research further. However, common limitations with such algorithms are reliance
on a large number of training images, and requirement of careful optimization
of the architecture of deep networks. In this paper, we attempt solving these
issues with transfer learning, where state-of-the-art architectures such as VGG
and Inception are initialized with pre-trained weights from large benchmark
datasets consisting of natural images, and the fully-connected layer is
re-trained with only a small number of MRI images. We employ image entropy to
select the most informative slices for training. Through experimentation on the
OASIS MRI dataset, we show that with training size almost 10 times smaller than
the state-of-the-art, we reach comparable or even better performance than
current deep-learning based methods.

negative:
The application of Deep Learning (DL) for medical diagnosis is often hampered
by two problems. First, the amount of training data may be scarce, as it is
limited by the number of patients who have acquired the condition to be
diagnosed. Second, the training data may be corrupted by various types of
noise. Here, we study the problem of brain tumor detection from magnetic
resonance spectroscopy (MRS) data, where both types of problems are prominent.
To overcome these challenges, we propose a new method for training a deep
neural network that distills particularly representative training examples and
augments the training data by mixing these samples from one class with those
from the same and other classes to create additional training samples. We
demonstrate that this technique substantially improves performance, allowing
our method to reach human-expert-level accuracy with just a few thousand
training examples. Interestingly, the network learns to rely on features of the
data that are usually ignored by human experts, suggesting new directions for
future research.

negative:
Reinforcement learning methods have recently been very successful at
performing complex sequential tasks like playing Atari games, Go and Poker.
These algorithms have outperformed humans in several tasks by learning from
scratch, using only scalar rewards obtained through interaction with their
environment. While there certainly has been considerable independent innovation
to produce such results, many core ideas in reinforcement learning are inspired
by phenomena in animal learning, psychology and neuroscience. In this paper, we
comprehensively review a large number of findings in both neuroscience and
psychology that evidence reinforcement learning as a promising candidate for
modeling learning and decision making in the brain. In doing so, we construct a
mapping between various classes of modern RL algorithms and specific findings
in both neurophysiological and behavioral literature. We then discuss the
implications of this observed relationship between RL, neuroscience and
psychology and its role in advancing research in both AI and brain science.

negative:
Neurodegenerative diseases are frequently associated with structural changes
in the brain. Magnetic Resonance Imaging (MRI) scans can show these variations
and therefore be used as a supportive feature for a number of neurodegenerative
diseases. The hippocampus has been known to be a biomarker for Alzheimer
disease and other neurological and psychiatric diseases. However, it requires
accurate, robust and reproducible delineation of hippocampal structures. Fully
automatic methods are usually the voxel based approach, for each voxel a number
of local features were calculated. In this paper we compared four different
techniques for feature selection from a set of 315 features extracted for each
voxel: (i) filter method based on the Kolmogorov-Smirnov test; two wrapper
methods, respectively, (ii) Sequential Forward Selection and (iii) Sequential
Backward Elimination; and (iv) embedded method based on the Random Forest
Classifier on a set of 10 T1-weighted brain MRIs and tested on an independent
set of 25 subjects. The resulting segmentations were compared with manual
reference labelling. By using only 23 features for each voxel (sequential
backward elimination) we obtained comparable state of-the-art performances with
respect to the standard tool FreeSurfer.

negative:
Reinforcement learning has recently shown promise as a technique for training
an artificial neural network to parse sentences in some unknown format, through
a body of work known as RL-GRIT. A key aspect of the RL-GRIT approach is that
rather than explicitly inferring a grammar that describes the format, the
neural network learns to perform various parsing actions (such as merging two
tokens) over a corpus of sentences, with the goal of maximizing the estimated
frequency of the resulting parse structures. This can allow the learning
process to more easily explore different action choices, since a given choice
may change the optimality of the parse (as expressed by the total reward), but
will not result in the failure to parse a sentence. However, this also presents
a limitation: because the trained neural network can successfully parse any
sentence, it cannot be directly used to identify sentences that deviate from
the format of the training sentences, i.e., that are anomalous. In this paper,
we address this limitation by presenting procedures for extracting production
rules from the neural network, and for using these rules to determine whether a
given sentence is nominal or anomalous. When a sentence is anomalous, an
attempt is made to identify the location of the anomaly. We empirically
demonstrate that our approach is capable of grammatical inference and anomaly
detection for both non-regular formats and those containing regions of high
randomness/entropy. While a format with high randomness typically requires
large sets of production rules, we propose a two pass grammatical inference
method to generate parsimonious rule sets for such formats. By further
improving parser learning, and leveraging the presented rule extraction and
anomaly detection algorithms, one might begin to understand common errors,
either benign or malicious, in practical formats.

negative:
We propose a new algorithm for hyperparameter selection in machine learning
algorithms. The algorithm is a novel modification of Harmonica, a spectral
hyperparameter selection approach using sparse recovery methods. In particular,
we show that a special encoding of hyperparameter space enables a natural
group-sparse recovery formulation, which when coupled with HyperBand (a
multi-armed bandit strategy) leads to improvement over existing hyperparameter
optimization methods such as Successive Halving and Random Search. Experimental
results on image datasets such as CIFAR-10 confirm the benefits of our
approach.

negative:
With a mortality rate of 5.4 million lives worldwide every year and a
healthcare cost of more than 16 billion dollars in the USA alone, sepsis is one
of the leading causes of hospital mortality and an increasing concern in the
ageing western world. Recently, medical and technological advances have helped
re-define the illness criteria of this disease, which is otherwise poorly
understood by the medical society. Together with the rise of widely accessible
Electronic Health Records, the advances in data mining and complex nonlinear
algorithms are a promising avenue for the early detection of sepsis. This work
contributes to the research effort in the field of automated sepsis detection
with an open-access labelling of the medical MIMIC-III data set. Moreover, we
propose MGP-AttTCN: a joint multitask Gaussian Process and attention-based deep
learning model to early predict the occurrence of sepsis in an interpretable
manner. We show that our model outperforms the current state-of-the-art and
present evidence that different labelling heuristics lead to discrepancies in
task difficulty. For instance, when predicting sepsis five hours prior to onset
on our new realistic labels, our proposed model achieves an area under the ROC
curve of 0.660 and an area under the PR curve of 0.483, whereas the (less
interpretable) previous state-of-the-art model (MGP-TCN) achieves 0.635 AUROC
and 0.460 AUPR and the popular commercial InSight model achieves 0.490 AUROC
and 0.359 AUPR.

negative:
The timeliness of detection of a sepsis event in progress is a crucial factor
in the outcome for the patient. Machine learning models built from data in
electronic health records can be used as an effective tool for improving this
timeliness, but so far the potential for clinical implementations has been
largely limited to studies in intensive care units. This study will employ a
richer data set that will expand the applicability of these models beyond
intensive care units. Furthermore, we will circumvent several important
limitations that have been found in the literature: 1) Models are evaluated
shortly before sepsis onset without considering interventions already
initiated. 2) Machine learning models are built on a restricted set of clinical
parameters, which are not necessarily measured in all departments. 3) Model
performance is limited by current knowledge of sepsis, as feature interactions
and time dependencies are hardcoded into the model. In this study, we present a
model to overcome these shortcomings using a deep learning approach on a
diverse multicenter data set. We used retrospective data from multiple Danish
hospitals over a seven-year period. Our sepsis detection system is constructed
as a combination of a convolutional neural network and a long short-term memory
network. We suggest a retrospective assessment of interventions by looking at
intravenous antibiotics and blood cultures preceding the prediction time.
Results show performance ranging from AUROC 0.856 (3 hours before sepsis onset)
to AUROC 0.756 (24 hours before sepsis onset). We present a deep learning
system for early detection of sepsis that is able to learn characteristics of
the key factors and interactions from the raw event sequence data itself,
without relying on a labor-intensive feature extraction work.

