pwc-archive/papers-with-abstracts huggingface dataset:
DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG
Authors: Akara Supratak, Hao Dong, Chao Wu, Yike Guo
Date: 2017-03-12 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Sleep Stage Detection

The present study proposes a deep learning model, named DeepSleepNet, for
automatic sleep stage scoring based on raw single-channel EEG. Most of the
existing methods rely on hand-engineered features which require prior knowledge
of sleep analysis. Only a few of them encode the temporal information such as
transition rules, which is important for identifying the next sleep stages,
into the extracted features. In the proposed model, we utilize Convolutional
Neural Networks to extract time-invariant features, and bidirectional-Long
Short-Term Memory to learn transition rules among sleep stages automatically
from EEG epochs. We implement a two-step training algorithm to train our model
efficiently. We evaluated our model using different single-channel EEGs
(F4-EOG(Left), Fpz-Cz and Pz-Oz) from two public sleep datasets, that have
different properties (e.g., sampling rate) and scoring standards (AASM and
R&K). The results showed that our model achieved similar overall accuracy and
macro F1-score (MASS: 86.2%-81.7, Sleep-EDF: 82.0%-76.9) compared to the
state-of-the-art methods (MASS: 85.9%-80.5, Sleep-EDF: 78.9%-73.7) on both
datasets. This demonstrated that, without changing the model architecture and
the training algorithm, our model could automatically learn features for sleep
stage scoring from different raw single-channel EEGs from different datasets
without utilizing any hand-engineered features.
Stylometric Analysis of Early Modern Period English Plays
Authors: Mark Eisen, Santiago Segarra, Gabriel Egan, Alejandro Ribeiro
Date: 2016-10-18 00:00:00
Tasks: Authorship Attribution

Function word adjacency networks (WANs) are used to study the authorship of
plays from the Early Modern English period. In these networks, nodes are
function words and directed edges between two nodes represent the relative
frequency of directed co-appearance of the two words. For every analyzed play,
a WAN is constructed and these are aggregated to generate author profile
networks. We first study the similarity of writing styles between Early English
playwrights by comparing the profile WANs. The accuracy of using WANs for
authorship attribution is then demonstrated by attributing known plays among
six popular playwrights. Moreover, the WAN method is shown to outperform other
frequency-based methods on attributing Early English plays. In addition, WANs
are shown to be reliable classifiers even when attributing collaborative plays.
For several plays of disputed co-authorship, a deeper analysis is performed by
attributing every act and scene separately, in which we both corroborate
existing breakdowns and provide evidence of new assignments.
Effective sketching methods for value function approximation
Authors: Yangchen Pan, Erfan Sadeqi Azer, Martha White
Date: 2017-08-03 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

High-dimensional representations, such as radial basis function networks or
tile coding, are common choices for policy evaluation in reinforcement
learning. Learning with such high-dimensional representations, however, can be
expensive, particularly for matrix methods, such as least-squares temporal
difference learning or quasi-Newton methods that approximate matrix step-sizes.
In this work, we explore the utility of sketching for these two classes of
algorithms. We highlight issues with sketching the high-dimensional features
directly, which can incur significant bias. As a remedy, we demonstrate how to
use sketching more sparingly, with only a left-sided sketch, that can still
enable significant computational gains and the use of these matrix-based
learning algorithms that are less sensitive to parameters. We empirically
investigate these algorithms, in four domains with a variety of
representations. Our aim is to provide insights into effective use of sketching
in practice.
Ensemble representation learning: an analysis of fitness and survival for wrapper-based genetic programming methods
Authors: William La Cava, Jason H. Moore
Date: 2017-03-20 00:00:00
Tasks: Feature Engineering, General Classification, Representation Learning

