pwc-archive/papers-with-abstracts huggingface dataset:
RetiNet: Automatic AMD identification in OCT volumetric data
Authors: Stefanos Apostolopoulos, Carlos Ciller, Sandro I. De Zanet, Sebastian Wolf, Raphael Sznitman
Date: 2016-10-12 00:00:00

Optical Coherence Tomography (OCT) provides a unique ability to image the eye
retina in 3D at micrometer resolution and gives ophthalmologist the ability to
visualize retinal diseases such as Age-Related Macular Degeneration (AMD).
While visual inspection of OCT volumes remains the main method for AMD
identification, doing so is time consuming as each cross-section within the
volume must be inspected individually by the clinician. In much the same way,
acquiring ground truth information for each cross-section is expensive and time
consuming. This fact heavily limits the ability to acquire large amounts of
ground truth, which subsequently impacts the performance of learning-based
methods geared at automatic pathology identification. To avoid this burden, we
propose a novel strategy for automatic analysis of OCT volumes where only
volume labels are needed. That is, we train a classifier in a semi-supervised
manner to conduct this task. Our approach uses a novel Convolutional Neural
Network (CNN) architecture, that only needs volume-level labels to be trained
to automatically asses whether an OCT volume is healthy or contains AMD. Our
architecture involves first learning a cross-section pathology classifier using
pseudo-labels that could be corrupted and then leverage these towards a more
accurate volume-level classification. We then show that our approach provides
excellent performances on a publicly available dataset and outperforms a number
of existing automatic techniques.
Fast Training of Convolutional Neural Networks via Kernel Rescaling
Authors: Pedro Porto Buarque de Gusmão, Gianluca Francini, Skjalg Lepsøy, Enrico Magli
Date: 2016-10-12 00:00:00

Training deep Convolutional Neural Networks (CNN) is a time consuming task
that may take weeks to complete. In this article we propose a novel,
theoretically founded method for reducing CNN training time without incurring
any loss in accuracy. The basic idea is to begin training with a pre-train
network using lower-resolution kernels and input images, and then refine the
results at the full resolution by exploiting the spatial scaling property of
convolutions. We apply our method to the ImageNet winner OverFeat and to the
more recent ResNet architecture and show a reduction in training time of nearly
20% while test set accuracy is preserved in both cases.
Optimizing Memory Efficiency for Deep Convolutional Neural Networks on GPUs
Authors: Chao Li, Yi Yang, Min Feng, Srimat Chakradhar, Huiyang Zhou
Date: 2016-10-12 00:00:00
Tasks: Computational Efficiency, GPU

Leveraging large data sets, deep Convolutional Neural Networks (CNNs) achieve
state-of-the-art recognition accuracy. Due to the substantial compute and
memory operations, however, they require significant execution time. The
massive parallel computing capability of GPUs make them as one of the ideal
platforms to accelerate CNNs and a number of GPU-based CNN libraries have been
developed. While existing works mainly focus on the computational efficiency of
CNNs, the memory efficiency of CNNs have been largely overlooked. Yet CNNs have
intricate data structures and their memory behavior can have significant impact
on the performance. In this work, we study the memory efficiency of various CNN
layers and reveal the performance implication from both data layouts and memory
access patterns. Experiments show the universal effect of our proposed
optimizations on both single layers and various networks, with up to 27.9x for
a single layer and up to 5.6x on the whole networks.
The Virtual Electromagnetic Interaction between Digital Images for Image Matching with Shifting Transformation
Authors: Xiaodong Zhuang, N. E. Mastorakis
Date: 2016-10-12 00:00:00
Tasks: Edge Detection