negative:
Despite significant advances in modeling of friction-induced vibrations and
brake squeal, the majority of industrial research and design is still conducted
experimentally, since many aspects of squeal and its mechanisms involved remain
unknown. We report here for the first time on novel strategies for handling
data-intensive vibration testings to gain better insights into friction brake
system vibrations and noise generation mechanisms. Machine learning-based
methods to detect and characterize vibrations, to understand sensitivities and
to predict brake squeal are applied with the aim to illustrate how
interdisciplinary approaches can leverage the potential of data science
techniques for classical mechanical engineering challenges. In the first part,
a deep learning brake squeal detector is developed to identify several classes
of typical friction noise recordings. The detection method is rooted in recent
computer vision techniques for object detection based on convolutional neural
networks. It allows to overcome limitations of classical approaches that solely
rely on instantaneous spectral properties of the recorded noise. Results
indicate superior detection and characterization quality when compared to a
state-of-the-art brake squeal detector. In the second part, a recurrent neural
network is employed to learn the parametric patterns that determine the dynamic
stability of an operating brake system. Given a set of multivariate loading
conditions, the RNN learns to predict the noise generation of the structure.
The validated RNN represents a virtual twin model for the squeal behavior of a
specific brake system. It is found that this model can predict the occurrence
and the onset of brake squeal with high accuracy and that it can identify the
complicated patterns and temporal dependencies in the loading conditions that
drive the dynamical structure into regimes of instability.

negative:
We present a framework capable of tackilng the problem of continual object
recognition in a setting which resembles that under whichhumans see and learn.
This setting has a set of unique characteristics:it assumes an egocentric
point-of-view bound to the needs of a singleperson, which implies a relatively
low diversity of data and a coldstart with no data; it requires to operate in
an open world, where newobjects can be encounteredat any time; supervision is
scarce and hasto be solicited to the user, and completelyunsupervised
recognitionof new objects should be possible. Note that this setting differs
fromthe one addressed in the open world recognition literature, where
supervised feedback is always requested to be able to incorporate newobjects.
We propose a first solution to this problem in the form ofa memory-based
incremental framework that is capable of storinginformation of each and any
object it encounters, while using the supervision of the user to learn to
discriminate between known and unknown objects. Our approach is based on four
main features: the useof time and space persistence (i.e., the appearance of
objects changesrelatively slowly), the use of similarity as the main driving
principlefor object recognition and novelty detection, the progressive
introduction of new objects in a developmental fashion and the
selectiveelicitation of user feedback in an online active learning fashion.
Experimental results show the feasibility of open world, generic
objectrecognition, the ability to recognize, memorize and re-identify
newobjects even in complete absence of user supervision, and the utilityof
persistence and incrementality in boosting performance.

negative:
Few-Shot Learning is the challenge of training a model with only a small
amount of data. Many solutions to this problem use meta-learning algorithms,
i.e. algorithms that learn to learn. By sampling few-shot tasks from a larger
dataset, we can teach these algorithms to solve new, unseen tasks. This
document reports my work on meta-learning algorithms for Few-Shot Computer
Vision. This work was done during my internship at Sicara, a French company
building image recognition solutions for businesses. It contains: 1. an
extensive review of the state-of-the-art in few-shot computer vision; 2. a
benchmark of meta-learning algorithms for few-shot image classification; 3. the
introduction to a novel meta-learning algorithm for few-shot object detection,
which is still in development.

negative:
We present Earliness-Aware Deep Convolutional Networks (EA-ConvNets), an
end-to-end deep learning framework, for early classification of time series
data. Unlike most existing methods for early classification of time series
data, that are designed to solve this problem under the assumption of the
availability of a good set of pre-defined (often hand-crafted) features, our
framework can jointly perform feature learning (by learning a deep hierarchy of
\emph{shapelets} capturing the salient characteristics in each time series),
along with a dynamic truncation model to help our deep feature learning
architecture focus on the early parts of each time series. Consequently, our
framework is able to make highly reliable early predictions, outperforming
various state-of-the-art methods for early time series classification, while
also being competitive when compared to the state-of-the-art time series
classification algorithms that work with \emph{fully observed} time series
data. To the best of our knowledge, the proposed framework is the first to
perform data-driven (deep) feature learning in the context of early
classification of time series data. We perform a comprehensive set of
experiments, on several benchmark data sets, which demonstrate that our method
yields significantly better predictions than various state-of-the-art methods
designed for early time series classification. In addition to obtaining high
accuracies, our experiments also show that the learned deep shapelets based
features are also highly interpretable and can help gain better understanding
of the underlying characteristics of time series data.

negative:
Many practical applications of reinforcement learning require agents to learn
from sparse and delayed rewards. It challenges the ability of agents to
attribute their actions to future outcomes. In this paper, we consider the
problem formulation of episodic reinforcement learning with trajectory
feedback. It refers to an extreme delay of reward signals, in which the agent
can only obtain one reward signal at the end of each trajectory. A popular
paradigm for this problem setting is learning with a designed auxiliary dense
reward function, namely proxy reward, instead of sparse environmental signals.
Based on this framework, this paper proposes a novel reward redistribution
algorithm, randomized return decomposition (RRD), to learn a proxy reward
function for episodic reinforcement learning. We establish a surrogate problem
by Monte-Carlo sampling that scales up least-squares-based reward
redistribution to long-horizon problems. We analyze our surrogate loss function
by connection with existing methods in the literature, which illustrates the
algorithmic properties of our approach. In experiments, we extensively evaluate
our proposed method on a variety of benchmark tasks with episodic rewards and
demonstrate substantial improvement over baseline algorithms.

negative:
Fake tweets are observed to be ever-increasing, demanding immediate
countermeasures to combat their spread. During COVID-19, tweets with
misinformation should be flagged and neutralized in their early stages to
mitigate the damages. Most of the existing methods for early detection of fake
news assume to have enough propagation information for large labeled tweets --
which may not be an ideal setting for cases like COVID-19 where both aspects
are largely absent. In this work, we present ENDEMIC, a novel early detection
model which leverages exogenous and endogenous signals related to tweets, while
learning on limited labeled data. We first develop a novel dataset, called CTF
for early COVID-19 Twitter fake news, with additional behavioral test sets to
validate early detection. We build a heterogeneous graph with
follower-followee, user-tweet, and tweet-retweet connections and train a graph
embedding model to aggregate propagation information. Graph embeddings and
contextual features constitute endogenous, while time-relative web-scraped
information constitutes exogenous signals. ENDEMIC is trained in a
semi-supervised fashion, overcoming the challenge of limited labeled data. We
propose a co-attention mechanism to fuse signal representations optimally.
Experimental results on ECTF, PolitiFact, and GossipCop show that ENDEMIC is
highly reliable in detecting early fake tweets, outperforming nine
state-of-the-art methods significantly.

negative:
Early diagnosis of treatable diseases is essential for improving healthcare,
and many diseases' onsets are predictable from annual lab tests and their
temporal trends. We introduce a multi-resolution convolutional neural network
for early detection of multiple diseases from irregularly measured sparse lab
values. Our novel architecture takes as input both an imputed version of the
data and a binary observation matrix. For imputing the temporal sparse
observations, we develop a flexible, fast to train method for differentiable
multivariate kernel regression. Our experiments on data from 298K individuals
over 8 years, 18 common lab measurements, and 171 diseases show that the
temporal signatures learned via convolution are significantly more predictive
than baselines commonly used for early disease diagnosis.