Recently we proposed a general, ensemble-based feature engineering wrapper
(FEW) that was paired with a number of machine learning methods to solve
regression problems. Here, we adapt FEW for supervised classification and
perform a thorough analysis of fitness and survival methods within this
framework. Our tests demonstrate that two fitness metrics, one introduced as an
adaptation of the silhouette score, outperform the more commonly used Fisher
criterion. We analyze survival methods and demonstrate that $\epsilon$-lexicase
survival works best across our test problems, followed by random survival which
outperforms both tournament and deterministic crowding. We conduct a benchmark
comparison to several classification methods using a large set of problems and
show that FEW can improve the best classifier performance in several cases. We
show that FEW generates consistent, meaningful features for a biomedical
problem with different ML pairings.
Why does deep and cheap learning work so well?
Authors: Henry W. Lin, Max Tegmark, David Rolnick
Date: 2016-08-29 00:00:00

We show how the success of deep learning could depend not only on mathematics
but also on physics: although well-known mathematical theorems guarantee that
neural networks can approximate arbitrary functions well, the class of
functions of practical interest can frequently be approximated through "cheap
learning" with exponentially fewer parameters than generic ones. We explore how
properties frequently encountered in physics such as symmetry, locality,
compositionality, and polynomial log-probability translate into exceptionally
simple neural networks. We further argue that when the statistical process
generating the data is of a certain hierarchical form prevalent in physics and
machine-learning, a deep neural network can be more efficient than a shallow
one. We formalize these claims using information theory and discuss the
relation to the renormalization group. We prove various "no-flattening
theorems" showing when efficient linear deep networks cannot be accurately
approximated by shallow ones without efficiency loss, for example, we show that
$n$ variables cannot be multiplied using fewer than 2^n neurons in a single
hidden layer.
Unsupervised Representation Learning by Sorting Sequences
Authors: Hsin-Ying Lee, Jia-Bin Huang, Maneesh Singh, Ming-Hsuan Yang
Date: 2017-08-03 00:00:00
Proceeding: ICCV 2017 10
Tasks: Action Recognition, image-classification, Image Classification, object-detection, Object Detection, Representation Learning, Self-Supervised Action Recognition, Temporal Action Localization

We present an unsupervised representation learning approach using videos
without semantic labels. We leverage the temporal coherence as a supervisory
signal by formulating representation learning as a sequence sorting task. We
take temporally shuffled frames (i.e., in non-chronological order) as inputs
and train a convolutional neural network to sort the shuffled sequences.
Similar to comparison-based sorting algorithms, we propose to extract features
from all frame pairs and aggregate them to predict the correct order. As
sorting shuffled image sequence requires an understanding of the statistical
temporal structure of images, training with such a proxy task allows us to
learn rich and generalizable visual representation. We validate the
effectiveness of the learned representation using our method as pre-training on
high-level recognition problems. The experimental results show that our method
compares favorably against state-of-the-art methods on action recognition,
image classification and object detection tasks.
Stochastic Separation Theorems
Authors: A. N. Gorban, I. Y. Tyukin
Date: 2017-03-03 00:00:00

The problem of non-iterative one-shot and non-destructive correction of
unavoidable mistakes arises in all Artificial Intelligence applications in the
real world. Its solution requires robust separation of samples with errors from
samples where the system works properly. We demonstrate that in (moderately)
high dimension this separation could be achieved with probability close to one
by linear discriminants. Surprisingly, separation of a new image from a very
large set of known images is almost always possible even in moderately high
dimensions by linear functionals, and coefficients of these functionals can be
found explicitly. Based on fundamental properties of measure concentration, we
show that for $M<a\exp(b{n})$ random $M$-element sets in $\mathbb{R}^n$ are
linearly separable with probability $p$, $p>1-\vartheta$, where $1>\vartheta>0$
is a given small constant. Exact values of $a,b>0$ depend on the probability
distribution that determines how the random $M$-element sets are drawn, and on
the constant $\vartheta$. These {\em stochastic separation theorems} provide a
new instrument for the development, analysis, and assessment of machine
learning methods and algorithms in high dimension. Theoretical statements are
illustrated with numerical examples.
Real-time Geometry-Aware Augmented Reality in Minimally Invasive Surgery
Authors: Long Chen, Wen Tang, Nigel W. John
Date: 2017-08-03 00:00:00
Tasks: Stereo Matching, Stereo Matching Hand, Surface Reconstruction