A novel way of matching two images with shifting transformation is studied.
The approach is based on the presentation of the virtual edge current in
images, and also the study of virtual electromagnetic interaction between two
related images inspired by electromagnetism. The edge current in images is
proposed as a discrete simulation of the physical current, which is based on
the significant edge line extracted by Canny-like edge detection. Then the
virtual interaction of the edge currents between related images is studied by
imitating the electro-magnetic interaction between current-carrying wires.
Based on the virtual interaction force between two related images, a novel
method is presented and applied in image matching for shifting transformation.
The preliminary experimental results indicate the effectiveness of the proposed
method.
A Model of Virtual Carrier Immigration in Digital Images for Region Segmentation
Authors: Xiaodong Zhuang, N. E. Mastorakis
Date: 2016-10-12 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

A novel model for image segmentation is proposed, which is inspired by the
carrier immigration mechanism in physical P-N junction. The carrier diffusing
and drifting are simulated in the proposed model, which imitates the physical
self-balancing mechanism in P-N junction. The effect of virtual carrier
immigration in digital images is analyzed and studied by experiments on test
images and real world images. The sign distribution of net carrier at the
model's balance state is exploited for region segmentation. The experimental
results for both test images and real-world images demonstrate self-adaptive
and meaningful gathering of pixels to suitable regions, which prove the
effectiveness of the proposed method for image region segmentation.
The Analysis of Local Motion and Deformation in Image Sequences Inspired by Physical Electromagnetic Interaction
Authors: Xiaodong Zhuang, N. E. Mastorakis
Date: 2016-10-12 00:00:00

In order to analyze the moving and deforming of the objects in image
sequence, a novel way is presented to analyze the local changes of object edges
between two related images (such as two adjacent frames in a video sequence),
which is inspired by the physical electromagnetic interaction. The changes of
edge between adjacent frames in sequences are analyzed by simulation of virtual
current interaction, which can reflect the change of the object's position or
shape. The virtual current along the main edge line is proposed based on the
significant edge extraction. Then the virtual interaction between the current
elements in the two related images is studied by imitating the interaction
between physical current-carrying wires. The experimental results prove that
the distribution of magnetic forces on the current elements in one image
applied by the other can reflect the local change of edge lines from one image
to the other, which is important in further analysis.
Maximum entropy models for generation of expressive music
Authors: Simon Moulieras, François Pachet
Date: 2016-10-12 00:00:00

In the context of contemporary monophonic music, expression can be seen as
the difference between a musical performance and its symbolic representation,
i.e. a musical score. In this paper, we show how Maximum Entropy (MaxEnt)
models can be used to generate musical expression in order to mimic a human
performance. As a training corpus, we had a professional pianist play about 150
melodies of jazz, pop, and latin jazz. The results show a good predictive
power, validating the choice of our model. Additionally, we set up a listening
test whose results reveal that on average, people significantly prefer the
melodies generated by the MaxEnt model than the ones without any expression, or
with fully random expression. Furthermore, in some cases, MaxEnt melodies are
almost as popular as the human performed ones.
On the Influence of Momentum Acceleration on Online Learning
Authors: Kun Yuan, Bicheng Ying, Ali H. Sayed
Date: 2016-03-14 00:00:00

The article examines in some detail the convergence rate and
mean-square-error performance of momentum stochastic gradient methods in the
constant step-size and slow adaptation regime. The results establish that
momentum methods are equivalent to the standard stochastic gradient method with
a re-scaled (larger) step-size value. The size of the re-scaling is determined
by the value of the momentum parameter. The equivalence result is established
for all time instants and not only in steady-state. The analysis is carried out
for general strongly convex and smooth risk functions, and is not limited to
quadratic risks. One notable conclusion is that the well-known bene ts of
momentum constructions for deterministic optimization problems do not
necessarily carry over to the adaptive online setting when small constant
step-sizes are used to enable continuous adaptation and learn- ing in the
presence of persistent gradient noise. From simulations, the equivalence
between momentum and standard stochastic gradient methods is also observed for
non-differentiable and non-convex problems.
Recurrent Neural Network Grammars
Authors: Chris Dyer, Adhiguna Kuncoro, Miguel Ballesteros, Noah A. Smith
Date: 2016-02-25 00:00:00
Proceeding: NAACL 2016 6
Tasks: Constituency Parsing, Language Modeling, Language Modelling