negative:
In statistical modelling the biggest threat is concept drift which makes the
model gradually showing deteriorating performance over time. There are state of
the art methodologies to detect the impact of concept drift, however general
strategy considered to overcome the issue in performance is to rebuild or
re-calibrate the model periodically as the variable patterns for the model
changes significantly due to market change or consumer behavior change etc.
Quantitative research is the most widely spread application of data science in
Marketing or financial domain where applicability of state of the art
reinforcement learning for auto-learning is less explored paradigm.
Reinforcement learning is heavily dependent on having a simulated environment
which is majorly available for gaming or online systems, to learn from the live
feedback. However, there are some research happened on the area of online
advertisement, pricing etc where due to the nature of the online learning
environment scope of reinforcement learning is explored. Our proposed solution
is a reinforcement learning based, true self-learning algorithm which can adapt
to the data change or concept drift and auto learn and self-calibrate for the
new patterns of the data solving the problem of concept drift.
  Keywords - Reinforcement learning, Genetic Algorithm, Q-learning,
Classification modelling, CMA-ES, NES, Multi objective optimization, Concept
drift, Population stability index, Incremental learning, F1-measure, Predictive
Modelling, Self-learning, MCTS, AlphaGo, AlphaZero

negative:
The recent explosion of machine learning (ML) and artificial intelligence
(AI) shows great potential in the breakthrough of metal additive manufacturing
(AM) process modeling. However, the success of conventional machine learning
tools in data science is primarily attributed to the unprecedented large amount
of labeled data-sets (big data), which can be either obtained by experiments or
first-principle simulations. Unfortunately, these labeled data-sets are
expensive to obtain in AM due to the high expense of the AM experiments and
prohibitive computational cost of high-fidelity simulations.
  We propose a physics-informed neural network (PINN) framework that fuses both
data and first physical principles, including conservation laws of momentum,
mass, and energy, into the neural network to inform the learning processes. To
the best knowledge of the authors, this is the first application of PINN to
three dimensional AM processes modeling. Besides, we propose a hard-type
approach for Dirichlet boundary conditions (BCs) based on a Heaviside function,
which can not only enforce the BCs but also accelerate the learning process.
The PINN framework is applied to two representative metal manufacturing
problems, including the 2018 NIST AM-Benchmark test series. We carefully assess
the performance of the PINN model by comparing the predictions with available
experimental data and high-fidelity simulation results. The investigations show
that the PINN, owed to the additional physical knowledge, can accurately
predict the temperature and melt pool dynamics during metal AM processes with
only a moderate amount of labeled data-sets. The foray of PINN to metal AM
shows the great potential of physics-informed deep learning for broader
applications to advanced manufacturing.

negative:
Early outbreak detection is a key aspect in the containment of infectious
diseases, as it enables the identification and isolation of infected
individuals before the disease can spread to a larger population. Instead of
detecting unexpected increases of infections by monitoring confirmed cases,
syndromic surveillance aims at the detection of cases with early symptoms,
which allows a more timely disclosure of outbreaks. However, the definition of
these disease patterns is often challenging, as early symptoms are usually
shared among many diseases and a particular disease can have several clinical
pictures in the early phase of an infection. To support epidemiologists in the
process of defining reliable disease patterns, we present a novel, data-driven
approach to discover such patterns in historic data. The key idea is to take
into account the correlation between indicators in a health-related data source
and the reported number of infections in the respective geographic region. In
an experimental evaluation, we use data from several emergency departments to
discover disease patterns for three infectious diseases. Our results suggest
that the proposed approach is able to find patterns that correlate with the
reported infections and often identifies indicators that are related to the
respective diseases.

negative:
Diseases and pests cause huge economic loss to the apple industry every year.
The identification of various apple diseases is challenging for the farmers as
the symptoms produced by different diseases may be very similar, and may be
present simultaneously. This paper is an attempt to provide the timely and
accurate detection and identification of apple diseases. In this study, we
propose a deep learning based approach for identification and classification of
apple diseases. The first part of the study is dataset creation which includes
data collection and data labelling. Next, we train a Convolutional Neural
Network (CNN) model on the prepared dataset for automatic classification of
apple diseases. CNNs are end-to-end learning algorithms which perform automatic
feature extraction and learn complex features directly from raw images, making
them suitable for wide variety of tasks like image classification, object
detection, segmentation etc. We applied transfer learning to initialize the
parameters of the proposed deep model. Data augmentation techniques like
rotation, translation, reflection and scaling were also applied to prevent
overfitting. The proposed CNN model obtained encouraging results, reaching
around 97.18% of accuracy on our prepared dataset. The results validate that
the proposed method is effective in classifying various types of apple diseases
and can be used as a practical tool by farmers.

negative:
In this paper we propose a novel framework for decentralized, online learning
by many learners. At each moment of time, an instance characterized by a
certain context may arrive to each learner; based on the context, the learner
can select one of its own actions (which gives a reward and provides
information) or request assistance from another learner. In the latter case,
the requester pays a cost and receives the reward but the provider learns the
information. In our framework, learners are modeled as cooperative contextual
bandits. Each learner seeks to maximize the expected reward from its arrivals,
which involves trading off the reward received from its own actions, the
information learned from its own actions, the reward received from the actions
requested of others and the cost paid for these actions - taking into account
what it has learned about the value of assistance from each other learner. We
develop distributed online learning algorithms and provide analytic bounds to
compare the efficiency of these with algorithms with the complete knowledge
(oracle) benchmark (in which the expected reward of every action in every
context is known by every learner). Our estimates show that regret - the loss
incurred by the algorithm - is sublinear in time. Our theoretical framework can
be used in many practical applications including Big Data mining, event
detection in surveillance sensor networks and distributed online recommendation
systems.

negative:
What goals should a multi-goal reinforcement learning agent pursue during
training in long-horizon tasks? When the desired (test time) goal distribution
is too distant to offer a useful learning signal, we argue that the agent
should not pursue unobtainable goals. Instead, it should set its own intrinsic
goals that maximize the entropy of the historical achieved goal distribution.
We propose to optimize this objective by having the agent pursue past achieved
goals in sparsely explored areas of the goal space, which focuses exploration
on the frontier of the achievable goal set. We show that our strategy achieves
an order of magnitude better sample efficiency than the prior state of the art
on long-horizon multi-goal tasks including maze navigation and block stacking.

negative:
A key challenge in scaling up robot learning to many skills and environments
is removing the need for human supervision, so that robots can collect their
own data and improve their own performance without being limited by the cost of
requesting human feedback. Model-based reinforcement learning holds the promise
of enabling an agent to learn to predict the effects of its actions, which
could provide flexible predictive models for a wide range of tasks and
environments, without detailed human supervision. We develop a method for
combining deep action-conditioned video prediction models with model-predictive
control that uses entirely unlabeled training data. Our approach does not
require a calibrated camera, an instrumented training set-up, nor precise
sensing and actuation. Our results show that our method enables a real robot to
perform nonprehensile manipulation -- pushing objects -- and can handle novel
objects not seen during training.

negative:
Parkinson's disease (PD) is debilitating, progressive, and clinically marked
by motor symptoms. As the second most common neurodegenerative disease in the
world, it affects over 10 million lives globally. Existing diagnoses methods
have limitations, such as the expense of visiting doctors and the challenge of
automated early detection, considering that behavioral differences in patients
and healthy individuals are often indistinguishable in the early stages.
However, micrographia, a handwriting disorder that leads to abnormally small
handwriting, tremors, dystonia, and slow movement in the hands and fingers, is
commonly observed in the early stages of PD. In this work, we apply machine
learning techniques to extract signs of micrographia from drawing samples
gathered from two open-source datasets and achieve a predictive accuracy of
94%. This work also sets the foundations for a publicly available and
user-friendly web portal that anyone with access to a pen, printer, and phone
can use for early PD detection.

