pwc-archive/papers-with-abstracts huggingface dataset:
Exploiting Structure Sparsity for Covariance-based Visual Representation
Authors: Jianjia Zhang, Lei Wang, Luping Zhou, Wanqing Li
Date: 2016-10-27 00:00:00
Tasks: Action Recognition, Temporal Action Localization

The past few years have witnessed increasing research interest on
covariance-based feature representation. A variety of methods have been
proposed to boost its efficacy, with some recent ones resorting to nonlinear
kernel technique. Noting that the essence of this feature representation is to
characterise the underlying structure of visual features, this paper argues
that an equally, if not more, important approach to boosting its efficacy shall
be to improve the quality of this characterisation. Following this idea, we
propose to exploit the structure sparsity of visual features in skeletal human
action recognition, and compute sparse inverse covariance estimate (SICE) as
feature representation. We discuss the advantage of this new representation on
dealing with small sample, high dimensionality, and modelling capability.
Furthermore, utilising the monotonicity property of SICE, we efficiently
generate a hierarchy of SICE matrices to characterise the structure of visual
features at different sparsity levels, and two discriminative learning
algorithms are then developed to adaptively integrate them to perform
recognition. As demonstrated by extensive experiments, the proposed
representation leads to significantly improved recognition performance over the
state-of-the-art comparable methods. In particular, as a method fully based on
linear technique, it is comparable or even better than those employing
nonlinear kernel technique. This result well demonstrates the value of
exploiting structure sparsity for covariance-based feature representation.
Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model
Authors: Sheng Wang, Siqi Sun, Zhen Li, Renyu Zhang, Jinbo Xu
Date: 2016-09-02 00:00:00
Tasks: Prediction, Protein Folding

Recently exciting progress has been made on protein contact prediction, but
the predicted contacts for proteins without many sequence homologs is still of
low quality and not very useful for de novo structure prediction. This paper
presents a new deep learning method that predicts contacts by integrating both
evolutionary coupling (EC) and sequence conservation information through an
ultra-deep neural network formed by two deep residual networks. This deep
neural network allows us to model very complex sequence-contact relationship as
well as long-range inter-contact correlation. Our method greatly outperforms
existing contact prediction methods and leads to much more accurate
contact-assisted protein folding. Tested on three datasets of 579 proteins, the
average top L long-range prediction accuracy obtained our method, the
representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21
and 0.30, respectively; the average top L/10 long-range accuracy of our method,
CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding
using our predicted contacts as restraints can yield correct folds (i.e.,
TMscore>0.6) for 203 test proteins, while that using MetaPSICOV- and
CCMpred-predicted contacts can do so for only 79 and 62 proteins, respectively.
Further, our contact-assisted models have much better quality than
template-based models. Using our predicted contacts as restraints, we can (ab
initio) fold 208 of the 398 membrane proteins with TMscore>0.5. By contrast,
when the training proteins of our method are used as templates, homology
modeling can only do so for 10 of them. One interesting finding is that even if
we do not train our prediction models with any membrane proteins, our method
works very well on membrane protein prediction. Finally, in recent blind CAMEO
benchmark our method successfully folded 5 test proteins with a novel fold.
Invariant Representations for Noisy Speech Recognition
Authors: Dmitriy Serdyuk, Kartik Audhkhasi, Philémon Brakel, Bhuvana Ramabhadran, Samuel Thomas, Yoshua Bengio
Date: 2016-11-27 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Domain Adaptation, Image Generation, Noisy Speech Recognition, speech-recognition, Speech Recognition