The potential of Augmented Reality (AR) technology to assist minimally
invasive surgeries (MIS) lies in its computational performance and accuracy in
dealing with challenging MIS scenes. Even with the latest hardware and software
technologies, achieving both real-time and accurate augmented information
overlay in MIS is still a formidable task. In this paper, we present a novel
real-time AR framework for MIS that achieves interactive geometric aware
augmented reality in endoscopic surgery with stereo views. Our framework tracks
the movement of the endoscopic camera and simultaneously reconstructs a dense
geometric mesh of the MIS scene. The movement of the camera is predicted by
minimising the re-projection error to achieve a fast tracking performance,
while the 3D mesh is incrementally built by a dense zero mean normalised cross
correlation stereo matching method to improve the accuracy of the surface
reconstruction. Our proposed system does not require any prior template or
pre-operative scan and can infer the geometric information intra-operatively in
real-time. With the geometric information available, our proposed AR framework
is able to interactively add annotations, localisation of tumours and vessels,
and measurement labelling with greater precision and accuracy compared with the
state of the art approaches.
Recent Developments and Future Challenges in Medical Mixed Reality
Authors: Long Chen, Thomas Day, Wen Tang, Nigel W. John
Date: 2017-08-03 00:00:00
Tasks: Classification, General Classification, Mixed Reality

Mixed Reality (MR) is of increasing interest within technology-driven modern
medicine but is not yet used in everyday practice. This situation is changing
rapidly, however, and this paper explores the emergence of MR technology and
the importance of its utility within medical applications. A classification of
medical MR has been obtained by applying an unbiased text mining method to a
database of 1,403 relevant research papers published over the last two decades.
The classification results reveal a taxonomy for the development of medical MR
research during this period as well as suggesting future trends. We then use
the classification to analyse the technology and applications developed in the
last five years. Our objective is to aid researchers to focus on the areas
where technology advancements in medical MR are most needed, as well as
providing medical practitioners with a useful source of reference.
Detecting early signs of depressive and manic episodes in patients with bipolar disorder using the signature-based model
Authors: Andrey Kormilitzin, Kate E. A. Saunders, Paul J. Harrison, John R. Geddes, Terry Lyons
Date: 2017-08-03 00:00:00
Tasks: Time Series, Time Series Analysis

Recurrent major mood episodes and subsyndromal mood instability cause
substantial disability in patients with bipolar disorder. Early identification
of mood episodes enabling timely mood stabilisation is an important clinical
goal. Recent technological advances allow the prospective reporting of mood in
real time enabling more accurate, efficient data capture. The complex nature of
these data streams in combination with challenge of deriving meaning from
missing data mean pose a significant analytic challenge. The signature method
is derived from stochastic analysis and has the ability to capture important
properties of complex ordered time series data. To explore whether the onset of
episodes of mania and depression can be identified using self-reported mood
data.
Estimating speech from lip dynamics
Authors: Jithin Donny George, Ronan Keane, Conor Zellmer
Date: 2017-08-03 00:00:00
Tasks: Lip Reading, Position, Sentence

The goal of this project is to develop a limited lip reading algorithm for a
subset of the English language. We consider a scenario in which no audio
information is available. The raw video is processed and the position of the
lips in each frame is extracted. We then prepare the lip data for processing
and classify the lips into visemes and phonemes. Hidden Markov Models are used
to predict the words the speaker is saying based on the sequences of classified
phonemes and visemes. The GRID audiovisual sentence corpus [10][11] database is
used for our study.
Unsupervised Video Understanding by Reconciliation of Posture Similarities
Authors: Timo Milbich, Miguel Bautista, Ekaterina Sutter, Bjorn Ommer
Date: 2017-08-03 00:00:00
Proceeding: ICCV 2017 10
Tasks: Action Classification, Retrieval, Super-Resolution, Video Understanding