negative:
Healthcare is one of the most important aspects of human life. Heart disease
is known to be one of the deadliest diseases which is hampering the lives of
many people around the world. Heart disease must be detected early so the loss
of lives can be prevented. The availability of large-scale data for medical
diagnosis has helped developed complex machine learning and deep learning-based
models for automated early diagnosis of heart diseases. The classical
approaches have been limited in terms of not generalizing well to new data
which have not been seen in the training set. This is indicated by a large gap
in training and test accuracies. This paper proposes a novel deep learning
architecture using a 1D convolutional neural network for classification between
healthy and non-healthy persons to overcome the limitations of classical
approaches. Various clinical parameters are used for assessing the risk profile
in the patients which helps in early diagnosis. Various techniques are used to
avoid overfitting in the proposed network. The proposed network achieves over
97% training accuracy and 96% test accuracy on the dataset. The accuracy of the
model is compared in detail with other classification algorithms using various
performance parameters which proves the effectiveness of the proposed
architecture.

negative:
The proliferation of web platforms has created incentives for online abuse.
Many graph-based anomaly detection techniques are proposed to identify the
suspicious accounts and behaviors. However, most of them detect the anomalies
once the users have performed many such behaviors. Their performance is
substantially hindered when the users' observed data is limited at an early
stage, which needs to be improved to minimize financial loss. In this work, we
propose Eland, a novel framework that uses action sequence augmentation for
early anomaly detection. Eland utilizes a sequence predictor to predict next
actions of every user and exploits the mutual enhancement between action
sequence augmentation and user-action graph anomaly detection. Experiments on
three real-world datasets show that Eland improves the performance of a variety
of graph-based anomaly detection methods. With Eland, anomaly detection
performance at an earlier stage is better than non-augmented methods that need
significantly more observed data by up to 15% on the Area under the ROC curve.

negative:
Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative
disease of the brain and the spinal cord, which leads to paralysis of motor
functions. Patients retain their ability to blink, which can be used for
communication. Here, We present an Artificial Intelligence (AI) system that
uses eye-blinks to communicate with the outside world, running on real-time
Internet-of-Things (IoT) devices. The system uses a Convolutional Neural
Network (CNN) to find the blinking pattern, which is defined as a series of
Open and Closed states. Each pattern is mapped to a collection of words that
manifest the patient's intent. To investigate the best trade-off between
accuracy and latency, we investigated several Convolutional Network
architectures, such as ResNet, SqueezeNet, DenseNet, and InceptionV3, and
evaluated their performance. We found that the InceptionV3 architecture, after
hyper-parameter fine-tuning on the specific task led to the best performance
with an accuracy of 99.20% and 94ms latency. This work demonstrates how the
latest advances in deep learning architectures can be adapted for clinical
systems that ameliorate the patient's quality of life regardless of the
point-of-care.

negative:
In order to detect unknown intrusions and runtime errors of computer
programs, the cyber-security community has developed various detection
techniques. Anomaly detection is an approach that is designed to profile the
normal runtime behavior of computer programs in order to detect intrusions and
errors as anomalous deviations from the observed normal. However, normal but
unobserved behavior can trigger false positives. This limitation has
significantly decreased the practical viability of anomaly detection
techniques. Reported approaches to this limitation span a simple alert
threshold definition to distribution models for approximating all normal
behavior based on the limited observation. However, each assumption or
approximation poses the potential for even greater false positive rates. This
paper presents our study on how to explain the presence of anomalies using a
neural network, particularly Long Short-Term Memory, independent of actual data
distributions. We present and compare three anomaly detection models, and
report on our experience running different types of attacks on an Apache
Hypertext Transfer Protocol server. We performed a comparative study, focusing
on each model's ability to detect the onset of each attack while avoiding false
positives resulting from unknown normal behavior. Our best-performing model
detected the true onset of every attack with zero false positives.

negative:
In medical science, the use of computer science in disease detection and
diagnosis is gaining popularity. Previously, the detection of disease used to
take a significant amount of time and was less reliable. Machine learning (ML)
techniques employed in recent biomedical researches are making revolutionary
changes by gaining higher accuracy with more concise timing. At present, it is
even possible to automatically detect diseases from the scanned images with the
help of ML. In this research, we have taken such an attempt to detect retinal
diseases from optical coherence tomography (OCT) X-ray images. Here, we propose
a deep learning (DL) based approach in detecting retinal diseases from OCT
images which can identify three conditions of the retina. Four different models
used in this approach are compared with each other. On the test set, the
detection accuracy is 98.00\% for a vanilla convolutional neural network (CNN)
model, 99.07\% for Xception model, 97.00\% for ResNet50 model, and 99.17\% for
MobileNetV2 model. The MobileNetV2 model acquires the highest accuracy, and the
closest to the highest is the Xception model. The proposed approach has a
potential impact on creating a tool for automatically detecting retinal
diseases.

negative:
Fever can provide valuable information for diagnosis and prognosis of various
diseases such as pneumonia, dengue, sepsis, etc., therefore, predicting fever
early can help in the effectiveness of treatment options and expediting the
treatment process. This study aims to develop novel algorithms that can
accurately predict fever onset in critically ill patients by applying machine
learning technique on continuous physiological data. We analyzed continuous
physiological data collected every 5-minute from a cohort of over 200,000
critically ill patients admitted to an Intensive Care Unit (ICU) over a 2-year
period. Each episode of fever from the same patient were considered as an
independent event, with separations of at least 24 hours. We extracted
descriptive statistical features from six physiological data streams, including
heart rate, respiration, systolic and diastolic blood pressure, mean arterial
pressure, and oxygen saturation, and use these features to independently
predict the onset of fever. Using a bootstrap aggregation method, we created a
balanced dataset of 7,801 afebrile and febrile patients and analyzed features
up to 4 hours before the fever onset. We found that supervised machine learning
methods can predict fever up to 4 hours before onset in critically ill patients
with high recall, precision, and F1-score. This study demonstrates the
viability of using machine learning to predict fever among hospitalized adults.
The discovery of salient physiomarkers through machine learning and deep
learning techniques has the potential to further accelerate the development and
implementation of innovative care delivery protocols and strategies for
medically vulnerable patients.

negative:
The recent history of machine learning research has taught us that machine
learning methods can be most effective when they are provided with very large,
high-capacity models, and trained on very large and diverse datasets. This has
spurred the community to search for ways to remove any bottlenecks to scale.
Often the foremost among such bottlenecks is the need for human effort,
including the effort of curating and labeling datasets. As a result,
considerable attention in recent years has been devoted to utilizing unlabeled
data, which can be collected in vast quantities. However, some of the most
widely used methods for training on such unlabeled data themselves require
human-designed objective functions that must correlate in some meaningful way
to downstream tasks. I will argue that a general, principled, and powerful
framework for utilizing unlabeled data can be derived from reinforcement
learning, using general purpose unsupervised or self-supervised reinforcement
learning objectives in concert with offline reinforcement learning methods that
can leverage large datasets. I will discuss how such a procedure is more
closely aligned with potential downstream tasks, and how it could build on
existing techniques that have been developed in recent years.