Modern automatic speech recognition (ASR) systems need to be robust under
acoustic variability arising from environmental, speaker, channel, and
recording conditions. Ensuring such robustness to variability is a challenge in
modern day neural network-based ASR systems, especially when all types of
variability are not seen during training. We attempt to address this problem by
encouraging the neural network acoustic model to learn invariant feature
representations. We use ideas from recent research on image generation using
Generative Adversarial Networks and domain adaptation ideas extending
adversarial gradient-based training. A recent work from Ganin et al. proposes
to use adversarial training for image domain adaptation by using an
intermediate representation from the main target classification network to
deteriorate the domain classifier performance through a separate neural
network. Our work focuses on investigating neural architectures which produce
representations invariant to noise conditions for ASR. We evaluate the proposed
architecture on the Aurora-4 task, a popular benchmark for noise robust ASR. We
show that our method generalizes better than the standard multi-condition
training especially when only a few noise categories are seen during training.
"Model and Run" Constraint Networks with a MILP Engine
Authors: Thierry Petit
Date: 2016-11-27 00:00:00

Constraint Programming (CP) users need significant expertise in order to
model their problems appropriately, notably to select propagators and search
strategies. This puts the brakes on a broader uptake of CP. In this paper, we
introduce MICE, a complete Java CP modeler that can use any Mixed Integer
Linear Programming (MILP) solver as a solution technique. Our aim is to provide
an alternative tool for democratizing the "CP-style" modeling thanks to its
simplicity of use, with reasonable solving capabilities. Our contributions
include new decompositions of (reified) constraints and constraints on
numerical variables.
Should I use TensorFlow
Authors: Martin Schrimpf
Date: 2016-11-27 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

Google's Machine Learning framework TensorFlow was open-sourced in November
2015 [1] and has since built a growing community around it. TensorFlow is
supposed to be flexible for research purposes while also allowing its models to
be deployed productively. This work is aimed towards people with experience in
Machine Learning considering whether they should use TensorFlow in their
environment. Several aspects of the framework important for such a decision are
examined, such as the heterogenity, extensibility and its computation graph. A
pure Python implementation of linear classification is compared with an
implementation utilizing TensorFlow. I also contrast TensorFlow to other
popular frameworks with respect to modeling capability, deployment and
performance and give a brief description of the current adaption of the
framework.
Uniform Information Segmentation
Authors: Radhakrishna Achanta, Pablo Márquez-Neila, Pascal Fua, Sabine Süsstrunk
Date: 2016-11-27 00:00:00
Tasks: Segmentation, Superpixels

Size uniformity is one of the main criteria of superpixel methods. But size
uniformity rarely conforms to the varying content of an image. The chosen size
of the superpixels therefore represents a compromise - how to obtain the fewest
superpixels without losing too much important detail. We propose that a more
appropriate criterion for creating image segments is information uniformity. We
introduce a novel method for segmenting an image based on this criterion. Since
information is a natural way of measuring image complexity, our proposed
algorithm leads to image segments that are smaller and denser in areas of high
complexity and larger in homogeneous regions, thus simplifying the image while
preserving its details. Our algorithm is simple and requires just one input
parameter - a threshold on the information content. On segmentation comparison
benchmarks it proves to be superior to the state-of-the-art. In addition, our
method is computationally very efficient, approaching real-time performance,
and is easily extensible to three-dimensional image stacks and video volumes.
Unified Scalable Equivalent Formulations for Schatten Quasi-Norms
Authors: Fanhua Shang, Yuanyuan Liu, James Cheng
Date: 2016-06-02 00:00:00