We introduce recurrent neural network grammars, probabilistic models of
sentences with explicit phrase structure. We explain efficient inference
procedures that allow application to both parsing and language modeling.
Experiments show that they provide better parsing in English than any single
previously published supervised generative model and better language modeling
than state-of-the-art sequential RNNs in English and Chinese.
Towards a Theoretical Analysis of PCA for Heteroscedastic Data
Authors: David Hong, Laura Balzano, Jeffrey A. Fessler
Date: 2016-10-12 00:00:00
Tasks: Anomaly Detection, Dimensionality Reduction

Principal Component Analysis (PCA) is a method for estimating a subspace
given noisy samples. It is useful in a variety of problems ranging from
dimensionality reduction to anomaly detection and the visualization of high
dimensional data. PCA performs well in the presence of moderate noise and even
with missing data, but is also sensitive to outliers. PCA is also known to have
a phase transition when noise is independent and identically distributed;
recovery of the subspace sharply declines at a threshold noise variance.
Effective use of PCA requires a rigorous understanding of these behaviors. This
paper provides a step towards an analysis of PCA for samples with
heteroscedastic noise, that is, samples that have non-uniform noise variances
and so are no longer identically distributed. In particular, we provide a
simple asymptotic prediction of the recovery of a one-dimensional subspace from
noisy heteroscedastic samples. The prediction enables: a) easy and efficient
calculation of the asymptotic performance, and b) qualitative reasoning to
understand how PCA is impacted by heteroscedasticity (such as outliers).
A Paradigm for Situated and Goal-Driven Language Learning
Authors: Jon Gauthier, Igor Mordatch
Date: 2016-10-12 00:00:00

A distinguishing property of human intelligence is the ability to flexibly
use language in order to communicate complex ideas with other humans in a
variety of contexts. Research in natural language dialogue should focus on
designing communicative agents which can integrate themselves into these
contexts and productively collaborate with humans. In this abstract, we propose
a general situated language learning paradigm which is designed to bring about
robust language agents able to cooperate productively with humans.
A Chain-Detection Algorithm for Two-Dimensional Grids
Authors: Paul Bonham, Azlan Iqbal
Date: 2016-10-12 00:00:00
Tasks: Position, valid, Vocal Bursts Valence Prediction

We describe a general method of detecting valid chains or links of pieces on
a two-dimensional grid. Specifically, using the example of the chess variant
known as Switch-Side Chain-Chess (SSCC). Presently, no foolproof method of
detecting such chains in any given chess position is known and existing graph
theory, to our knowledge, is unable to fully address this problem either. We
therefore propose a solution implemented and tested using the C++ programming
language. We have been unable to find an incorrect result and therefore offer
it as the most viable solution thus far to the chain-detection problem in this
chess variant. The algorithm is also scalable, in principle, to areas beyond
two-dimensional grids such as 3D analysis and molecular chemistry.
Transfer from Simulation to Real World through Learning Deep Inverse Dynamics Model
Authors: Paul Christiano, Zain Shah, Igor Mordatch, Jonas Schneider, Trevor Blackwell, Joshua Tobin, Pieter Abbeel, Wojciech Zaremba
Date: 2016-10-11 00:00:00
Tasks: Friction, Reinforcement Learning

Developing control policies in simulation is often more practical and safer
than directly running experiments in the real world. This applies to policies
obtained from planning and optimization, and even more so to policies obtained
from reinforcement learning, which is often very data demanding. However, a
policy that succeeds in simulation often doesn't work when deployed on a real
robot. Nevertheless, often the overall gist of what the policy does in
simulation remains valid in the real world. In this paper we investigate such
settings, where the sequence of states traversed in simulation remains
reasonable for the real world, even if the details of the controls are not, as
could be the case when the key differences lie in detailed friction, contact,
mass and geometry properties. During execution, at each time step our approach
computes what the simulation-based control policy would do, but then, rather
than executing these controls on the real robot, our approach computes what the
simulation expects the resulting next state(s) will be, and then relies on a
learned deep inverse dynamics model to decide which real-world action is most
suitable to achieve those next states. Deep models are only as good as their
training data, and we also propose an approach for data collection to
(incrementally) learn the deep inverse dynamics model. Our experiments shows
our approach compares favorably with various baselines that have been developed
for dealing with simulation to real world model discrepancy, including output
error control and Gaussian dynamics adaptation.
Recursion-Free Online Multiple Incremental/Decremental Analysis Based on Ridge Support Vector Learning
Authors: Bo-Wei Chen
Date: 2016-08-01 00:00:00