negative:
Disparate areas of machine learning have benefited from models that can take
raw data with little preprocessing as input and learn rich representations of
that raw data in order to perform well on a given prediction task. We evaluate
this approach in healthcare by using longitudinal measurements of lab tests,
one of the more raw signals of a patient's health state widely available in
clinical data, to predict disease onsets. In particular, we train a Long
Short-Term Memory (LSTM) recurrent neural network and two novel convolutional
neural networks for multi-task prediction of disease onset for 133 conditions
based on 18 common lab tests measured over time in a cohort of 298K patients
derived from 8 years of administrative claims data. We compare the neural
networks to a logistic regression with several hand-engineered, clinically
relevant features. We find that the representation-based learning approaches
significantly outperform this baseline. We believe that our work suggests a new
avenue for patient risk stratification based solely on lab results.

negative:
Machine Learning requires large amounts of labeled data to fit a model. Many
datasets are already publicly available, nevertheless forcing application
possibilities of machine learning to the domains of those public datasets. The
ever-growing penetration of machine learning algorithms in new application
areas requires solutions for the need for data in those new domains. This
thesis works on active learning as one possible solution to reduce the amount
of data that needs to be processed by hand, by processing only those datapoints
that specifically benefit the training of a strong model for the task. A newly
proposed framework for framing the active learning workflow as a reinforcement
learning problem is adapted for image classification and a series of three
experiments is conducted. Each experiment is evaluated and potential issues
with the approach are outlined. Each following experiment then proposes
improvements to the framework and evaluates their impact. After the last
experiment, a final conclusion is drawn, unfortunately rejecting this work's
hypothesis and outlining that the proposed framework at the moment is not
capable of improving active learning for image classification with a trained
reinforcement learning agent.

negative:
Chest X-ray scan is a most often used modality by radiologists to diagnose
many chest related diseases in their initial stages. The proposed system aids
the radiologists in making decision about the diseases found in the scans more
efficiently. Our system combines the techniques of image processing for feature
enhancement and deep learning for classification among diseases. We have used
the ChestX-ray14 database in order to train our deep learning model on the 14
different labeled diseases found in it. The proposed research shows the
significant improvement in the results by using wavelet transforms as
pre-processing technique.

negative:
Several neurodegenerative diseases involve the accumulation of cellular DNA
damage. Comet assays are a popular way of estimating the extent of DNA damage.
Current literature on the use of deep learning to quantify DNA damage presents
an empirical approach to hyper-parameter optimization and does not include
uncertainty estimates. Deep ensemble averaging is a standard approach to
estimating uncertainty but it requires several iterations of network training,
which makes it time-consuming. Here we present an approach to quantify the
extent of DNA damage that combines deep learning with a rigorous and
comprehensive method to optimize the hyper-parameters with the help of
statistical tests. We also use an architecture that allows for a faster
computation of deep ensemble averaging and performs statistical tests
applicable to networks using transfer learning. We applied our approach to a
comet assay dataset with more than 1300 images and achieved an $R^2$ of 0.84,
where the output included the confidence interval for each prediction. The
proposed architecture is an improvement over the current approaches since it
speeds up the uncertainty estimation by 30X while being statistically more
rigorous.

negative:
Crypto-ransomware is characterized by its irreversible effect even after the
detection and removal. As such, the early detection is crucial to protect user
data and files of being held to ransom. Several solutions have proposed
utilizing the data extracted during the initial phases of the attacks before
the encryption takes place. However, the lack of enough data at the early
phases of the attack along with high dimensional features space renders the
model prone to overfitting which decreases its detection accuracy. To this end,
this paper proposed a novel redundancy coefficient gradual up-weighting
approach that was incorporated to the calculation of redundancy term of mutual
information to improve the feature selection process and enhance the accuracy
of the detection model. Several machine learning classifiers were used to
evaluate the detection performance of the proposed techniques. The experimental
results show that the accuracy of proposed techniques achieved higher detection
accuracy. Those results demonstrate the efficacy of the proposed techniques for
the early detection tasks.

negative:
Making disguise between real and fake news propagation through online social
networks is an important issue in many applications. The time gap between the
news release time and detection of its label is a significant step towards
broadcasting the real information and avoiding the fake. Therefore, one of the
challenging tasks in this area is to identify fake and real news in early
stages of propagation. However, there is a trade-off between minimizing the
time gap and maximizing accuracy. Despite recent efforts in detection of fake
news, there has been no significant work that explicitly incorporates early
detection in its model. In this paper, we focus on accurate early labeling of
news, and propose a model by considering earliness both in modeling and
prediction. The proposed method utilizes recurrent neural networks with a novel
loss function, and a new stopping rule. Given the context of news, we first
embed it with a class-specific text representation. Then, we utilize the
available public profile of users, and speed of news diffusion, for early
labeling of the news. Experiments on real datasets demonstrate the
effectiveness of our model both in terms of early labelling and accuracy,
compared to the state of the art baseline and models.

negative:
Consider a 2-player normal-form game repeated over time. We introduce an
adaptive learning procedure, where the players only observe their own realized
payoff at each stage. We assume that agents do not know their own payoff
function, and have no information on the other player. Furthermore, we assume
that they have restrictions on their own action set such that, at each stage,
their choice is limited to a subset of their action set. We prove that the
empirical distributions of play converge to the set of Nash equilibria for
zero-sum and potential games, and games where one player has two actions.

negative:
How will my face look when I get older? Or, for a more challenging question:
How will my brain look when I get older? To answer this question one must
devise (and learn from data) a multivariate auto-regressive function which
given an image and a desired target age generates an output image. While
collecting data for faces may be easier, collecting longitudinal brain data is
not trivial. We propose a deep learning-based method that learns to simulate
subject-specific brain ageing trajectories without relying on longitudinal
data. Our method synthesises images conditioned on two factors: age (a
continuous variable), and status of Alzheimer's Disease (AD, an ordinal
variable). With an adversarial formulation we learn the joint distribution of
brain appearance, age and AD status, and define reconstruction losses to
address the challenging problem of preserving subject identity. We compare with
several benchmarks using two widely used datasets. We evaluate the quality and
realism of synthesised images using ground-truth longitudinal data and a
pre-trained age predictor. We show that, despite the use of cross-sectional
data, our model learns patterns of gray matter atrophy in the middle temporal
gyrus in patients with AD. To demonstrate generalisation ability, we train on
one dataset and evaluate predictions on the other. In conclusion, our model
shows an ability to separate age, disease influence and anatomy using only 2D
cross-sectional data that should be useful in large studies into
neurodegenerative disease, that aim to combine several data sources. To
facilitate such future studies by the community at large our code is made
available at https://github.com/xiat0616/BrainAgeing.

negative:
Early identification and prevention of various plant diseases in commercial
farms and orchards is a key feature of precision agriculture technology. This
paper presents a high-performance real-time fine-grain object detection
framework that addresses several obstacles in plant disease detection that
hinder the performance of traditional methods, such as, dense distribution,
irregular morphology, multi-scale object classes, textural similarity, etc. The
proposed model is built on an improved version of the You Only Look Once
(YOLOv4) algorithm. The modified network architecture maximizes both detection
accuracy and speed by including the DenseNet in the back-bone to optimize
feature transfer and reuse, two new residual blocks in the backbone and neck
enhance feature extraction and reduce computing cost; the Spatial Pyramid
Pooling (SPP) enhances receptive field, and a modified Path Aggregation Network
(PANet) preserves fine-grain localized information and improve feature fusion.
Additionally, the use of the Hard-Swish function as the primary activation
improved the model's accuracy due to better nonlinear feature extraction. The
proposed model is tested in detecting four different diseases in tomato plants
under various challenging environments. The model outperforms the existing
state-of-the-art detection models in detection accuracy and speed. At a
detection rate of 70.19 FPS, the proposed model obtained a precision value of
$90.33 \%$, F1-score of $93.64 \%$, and a mean average precision ($mAP$) value
of $96.29 \%$. Current work provides an effective and efficient method for
detecting different plant diseases in complex scenarios that can be extended to
different fruit and crop detection, generic disease detection, and various
automated agricultural detection processes.