The Schatten quasi-norm can be used to bridge the gap between the nuclear
norm and rank function, and is the tighter approximation to matrix rank.
However, most existing Schatten quasi-norm minimization (SQNM) algorithms, as
well as for nuclear norm minimization, are too slow or even impractical for
large-scale problems, due to the SVD or EVD of the whole matrix in each
iteration. In this paper, we rigorously prove that for any p, p1, p2>0
satisfying 1/p=1/p1+1/p2, the Schatten-p quasi-norm of any matrix is equivalent
to minimizing the product of the Schatten-p1 norm (or quasi-norm) and
Schatten-p2 norm (or quasi-norm) of its two factor matrices. Then we present
and prove the equivalence relationship between the product formula of the
Schatten quasi-norm and its weighted sum formula for the two cases of p1 and
p2: p1=p2 and p1\neq p2. In particular, when p>1/2, there is an equivalence
between the Schatten-p quasi-norm of any matrix and the Schatten-2p norms of
its two factor matrices, where the widely used equivalent formulation of the
nuclear norm can be viewed as a special case. That is, various SQNM problems
with p>1/2 can be transformed into the one only involving smooth, convex norms
of two factor matrices, which can lead to simpler and more efficient algorithms
than conventional methods.
  We further extend the theoretical results of two factor matrices to the cases
of three and more factor matrices, from which we can see that for any 0<p<1,
the Schatten-p quasi-norm of any matrix is the minimization of the mean of the
Schatten-(p3+1)p norms of all factor matrices, where p3 denotes the largest
integer not exceeding 1/p. In other words, for any 0<p<1, the SQNM problem can
be transformed into an optimization problem only involving the smooth, convex
norms of multiple factor matrices.
Layered Adaptive Importance Sampling
Authors: L. Martino, V. Elvira, D. Luengo, J. Corander
Date: 2015-05-18 00:00:00

Monte Carlo methods represent the "de facto" standard for approximating
complicated integrals involving multidimensional target distributions. In order
to generate random realizations from the target distribution, Monte Carlo
techniques use simpler proposal probability densities to draw candidate
samples. The performance of any such method is strictly related to the
specification of the proposal distribution, such that unfortunate choices
easily wreak havoc on the resulting estimators. In this work, we introduce a
layered (i.e., hierarchical) procedure to generate samples employed within a
Monte Carlo scheme. This approach ensures that an appropriate equivalent
proposal density is always obtained automatically (thus eliminating the risk of
a catastrophic performance), although at the expense of a moderate increase in
the complexity. Furthermore, we provide a general unified importance sampling
(IS) framework, where multiple proposal densities are employed and several IS
schemes are introduced by applying the so-called deterministic mixture
approach. Finally, given these schemes, we also propose a novel class of
adaptive importance samplers using a population of proposals, where the
adaptation is driven by independent parallel or interacting Markov Chain Monte
Carlo (MCMC) chains. The resulting algorithms efficiently combine the benefits
of both IS and MCMC methods.
A neuro-mathematical model for geometrical optical illusions
Authors: B. Franceschiello, A. Sarti, G. Citti
Date: 2016-11-27 00:00:00
Tasks: model, Object

Geometrical optical illusions have been object of many studies due to the
possibility they offer to understand the behaviour of low-level visual
processing. They consist in situations in which the perceived geometrical
properties of an object differ from those of the object in the visual stimulus.
Starting from the geometrical model introduced by Citti and Sarti in [3], we
provide a mathematical model and a computational algorithm which allows to
interpret these phenomena and to qualitatively reproduce the perceived
misperception.
Did Evolution get it right? An evaluation of Near-Infrared imaging in semantic scene segmentation using deep learning
Authors: J. Rafid Siddiqui
Date: 2016-11-27 00:00:00
Tasks: Scene Segmentation, Segmentation, Semantic Segmentation

Animals have evolved to restrict their sensing capabilities to certain region
of electromagnetic spectrum. This is surprisingly a very narrow band on a vast
scale which makes one think if there is a systematic bias underlying such
selective filtration. The situation becomes even more intriguing when we find a
sharp cutoff point at Near-infrared point whereby almost all animal vision
systems seem to have a lower bound. This brings us to an interesting question:
did evolution "intentionally" performed such a restriction in order to evolve
higher visual cognition? In this work this question is addressed by
experimenting with Near-infrared images for their potential applicability in
higher visual processing such as semantic segmentation. A modified version of
Fully Convolutional Networks are trained on NIR images and RGB images
respectively and compared for their respective effectiveness in the wake of
semantic segmentation. The results from the experiments show that visible part
of the spectrum alone is sufficient for the robust semantic segmentation of the
indoor as well as outdoor scenes.
Semi Supervised Preposition-Sense Disambiguation using Multilingual Data
Authors: Hila Gonen, Yoav Goldberg
Date: 2016-11-27 00:00:00
Proceeding: COLING 2016 12
Tasks: General Classification, Sentence, Translation