Understanding human activity and being able to explain it in detail surpasses
mere action classification by far in both complexity and value. The challenge
is thus to describe an activity on the basis of its most fundamental
constituents, the individual postures and their distinctive transitions.
Supervised learning of such a fine-grained representation based on elementary
poses is very tedious and does not scale. Therefore, we propose a completely
unsupervised deep learning procedure based solely on video sequences, which
starts from scratch without requiring pre-trained networks, predefined body
models, or keypoints. A combinatorial sequence matching algorithm proposes
relations between frames from subsets of the training data, while a CNN is
reconciling the transitivity conflicts of the different subsets to learn a
single concerted pose embedding despite changes in appearance across sequences.
Without any manual annotation, the model learns a structured representation of
postures and their temporal development. The model not only enables retrieval
of similar postures but also temporal super-resolution. Additionally, based on
a recurrent formulation, next frames can be synthesized.
Self corrective Perturbations for Semantic Segmentation and Classification
Authors: Swami Sankaranarayanan, Arpit Jain, Ser Nam Lim
Date: 2017-03-23 00:00:00
Tasks: Classification, General Classification, Scene Labeling, Semantic Segmentation

Convolutional Neural Networks have been a subject of great importance over
the past decade and great strides have been made in their utility for producing
state of the art performance in many computer vision problems. However, the
behavior of deep networks is yet to be fully understood and is still an active
area of research. In this work, we present an intriguing behavior: pre-trained
CNNs can be made to improve their predictions by structurally perturbing the
input. We observe that these perturbations - referred as Guided Perturbations -
enable a trained network to improve its prediction performance without any
learning or change in network weights. We perform various ablative experiments
to understand how these perturbations affect the local context and feature
representations. Furthermore, we demonstrate that this idea can improve
performance of several existing approaches on semantic segmentation and scene
labeling tasks on the PASCAL VOC dataset and supervised classification tasks on
MNIST and CIFAR10 datasets.
Patch-based adaptive weighting with segmentation and scale (PAWSS) for visual tracking
Authors: Xiaofei Du, Alessio Dore, Danail Stoyanov
Date: 2017-08-03 00:00:00
Tasks: Object, Object Tracking, Visual Object Tracking, Visual Tracking

Tracking-by-detection algorithms are widely used for visual tracking, where
the problem is treated as a classification task where an object model is
updated over time using online learning techniques. In challenging conditions
where an object undergoes deformation or scale variations, the update step is
prone to include background information in the model appearance or to lack the
ability to estimate the scale change, which degrades the performance of the
classifier. In this paper, we incorporate a Patch-based Adaptive Weighting with
Segmentation and Scale (PAWSS) tracking framework that tackles both the scale
and background problems. A simple but effective colour-based segmentation model
is used to suppress background information and multi-scale samples are
extracted to enrich the training pool, which allows the tracker to handle both
incremental and abrupt scale variations between frames. Experimentally, we
evaluate our approach on the online tracking benchmark (OTB) dataset and Visual
Object Tracking (VOT) challenge datasets. The results show that our approach
outperforms recent state-of-the-art trackers, and it especially improves the
successful rate score on the OTB dataset, while on the VOT datasets, PAWSS
ranks among the top trackers while operating at real-time frame rates.
A Self-supervised Learning System for Object Detection using Physics Simulation and Multi-view Pose Estimation
Authors: Chaitanya Mitash, Kostas E. Bekris, Abdeslam Boularias
Date: 2017-03-09 00:00:00
Tasks: Object, object-detection, Object Detection, Pose Estimation, Self-Supervised Learning, Synthetic Data Generation