negative:
The segmentation of brain tumors in multimodal MRIs is one of the most
challenging tasks in medical image analysis. The recent state of the art
algorithms solving this task is based on machine learning approaches and deep
learning in particular. The amount of data used for training such models and
its variability is a keystone for building an algorithm with high
representation power. In this paper, we study the relationship between the
performance of the model and the amount of data employed during the training
process. On the example of brain tumor segmentation challenge, we compare the
model trained with labeled data provided by challenge organizers, and the same
model trained in omni-supervised manner using additional unlabeled data
annotated with the ensemble of heterogeneous models. As a result, a single
model trained with additional data achieves performance close to the ensemble
of multiple models and outperforms individual methods.

negative:
This paper presents a methodology for early detection of audio events from
audio streams. Early detection is the ability to infer an ongoing event during
its initial stage. The proposed system consists of a novel inference step
coupled with dual parallel tailored-loss deep neural networks (DNNs). The DNNs
share a similar architecture except for their loss functions, i.e. weighted
loss and multitask loss, which are designed to efficiently cope with issues
common to audio event detection. The inference step is newly introduced to make
use of the network outputs for recognizing ongoing events. The monotonicity of
the detection function is required for reliable early detection, and will also
be proved. Experiments on the ITC-Irst database show that the proposed system
achieves state-of-the-art detection performance. Furthermore, even partial
events are sufficient to achieve good performance similar to that obtained when
an entire event is observed, enabling early event detection.

negative:
Additive Manufacturing (AM) is a crucial component of the smart industry. In
this paper, we propose an automated quality grading system for the AM process
using a deep convolutional neural network (CNN) model. The CNN model is trained
offline using the images of the internal and surface defects in the
layer-by-layer deposition of materials and tested online by studying the
performance of detecting and classifying the failure in AM process at different
extruder speeds and temperatures. The model demonstrates the accuracy of 94%
and specificity of 96%, as well as above 75% in three classifier measures of
the Fscore, the sensitivity, and precision for classifying the quality of the
printing process in five grades in real-time. The proposed online model adds an
automated, consistent, and non-contact quality control signal to the AM process
that eliminates the manual inspection of parts after they are entirely built.
The quality monitoring signal can also be used by the machine to suggest
remedial actions by adjusting the parameters in real-time. The proposed quality
predictive model serves as a proof-of-concept for any type of AM machines to
produce reliable parts with fewer quality hiccups while limiting the waste of
both time and materials.

negative:
Transfer learning techniques are important to handle small training sets and
to allow for quick generalization even from only a few examples. The following
paper is the introduction as well as the literature overview part of my thesis
related to the topic of transfer learning for visual recognition problems.

negative:
When learning from positive and unlabelled data, it is a strong assumption
that the positive observations are randomly sampled from the distribution of
$X$ conditional on $Y = 1$, where X stands for the feature and Y the label.
Most existing algorithms are optimally designed under the assumption. However,
for many real-world applications, the observed positive examples are dependent
on the conditional probability $P(Y = 1|X)$ and should be sampled biasedly. In
this paper, we assume that a positive example with a higher $P(Y = 1|X)$ is
more likely to be labelled and propose a probabilistic-gap based PU learning
algorithms. Specifically, by treating the unlabelled data as noisy negative
examples, we could automatically label a group positive and negative examples
whose labels are identical to the ones assigned by a Bayesian optimal
classifier with a consistency guarantee. The relabelled examples have a biased
domain, which is remedied by the kernel mean matching technique. The proposed
algorithm is model-free and thus do not have any parameters to tune.
Experimental results demonstrate that our method works well on both generated
and real-world datasets.

negative:
Nearly all existing counting methods are designed for a specific object
class. Our work, however, aims to create a counting model able to count any
class of object. To achieve this goal, we formulate counting as a matching
problem, enabling us to exploit the image self-similarity property that
naturally exists in object counting problems. We make the following three
contributions: first, a Generic Matching Network (GMN) architecture that can
potentially count any object in a class-agnostic manner; second, by
reformulating the counting problem as one of matching objects, we can take
advantage of the abundance of video data labeled for tracking, which contains
natural repetitions suitable for training a counting model. Such data enables
us to train the GMN. Third, to customize the GMN to different user
requirements, an adapter module is used to specialize the model with minimal
effort, i.e. using a few labeled examples, and adapting only a small fraction
of the trained parameters. This is a form of few-shot learning, which is
practical for domains where labels are limited due to requiring expert
knowledge (e.g. microbiology). We demonstrate the flexibility of our method on
a diverse set of existing counting benchmarks: specifically cells, cars, and
human crowds. The model achieves competitive performance on cell and crowd
counting datasets, and surpasses the state-of-the-art on the car dataset using
only three training images. When training on the entire dataset, the proposed
method outperforms all previous methods by a large margin.

negative:
In realistic environments, speech is usually interfered by various noise and
reverberation, which dramatically degrades the performance of automatic speech
recognition (ASR) systems. To alleviate this issue, the commonest way is to use
a well-designed speech enhancement approach as the front-end of ASR. However,
more complex pipelines, more computations and even higher hardware costs
(microphone array) are additionally consumed for this kind of methods. In
addition, speech enhancement would result in speech distortions and mismatches
to training. In this paper, we propose an adversarial training method to
directly boost noise robustness of acoustic model. Specifically, a jointly
compositional scheme of generative adversarial net (GAN) and neural
network-based acoustic model (AM) is used in the training phase. GAN is used to
generate clean feature representations from noisy features by the guidance of a
discriminator that tries to distinguish between the true clean signals and
generated signals. The joint optimization of generator, discriminator and AM
concentrates the strengths of both GAN and AM for speech recognition.
Systematic experiments on CHiME-4 show that the proposed method significantly
improves the noise robustness of AM and achieves the average relative error
rate reduction of 23.38% and 11.54% on the development and test set,
respectively.

negative:
Data science requires time-consuming iterative manual activities. In
particular, activities such as data selection, preprocessing, transformation,
and mining, highly depend on iterative trial-and-error processes that could be
sped-up significantly by providing quick feedback on the impact of changes. The
idea of progressive data science is to compute the results of changes in a
progressive manner, returning a first approximation of results quickly and
allow iterative refinements until converging to a final result. Enabling the
user to interact with the intermediate results allows an early detection of
erroneous or suboptimal choices, the guided definition of modifications to the
pipeline and their quick assessment. In this paper, we discuss the
progressiveness challenges arising in different steps of the data science
pipeline. We describe how changes in each step of the pipeline impact the
subsequent steps and outline why progressive data science will help to make the
process more effective. Computing progressive approximations of outcomes
resulting from changes creates numerous research challenges, especially if the
changes are made in the early steps of the pipeline. We discuss these
challenges and outline first steps towards progressiveness, which, we argue,
will ultimately help to significantly speed-up the overall data science
process.

negative:
In this technical report I present my method for automatic synthetic dataset
generation for object detection and demonstrate it on the video game League of
Legends. This report furthermore serves as a handbook on how to automatically
generate datasets and as an introduction on the dataset generation part of the
LeagueAI framework. The LeagueAI framework is a software framework that
provides detailed information about the game League of Legends based on the
same input a human player would have, namely vision. The framework allows
researchers and enthusiasts to develop their own intelligent agents or to
extract detailed information about the state of the game. A big problem of
machine vision applications usually is the laborious work of gathering large
amounts of hand labeled data. Thus, a crucial part of the vision pipeline of
the LeagueAI framework, the dataset generation, is presented in this report.
The method involves extracting image raw data from the game's 3D models and
combining them with the game background to create game-like synthetic images
and to generate the corresponding labels automatically. In an experiment I
compared a model trained on synthetic data to a model trained on hand labeled
data and a model trained on a combined dataset. The model trained on the
synthetic data showed higher detection precision on more classes and more
reliable tracking performance of the player character. The model trained on the
combined dataset did not perform better because of the different formats of the
older hand labeled dataset and the synthetic data.