Prepositions are very common and very ambiguous, and understanding their
sense is critical for understanding the meaning of the sentence. Supervised
corpora for the preposition-sense disambiguation task are small, suggesting a
semi-supervised approach to the task. We show that signals from unannotated
multilingual data can be used to improve supervised preposition-sense
disambiguation. Our approach pre-trains an LSTM encoder for predicting the
translation of a preposition, and then incorporates the pre-trained encoder as
a component in a supervised classification system, and fine-tunes it for the
task. The multilingual signals consistently improve results on two
preposition-sense datasets.
Kernel classification of connectomes based on earth mover's distance between graph spectra
Authors: Yulia Dodonova, Mikhail Belyaev, Anna Tkachev, Dmitry Petrov, Leonid Zhukov
Date: 2016-11-27 00:00:00
Tasks: Classification, General Classification, Graph Embedding

In this paper, we tackle a problem of predicting phenotypes from structural
connectomes. We propose that normalized Laplacian spectra can capture
structural properties of brain networks, and hence graph spectral distributions
are useful for a task of connectome-based classification. We introduce a kernel
that is based on earth mover's distance (EMD) between spectral distributions of
brain networks. We access performance of an SVM classifier with the proposed
kernel for a task of classification of autism spectrum disorder versus typical
development based on a publicly available dataset. Classification quality (area
under the ROC-curve) obtained with the EMD-based kernel on spectral
distributions is 0.71, which is higher than that based on simpler graph
embedding methods.
The polysemy of the words that children learn over time
Authors: Bernardino Casas, Neus Català, Ramon Ferrer-i-Cancho, Antoni Hernández-Fernández, Jaume Baixeries
Date: 2016-11-27 00:00:00

Here we study polysemy as a potential learning bias in vocabulary learning in
children. Words of low polysemy could be preferred as they reduce the
disambiguation effort for the listener. However, such preference could be a
side-effect of another bias: the preference of children for nouns in
combination with the lower polysemy of nouns with respect to other
part-of-speech categories. Our results show that mean polysemy in children
increases over time in two phases, i.e. a fast growth till the 31st month
followed by a slower tendency towards adult speech. In contrast, this evolution
is not found in adults interacting with children. This suggests that children
have a preference for non-polysemous words in their early stages of vocabulary
acquisition. Interestingly, the evolutionary pattern described above weakens
when controlling for syntactic category (noun, verb, adjective or adverb) but
it does not disappear completely, suggesting that it could result from
acombination of a standalone bias for low polysemy and a preference for nouns.
Deep Deformable Registration: Enhancing Accuracy by Fully Convolutional Neural Net
Authors: Sayan Ghosal, Nilanjan Ray
Date: 2016-11-27 00:00:00
Tasks: Medical Image Analysis

Deformable registration is ubiquitous in medical image analysis. Many
deformable registration methods minimize sum of squared difference (SSD) as the
registration cost with respect to deformable model parameters. In this work, we
construct a tight upper bound of the SSD registration cost by using a fully
convolutional neural network (FCNN) in the registration pipeline. The upper
bound SSD (UB-SSD) enhances the original deformable model parameter space by
adding a heatmap output from FCNN. Next, we minimize this UB-SSD by adjusting
both the parameters of the FCNN and the parameters of the deformable model in
coordinate descent. Our coordinate descent framework is end-to-end and can work
with any deformable registration method that uses SSD. We demonstrate
experimentally that our method enhances the accuracy of deformable registration
algorithms significantly on two publicly available 3D brain MRI data sets.
Learning without recall in directed circles and rooted trees
Authors: M. Amin Rahimian, Ali Jadbabaie
Date: 2016-11-27 00:00:00