Progress has been achieved recently in object detection given advancements in
deep learning. Nevertheless, such tools typically require a large amount of
training data and significant manual effort to label objects. This limits their
applicability in robotics, where solutions must scale to a large number of
objects and variety of conditions. This work proposes an autonomous process for
training a Convolutional Neural Network (CNN) for object detection and pose
estimation in robotic setups. The focus is on detecting objects placed in
cluttered, tight environments, such as a shelf with multiple objects. In
particular, given access to 3D object models, several aspects of the
environment are physically simulated. The models are placed in physically
realistic poses with respect to their environment to generate a labeled
synthetic dataset. To further improve object detection, the network self-trains
over real images that are labeled using a robust multi-view pose estimation
process. The proposed training process is evaluated on several existing
datasets and on a dataset collected for this paper with a Motoman robotic arm.
Results show that the proposed approach outperforms popular training processes
relying on synthetic - but not physically realistic - data and manual
annotation. The key contributions are the incorporation of physical reasoning
in the synthetic data generation process and the automation of the annotation
process over real images.
Mixed Neural Network Approach for Temporal Sleep Stage Classification
Authors: Hao Dong, Akara Supratak, Wei Pan, Chao Wu, Paul M. Matthews, Yike Guo
Date: 2016-10-15 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG), General Classification

This paper proposes a practical approach to addressing limitations posed by
use of single active electrodes in applications for sleep stage classification.
Electroencephalography (EEG)-based characterizations of sleep stage progression
contribute the diagnosis and monitoring of the many pathologies of sleep.
Several prior reports have explored ways of automating the analysis of sleep
EEG and of reducing the complexity of the data needed for reliable
discrimination of sleep stages in order to make it possible to perform sleep
studies at lower cost in the home (rather than only in specialized clinical
facilities). However, these reports have involved recordings from electrodes
placed on the cranial vertex or occiput, which can be uncomfortable or
difficult for subjects to position. Those that have utilized single EEG
channels which contain less sleep information, have showed poor classification
performance. We have taken advantage of Rectifier Neural Network for feature
detection and Long Short-Term Memory (LSTM) network for sequential data
learning to optimize classification performance with single electrode
recordings. After exploring alternative electrode placements, we found a
comfortable configuration of a single-channel EEG on the forehead and have
shown that it can be integrated with additional electrodes for simultaneous
recording of the electroocuolgram (EOG). Evaluation of data from 62 people
(with 494 hours sleep) demonstrated better performance of our analytical
algorithm for automated sleep classification than existing approaches using
vertex or occipital electrode placements. Use of this recording configuration
with neural network deconvolution promises to make clinically indicated home
sleep studies practical.
Deep Semantic Segmentation for Automated Driving: Taxonomy, Roadmap and Challenges
Authors: Mennatullah Siam, Sara Elkerdawy, Martin Jagersand, Senthil Yogamani
Date: 2017-07-08 00:00:00
Tasks: Segmentation, Semantic Segmentation, Survey

Semantic segmentation was seen as a challenging computer vision problem few
years ago. Due to recent advancements in deep learning, relatively accurate
solutions are now possible for its use in automated driving. In this paper, the
semantic segmentation problem is explored from the perspective of automated
driving. Most of the current semantic segmentation algorithms are designed for
generic images and do not incorporate prior structure and end goal for
automated driving. First, the paper begins with a generic taxonomic survey of
semantic segmentation algorithms and then discusses how it fits in the context
of automated driving. Second, the particular challenges of deploying it into a
safety system which needs high level of accuracy and robustness are listed.
Third, different alternatives instead of using an independent semantic
segmentation module are explored. Finally, an empirical evaluation of various
semantic segmentation architectures was performed on CamVid dataset in terms of
accuracy and speed. This paper is a preliminary shorter version of a more
detailed survey which is work in progress.