negative:
A system is presented that segments, clusters and predicts musical audio in
an unsupervised manner, adjusting the number of (timbre) clusters
instantaneously to the audio input. A sequence learning algorithm adapts its
structure to a dynamically changing clustering tree. The flow of the system is
as follows: 1) segmentation by onset detection, 2) timbre representation of
each segment by Mel frequency cepstrum coefficients, 3) discretization by
incremental clustering, yielding a tree of different sound classes (e.g.
instruments) that can grow or shrink on the fly driven by the instantaneous
sound events, resulting in a discrete symbol sequence, 4) extraction of
statistical regularities of the symbol sequence, using hierarchical N-grams and
the newly introduced conceptual Boltzmann machine, and 5) prediction of the
next sound event in the sequence. The system's robustness is assessed with
respect to complexity and noisiness of the signal. Clustering in isolation
yields an adjusted Rand index (ARI) of 82.7% / 85.7% for data sets of singing
voice and drums. Onset detection jointly with clustering achieve an ARI of
81.3% / 76.3% and the prediction of the entire system yields an ARI of 27.2% /
39.2%.

negative:
Detection of protein-protein interactions (PPIs) plays a vital role in
molecular biology. Particularly, infections are caused by the interactions of
host and pathogen proteins. It is important to identify host-pathogen
interactions (HPIs) to discover new drugs to counter infectious diseases.
Conventional wet lab PPI prediction techniques have limitations in terms of
large scale application and budget. Hence, computational approaches are
developed to predict PPIs. This study aims to develop large margin machine
learning models to predict interspecies PPIs with a special interest in
host-pathogen protein interactions (HPIs). Especially, we focus on seeking
answers to three queries that arise while developing an HPI predictor. 1) How
should we select negative samples? 2) What should be the size of negative
samples as compared to the positive samples? 3) What type of margin violation
penalty should be used to train the predictor? We compare two available methods
for negative sampling. Moreover, we propose a new method of assigning weights
to each training example in weighted SVM depending on the distance of the
negative examples from the positive examples. We have also developed a web
server for our HPI predictor called HoPItor (Host Pathogen Interaction
predicTOR) that can predict interactions between human and viral proteins. This
webserver can be accessed at the URL:
http://faculty.pieas.edu.pk/fayyaz/software.html#HoPItor.

negative:
We present an approach to analyze $C^1(\mathbb{R}^m)$ functions that
addresses limitations present in the Active Subspaces (AS) method of
Constantine et al.(2015; 2014). Under appropriate hypotheses, our Active
Manifolds (AM) method identifies a 1-D curve in the domain (the active
manifold) on which nearly all values of the unknown function are attained, and
which can be exploited for approximation or analysis, especially when $m$ is
large (high-dimensional input space). We provide theorems justifying our AM
technique and an algorithm permitting functional approximation and sensitivity
analysis. Using accessible, low-dimensional functions as initial examples, we
show AM reduces approximation error by an order of magnitude compared to AS, at
the expense of more computation. Following this, we revisit the sensitivity
analysis by Glaws et al. (2017), who apply AS to analyze a magnetohydrodynamic
power generator model, and compare the performance of AM on the same data. Our
analysis provides detailed information not captured by AS, exhibiting the
influence of each parameter individually along an active manifold. Overall, AM
represents a novel technique for analyzing functional models with benefits
including: reducing $m$-dimensional analysis to a 1-D analogue, permitting more
accurate regression than AS (at more computational expense), enabling more
informative sensitivity analysis, and granting accessible visualizations(2-D
plots) of parameter sensitivity along the AM.

negative:
A neuroscience method to understanding the brain is to find and study the
preferred stimuli that highly activate an individual cell or groups of cells.
Recent advances in machine learning enable a family of methods to synthesize
preferred stimuli that cause a neuron in an artificial or biological brain to
fire strongly. Those methods are known as Activation Maximization (AM) or
Feature Visualization via Optimization. In this chapter, we (1) review existing
AM techniques in the literature; (2) discuss a probabilistic interpretation for
AM; and (3) review the applications of AM in debugging and explaining networks.

negative:
We emphasize the importance of asking the right question when interpreting
the decisions of a learning model. We discuss a natural extension of the
theoretical machinery from Janzing et. al. 2020, which answers the question
"Why did my model predict a person has cancer?" for answering a more involved
question, "What caused my model to predict a person has cancer?" While the
former quantifies the direct effects of variables on the model, the latter also
accounts for indirect effects, thereby providing meaningful insights wherever
human beings can reason in terms of cause and effect. We propose three broad
categories for interpretations: observational, model-specific and causal each
of which are significant in their own right. Furthermore, this paper quantifies
feature relevance by weaving different natures of interpretations together with
different measures as characteristic functions for Shapley symmetrization.
Besides the widely used expected value of the model, we also discuss measures
of statistical uncertainty and dispersion as informative candidates, and their
merits in generating explanations for each data point, some of which are used
in this context for the first time. These measures are not only useful for
studying the influence of variables on the model output, but also on the
predictive performance of the model, and for that we propose relevant
characteristic functions that are also used for the first time.

negative:
We introduce a new task of rephrasing for a more natural virtual assistant.
Currently, virtual assistants work in the paradigm of intent slot tagging and
the slot values are directly passed as-is to the execution engine. However,
this setup fails in some scenarios such as messaging when the query given by
the user needs to be changed before repeating it or sending it to another user.
For example, for queries like 'ask my wife if she can pick up the kids' or
'remind me to take my pills', we need to rephrase the content to 'can you pick
up the kids' and 'take your pills' In this paper, we study the problem of
rephrasing with messaging as a use case and release a dataset of 3000 pairs of
original query and rephrased query. We show that BART, a pre-trained
transformers-based masked language model with auto-regressive decoding, is a
strong baseline for the task, and show improvements by adding a copy-pointer
and copy loss to it. We analyze different tradeoffs of BART-based and
LSTM-based seq2seq models, and propose a distilled LSTM-based seq2seq as the
best practical model.

negative:
Artificial self-perception is the machine ability to perceive its own body,
i.e., the mastery of modal and intermodal contingencies of performing an action
with a specific sensors/actuators body configuration. In other words, the
spatio-temporal patterns that relate its sensors (e.g. visual, proprioceptive,
tactile, etc.), its actions and its body latent variables are responsible of
the distinction between its own body and the rest of the world. This paper
describes some of the latest approaches for modelling artificial body
self-perception: from Bayesian estimation to deep learning. Results show the
potential of these free-model unsupervised or semi-supervised
crossmodal/intermodal learning approaches. However, there are still challenges
that should be overcome before we achieve artificial multisensory body
perception.

negative:
Natural language processing and machine learning algorithms have been shown
to be effective in a variety of applications. In this work, we contribute to
the area of AI adoption in the public sector. We present an automated system
that was used to process textual information, generate important keywords, and
automatically summarize key elements of the Meadville community statements. We
also describe the process of collaboration with My Meadville administrators
during the development of our system. My Meadville, a community initiative,
supported by the city of Meadville conducted a large number of interviews with
the residents of Meadville during the community events and transcribed these
interviews into textual data files. Their goal was to uncover the issues of
importance to the Meadville residents in an attempt to enhance public services.
Our AI system cleans and pre-processes the interview data, then using machine
learning algorithms it finds important keywords and key excerpts from each
interview. It also provides searching functionality to find excerpts from
relevant interviews based on specific keywords. Our automated system allowed
the city to save over 300 hours of human labor that would have taken to read
all interviews and highlight important points. Our findings are being used by
My Meadville initiative to locate important information from the collected data
set for ongoing community enhancement projects, to highlight relevant community
assets, and to assist in identifying the steps to be taken based on the
concerns and areas of improvement identified by the community members.