This work investigates the case of a network of agents that attempt to learn
some unknown state of the world amongst the finitely many possibilities. At
each time step, agents all receive random, independently distributed private
signals whose distributions are dependent on the unknown state of the world.
However, it may be the case that some or any of the agents cannot distinguish
between two or more of the possible states based only on their private
observations, as when several states result in the same distribution of the
private signals. In our model, the agents form some initial belief (probability
distribution) about the unknown state and then refine their beliefs in
accordance with their private observations, as well as the beliefs of their
neighbors. An agent learns the unknown state when her belief converges to a
point mass that is concentrated at the true state. A rational agent would use
the Bayes' rule to incorporate her neighbors' beliefs and own private signals
over time. While such repeated applications of the Bayes' rule in networks can
become computationally intractable, in this paper, we show that in the
canonical cases of directed star, circle or path networks and their
combinations, one can derive a class of memoryless update rules that replicate
that of a single Bayesian agent but replace the self beliefs with the beliefs
of the neighbors. This way, one can realize an exponentially fast rate of
learning similar to the case of Bayesian (fully rational) agents. The proposed
rules are a special case of the Learning without Recall.
Handwriting Profiling using Generative Adversarial Networks
Authors: Arna Ghosh, Biswarup Bhattacharya, Somnath Basu Roy Chowdhury
Date: 2016-11-27 00:00:00
Tasks: Reinforcement Learning

Handwriting is a skill learned by humans from a very early age. The ability
to develop one's own unique handwriting as well as mimic another person's
handwriting is a task learned by the brain with practice. This paper deals with
this very problem where an intelligent system tries to learn the handwriting of
an entity using Generative Adversarial Networks (GANs). We propose a modified
architecture of DCGAN (Radford, Metz, and Chintala 2015) to achieve this. We
also discuss about applying reinforcement learning techniques to achieve faster
learning. Our algorithm hopes to give new insights in this area and its uses
include identification of forged documents, signature verification, computer
generated art, digitization of documents among others. Our early implementation
of the algorithm illustrates a good performance with MNIST datasets.
SAD-GAN: Synthetic Autonomous Driving using Generative Adversarial Networks
Authors: Arna Ghosh, Biswarup Bhattacharya, Somnath Basu Roy Chowdhury
Date: 2016-11-27 00:00:00
Tasks: Autonomous Driving

Autonomous driving is one of the most recent topics of interest which is
aimed at replicating human driving behavior keeping in mind the safety issues.
We approach the problem of learning synthetic driving using generative neural
networks. The main idea is to make a controller trainer network using images
plus key press data to mimic human learning. We used the architecture of a
stable GAN to make predictions between driving scenes using key presses. We
train our model on one video game (Road Rash) and tested the accuracy and
compared it by running the model on other maps in Road Rash to determine the
extent of learning.
Real-Time Video Highlights for Yahoo Esports
Authors: Yale Song
Date: 2016-11-27 00:00:00
Tasks: CPU, Dota 2, Video Understanding

Esports has gained global popularity in recent years and several companies
have started offering live streaming videos of esports games and events. This
creates opportunities to develop large scale video understanding systems for
new product features and services. We present a technique for detecting
highlights from live streaming videos of esports game matches. Most video games
use pronounced visual effects to emphasize highlight moments; we use CNNs to
learn convolution filters of those visual effects for detecting highlights. We
propose a cascaded prediction approach that allows us to deal with several
challenges arise in a production environment. We demonstrate our technique on
our new dataset of three popular game titles, Heroes of the Storm, League of
Legends, and Dota 2. Our technique achieves 18 FPS on a single CPU with an
average precision of up to 83.18%. Part of our technique is currently deployed
in production on Yahoo Esports.