This study presents a rapid multiple incremental and decremental mechanism
based on Weight-Error Curves (WECs) for support-vector analysis. Recursion-free
computation is proposed for predicting the Lagrangian multipliers of new
samples. This study examines Ridge Support Vector Models, subsequently devising
a recursion-free function derived from WECs. With the proposed function, all
the new Lagrangian multipliers can be computed at once without using any
gradual step sizes. Moreover, such a function relaxes a constraint, where the
increment of new multiple Lagrangian multipliers should be the same in the
previous work, thereby easily satisfying the requirement of KKT conditions. The
proposed mechanism no longer requires typical bookkeeping strategies, which
compute the step size by checking all the training samples in each incremental
round.
Deep Learning Assessment of Tumor Proliferation in Breast Cancer Histological Images
Authors: Manan Shah, Christopher Rubadue, David Suster, Dayong Wang
Date: 2016-10-11 00:00:00
Tasks: Deep Learning, severity prediction

Current analysis of tumor proliferation, the most salient prognostic
biomarker for invasive breast cancer, is limited to subjective mitosis counting
by pathologists in localized regions of tissue images. This study presents the
first data-driven integrative approach to characterize the severity of tumor
growth and spread on a categorical and molecular level, utilizing multiple
biologically salient deep learning classifiers to develop a comprehensive
prognostic model. Our approach achieves pathologist-level performance on
three-class categorical tumor severity prediction. It additionally pioneers
prediction of molecular expression data from a tissue image, obtaining a
Spearman's rank correlation coefficient of 0.60 with ex vivo mean calculated
RNA expression. Furthermore, our framework is applied to identify over two
hundred unprecedented biomarkers critical to the accurate assessment of tumor
proliferation, validating our proposed integrative pipeline as the first to
holistically and objectively analyze histopathological images.
Restoring STM images via Sparse Coding: noise and artifact removal
Authors: João P. Oliveira, Ana Bragança, José Bioucas-Dias, Mário Figueiredo, Luís Alcácer, Jorge Morgado, Quirina Ferreira
Date: 2016-10-11 00:00:00
Tasks: Denoising, regression

In this article, we present a denoising algorithm to improve the
interpretation and quality of scanning tunneling microscopy (STM) images. Given
the high level of self-similarity of STM images, we propose a denoising
algorithm by reformulating the true estimation problem as a sparse regression,
often termed sparse coding. We introduce modifications to the algorithm to cope
with the existence of artifacts, mainly dropouts, which appear in a structured
way as consecutive line segments on the scanning direction. The resulting
algorithm treats the artifacts as missing data, and the estimated values
outperform those algorithms that substitute the outliers by a local filtering.
We provide code implementations for both Matlab and Gwyddion.
Guaranteed Matrix Completion via Non-convex Factorization
Authors: Ruoyu Sun, Zhi-Quan Luo
Date: 2014-11-28 00:00:00
Tasks: Matrix Completion