negative:
In this dissertation, I derive a new method to estimate the
Vapnik-Chervonenkis Dimension (VCD) for the class of linear functions. This
method is inspired by the technique developed by Vapnik et al. Vapnik et al.
(1994). My contribution rests on the approximation of the expected maximum
difference between two empirical Losses (EMDBTEL). In fact, I use a
cross-validated form of the error to compute the EMDBTEL, and I make the bound
on the EMDBTEL tighter by minimizing a constant in of its right upper bound. I
also derive two bounds for the true unknown risk using the additive (ERM1) and
the multiplicative (ERM2) Chernoff bounds. These bounds depend on the estimated
VCD and the empirical risk. These bounds can be used to perform model selection
and to declare with high probability, the chosen model will perform better
without making strong assumptions about the data generating process (DG).
  I measure the accuracy of my technique on simulated datasets and also on
three real datasets. The model selection provided by VCD was always as good as
if not better than the other methods under reasonable conditions.

negative:
Voice Onset Time (VOT), a key measurement of speech for basic research and
applied medical studies, is the time between the onset of a stop burst and the
onset of voicing. When the voicing onset precedes burst onset the VOT is
negative; if voicing onset follows the burst, it is positive. In this work, we
present a deep-learning model for accurate and reliable measurement of VOT in
naturalistic speech. The proposed system addresses two critical issues: it can
measure positive and negative VOT equally well, and it is trained to be robust
to variation across annotations. Our approach is based on the structured
prediction framework, where the feature functions are defined to be RNNs. These
learn to capture segmental variation in the signal. Results suggest that our
method substantially improves over the current state-of-the-art. In contrast to
previous work, our Deep and Robust VOT annotator, Dr.VOT, can successfully
estimate negative VOTs while maintaining state-of-the-art performance on
positive VOTs. This high level of performance generalizes to new corpora
without further retraining. Index Terms: structured prediction, multi-task
learning, adversarial training, recurrent neural networks, sequence
segmentation.

negative:
We study the low rank regression problem $\my = M\mx + \epsilon$, where $\mx$
and $\my$ are $d_1$ and $d_2$ dimensional vectors respectively. We consider the
extreme high-dimensional setting where the number of observations $n$ is less
than $d_1 + d_2$. Existing algorithms are designed for settings where $n$ is
typically as large as $\Rank(M)(d_1+d_2)$. This work provides an efficient
algorithm which only involves two SVD, and establishes statistical guarantees
on its performance. The algorithm decouples the problem by first estimating the
precision matrix of the features, and then solving the matrix denoising
problem. To complement the upper bound, we introduce new techniques for
establishing lower bounds on the performance of any algorithm for this problem.
Our preliminary experiments confirm that our algorithm often out-performs
existing baselines, and is always at least competitive.

negative:
Machine learning algorithms operating on mobile networks can be characterized
into three different categories. First is the classical situation in which the
end-user devices send their data to a central server where this data is used to
train a model. Second is the distributed setting in which each device trains
its own model and send its model parameters to a central server where these
model parameters are aggregated to create one final model. Third is the
federated learning setting in which, at any given time $t$, a certain number of
active end users train with their own local data along with feedback provided
by the central server and then send their newly estimated model parameters to
the central server. The server, then, aggregates these new parameters, updates
its own model, and feeds the updated parameters back to all the end users,
continuing this process until it converges.
  The main objective of this work is to provide an information-theoretic
framework for all of the aforementioned learning paradigms. Moreover, using the
provided framework, we develop upper and lower bounds on the generalization
error together with bounds on the privacy leakage in the classical, distributed
and federated learning settings.
  Keywords: Federated Learning, Distributed Learning, Machine Learning, Model
Aggregation.

negative:
Additive Manufacturing (AM) is a manufacturing paradigm that builds
three-dimensional objects from a computer-aided design model by successively
adding material layer by layer. AM has become very popular in the past decade
due to its utility for fast prototyping such as 3D printing as well as
manufacturing functional parts with complex geometries using processes such as
laser metal deposition that would be difficult to create using traditional
machining. As the process for creating an intricate part for an expensive metal
such as Titanium is prohibitive with respect to cost, computational models are
used to simulate the behavior of AM processes before the experimental run.
However, as the simulations are computationally costly and time-consuming for
predicting multiscale multi-physics phenomena in AM, physics-informed
data-driven machine-learning systems for predicting the behavior of AM
processes are immensely beneficial. Such models accelerate not only multiscale
simulation tools but also empower real-time control systems using in-situ data.
In this paper, we design and develop essential components of a scientific
framework for developing a data-driven model-based real-time control system.
Finite element methods are employed for solving time-dependent heat equations
and developing the database. The proposed framework uses extremely randomized
trees - an ensemble of bagged decision trees as the regression algorithm
iteratively using temperatures of prior voxels and laser information as inputs
to predict temperatures of subsequent voxels. The models achieve mean absolute
percentage errors below 1% for predicting temperature profiles for AM
processes. The code is made available for the research community at
https://github.com/paularindam/ml-iter-additive.

negative:
The ability to accurately quantify the performance an additively manufactured
(AM) product is important for a widespread industry adoption of AM as the
design is required to: (1) satisfy geometrical constraints, (2) satisfy
structural constraints dictated by its intended function, and (3) be cost
effective compared to traditional manufacturing methods. Optimization
techniques offer design aids in creating cost-effective structures that meet
the prescribed structural objectives. The fundamental problem in existing
approaches lies in the difficulty to quantify the structural performance as
each unique design leads to a new set of analyses to determine the structural
robustness and such analyses can be very costly due to the complexity of in-use
forces experienced by the structure. This work develops computationally
tractable methods tailored to maximize the structural performance of AM
products. A geometry preserving build orientation optimization method as well
as data-driven shape optimization approaches to structural design are
presented. Proposed methods greatly enhance the value of AM technology by
taking advantage of the design space enabled by it for a broad class of
problems involving complex in-use loads.

negative:
We address the problem of solving mixed random linear equations. We have
unlabeled observations coming from multiple linear regressions, and each
observation corresponds to exactly one of the regression models. The goal is to
learn the linear regressors from the observations. Classically, Alternating
Minimization (AM) (which is a variant of Expectation Maximization (EM)) is used
to solve this problem. AM iteratively alternates between the estimation of
labels and solving the regression problems with the estimated labels.
Empirically, it is observed that, for a large variety of non-convex problems
including mixed linear regression, AM converges at a much faster rate compared
to gradient based algorithms. However, the existing theory suggests similar
rate of convergence for AM and gradient based methods, failing to capture this
empirical behavior. In this paper, we close this gap between theory and
practice for the special case of a mixture of $2$ linear regressions. We show
that, provided initialized properly, AM enjoys a \emph{super-linear} rate of
convergence in certain parameter regimes. To the best of our knowledge, this is
the first work that theoretically establishes such rate for AM. Hence, if we
want to recover the unknown regressors upto an error (in $\ell_2$ norm) of
$\epsilon$, AM only takes $\mathcal{O}(\log \log (1/\epsilon))$ iterations.
Furthermore, we compare AM with a gradient based heuristic algorithm
empirically and show that AM dominates in iteration complexity as well as
wall-clock time.