Matrix factorization is a popular approach for large-scale matrix completion.
The optimization formulation based on matrix factorization can be solved very
efficiently by standard algorithms in practice. However, due to the
non-convexity caused by the factorization model, there is a limited theoretical
understanding of this formulation. In this paper, we establish a theoretical
guarantee for the factorization formulation to correctly recover the underlying
low-rank matrix. In particular, we show that under similar conditions to those
in previous works, many standard optimization algorithms converge to the global
optima of a factorization formulation, and recover the true low-rank matrix. We
study the local geometry of a properly regularized factorization formulation
and prove that any stationary point in a certain local region is globally
optimal. A major difference of our work from the existing results is that we do
not need resampling in either the algorithm or its analysis. Compared to other
works on nonconvex optimization, one extra difficulty lies in analyzing
nonconvex constrained optimization when the constraint (or the corresponding
regularizer) is not "consistent" with the gradient direction. One technical
contribution is the perturbation analysis for non-symmetric matrix
factorization.
Maximum entropy models capture melodic styles
Authors: Jason Sakellariou, Francesca Tria, Vittorio Loreto, François Pachet
Date: 2016-10-11 00:00:00
Tasks: Data Compression, Music Generation

We introduce a Maximum Entropy model able to capture the statistics of
melodies in music. The model can be used to generate new melodies that emulate
the style of the musical corpus which was used to train it. Instead of using
the $n-$body interactions of $(n-1)-$order Markov models, traditionally used in
automatic music generation, we use a $k-$nearest neighbour model with pairwise
interactions only. In that way, we keep the number of parameters low and avoid
over-fitting problems typical of Markov models. We show that long-range musical
phrases don't need to be explicitly enforced using high-order Markov
interactions, but can instead emerge from multiple, competing, pairwise
interactions. We validate our Maximum Entropy model by contrasting how much the
generated sequences capture the style of the original corpus without
plagiarizing it. To this end we use a data-compression approach to discriminate
the levels of borrowing and innovation featured by the artificial sequences.
The results show that our modelling scheme outperforms both fixed-order and
variable-order Markov models. This shows that, despite being based only on
pairwise interactions, this Maximum Entropy scheme opens the possibility to
generate musically sensible alterations of the original phrases, providing a
way to generate innovation.
Convolutional Neural Networks For Automatic State-Time Feature Extraction in Reinforcement Learning Applied to Residential Load Control
Authors: Bert J. Claessens, Peter Vrancx, Frederik Ruelens
Date: 2016-04-28 00:00:00
Tasks: Reinforcement Learning

Direct load control of a heterogeneous cluster of residential demand
flexibility sources is a high-dimensional control problem with partial
observability. This work proposes a novel approach that uses a convolutional
neural network to extract hidden state-time features to mitigate the curse of
partial observability. More specific, a convolutional neural network is used as
a function approximator to estimate the state-action value function or
Q-function in the supervised learning step of fitted Q-iteration. The approach
is evaluated in a qualitative simulation, comprising a cluster of
thermostatically controlled loads that only share their air temperature, whilst
their envelope temperature remains hidden. The simulation results show that the
presented approach is able to capture the underlying hidden features and
successfully reduce the electricity cost the cluster.
Crossing the Road Without Traffic Lights: An Android-based Safety Device
Authors: Adi Perry, Dor Verbin, Nahum Kiryati
Date: 2016-10-11 00:00:00
Tasks: Optical Flow Estimation

In the absence of pedestrian crossing lights, finding a safe moment to cross
the road is often hazardous and challenging, especially for people with visual
impairments. We present a reliable low-cost solution, an Android device
attached to a traffic sign or lighting pole near the crossing, indicating
whether it is safe to cross the road. The indication can be by sound, display,
vibration, and various communication modalities provided by the Android device.
The integral system camera is aimed at approaching traffic. Optical flow is
computed from the incoming video stream, and projected onto an influx map,
automatically acquired during a brief training period. The crossing safety is
determined based on a 1-dimensional temporal signal derived from the
projection. We implemented the complete system on a Samsung Galaxy K-Zoom
Android smartphone, and obtained real-time operation. The system achieves
promising experimental results, providing pedestrians with sufficiently early
warning of approaching vehicles. The system can serve as a stand-alone safety
device, that can be installed where pedestrian crossing lights are ruled out.
Requiring no dedicated infrastructure, it can be powered by a solar panel and
remotely maintained via the cellular network.