pwc-archive/papers-with-abstracts huggingface dataset:
A Framework to Adjust Dependency Measure Estimates for Chance
Authors: Simone Romano, Nguyen Xuan Vinh, James Bailey, Karin Verspoor
Date: 2015-10-27 00:00:00

Estimating the strength of dependency between two variables is fundamental
for exploratory analysis and many other applications in data mining. For
example: non-linear dependencies between two continuous variables can be
explored with the Maximal Information Coefficient (MIC); and categorical
variables that are dependent to the target class are selected using Gini gain
in random forests. Nonetheless, because dependency measures are estimated on
finite samples, the interpretability of their quantification and the accuracy
when ranking dependencies become challenging. Dependency estimates are not
equal to 0 when variables are independent, cannot be compared if computed on
different sample size, and they are inflated by chance on variables with more
categories. In this paper, we propose a framework to adjust dependency measure
estimates on finite samples. Our adjustments, which are simple and applicable
to any dependency measure, are helpful in improving interpretability when
quantifying dependency and in improving accuracy on the task of ranking
dependencies. In particular, we demonstrate that our approach enhances the
interpretability of MIC when used as a proxy for the amount of noise between
variables, and to gain accuracy when ranking variables during the splitting
procedure in random forests.
Unsupervised Learning of Visual Structure using Predictive Generative Networks
Authors: William Lotter, Gabriel Kreiman, David Cox
Date: 2015-11-19 00:00:00

The ability to predict future states of the environment is a central pillar
of intelligence. At its core, effective prediction requires an internal model
of the world and an understanding of the rules by which the world changes.
Here, we explore the internal models developed by deep neural networks trained
using a loss based on predicting future frames in synthetic video sequences,
using a CNN-LSTM-deCNN framework. We first show that this architecture can
achieve excellent performance in visual sequence prediction tasks, including
state-of-the-art performance in a standard 'bouncing balls' dataset (Sutskever
et al., 2009). Using a weighted mean-squared error and adversarial loss
(Goodfellow et al., 2014), the same architecture successfully extrapolates
out-of-the-plane rotations of computer-generated faces. Furthermore, despite
being trained end-to-end to predict only pixel-level information, our
Predictive Generative Networks learn a representation of the latent structure
of the underlying three-dimensional objects themselves. Importantly, we find
that this representation is naturally tolerant to object transformations, and
generalizes well to new tasks, such as classification of static images. Similar
models trained solely with a reconstruction loss fail to generalize as
effectively. We argue that prediction can serve as a powerful unsupervised loss
for learning rich internal representations of high-level object features.
A Closed-Form Solution to Tensor Voting: Theory and Applications
Authors: Tai-Pang Wu, Sai-Kit Yeung, Jiaya Jia, Chi-Keung Tang, Gerard Medioni
Date: 2016-01-19 00:00:00
Tasks: Form, parameter estimation, Stereo Matching, Stereo Matching Hand

We prove a closed-form solution to tensor voting (CFTV): given a point set in
any dimensions, our closed-form solution provides an exact, continuous and
efficient algorithm for computing a structure-aware tensor that simultaneously
achieves salient structure detection and outlier attenuation. Using CFTV, we
prove the convergence of tensor voting on a Markov random field (MRF), thus
termed as MRFTV, where the structure-aware tensor at each input site reaches a
stationary state upon convergence in structure propagation. We then embed
structure-aware tensor into expectation maximization (EM) for optimizing a
single linear structure to achieve efficient and robust parameter estimation.
Specifically, our EMTV algorithm optimizes both the tensor and fitting
parameters and does not require random sampling consensus typically used in
existing robust statistical techniques. We performed quantitative evaluation on
its accuracy and robustness, showing that EMTV performs better than the
original TV and other state-of-the-art techniques in fundamental matrix
estimation for multiview stereo matching. The extensions of CFTV and EMTV for
extracting multiple and nonlinear structures are underway. An addendum is
included in this arXiv version.
Causality, Information and Biological Computation: An algorithmic software approach to life, disease and the immune system
Authors: Hector Zenil, Angelika Schmidt, Jesper Tegnér
Date: 2015-08-24 00:00:00

Biology has taken strong steps towards becoming a computer science aiming at
reprogramming nature after the realisation that nature herself has reprogrammed
organisms by harnessing the power of natural selection and the digital
prescriptive nature of replicating DNA. Here we further unpack ideas related to
computability, algorithmic information theory and software engineering, in the
context of the extent to which biology can be (re)programmed, and with how we
may go about doing so in a more systematic way with all the tools and concepts
offered by theoretical computer science in a translation exercise from
computing to molecular biology and back. These concepts provide a means to a
hierarchical organization thereby blurring previously clear-cut lines between
concepts like matter and life, or between tumour types that are otherwise taken
as different and may not have however a different cause. This does not diminish
the properties of life or make its components and functions less interesting.
On the contrary, this approach makes for a more encompassing and integrated
view of nature, one that subsumes observer and observed within the same system,
and can generate new perspectives and tools with which to view complex diseases
like cancer, approaching them afresh from a software-engineering viewpoint that
casts evolution in the role of programmer, cells as computing machines, DNA and
genes as instructions and computer programs, viruses as hacking devices, the
immune system as a software debugging tool, and diseases as an
information-theoretic battlefield where all these forces deploy. We show how
information theory and algorithmic programming may explain fundamental
mechanisms of life and death.
Habits vs Environment: What really causes asthma?
Authors: Mengfan Tang, Pranav Agrawal, Ramesh Jain
Date: 2016-01-20 00:00:00

Despite considerable number of studies on risk factors for asthma onset, very
little is known about their relative importance. To have a full picture of
these factors, both categories, personal and environmental data, have to be
taken into account simultaneously, which is missing in previous studies. We
propose a framework to rank the risk factors from heterogeneous data sources of
the two categories. Established on top of EventShop and Personal EventShop,
this framework extracts about 400 features, and analyzes them by employing a
gradient boosting tree. The features come from sources including personal
profile and life-event data, and environmental data on air pollution, weather
and PM2.5 emission sources. The top ranked risk factors derived from our
framework agree well with the general medical consensus. Thus, our framework is
a reliable approach, and the discovered rankings of relative importance of risk
factors can provide insights for the prevention of asthma.
Origami: A 803 GOp/s/W Convolutional Network Accelerator
Authors: Lukas Cavigelli, Luca Benini
Date: 2015-12-14 00:00:00
Tasks: Action Recognition, Object Recognition, Optical Flow Estimation, Semantic Segmentation, Temporal Action Localization

An ever increasing number of computer vision and image/video processing
challenges are being approached using deep convolutional neural networks,
obtaining state-of-the-art results in object recognition and detection,
semantic segmentation, action recognition, optical flow and superresolution.
Hardware acceleration of these algorithms is essential to adopt these
improvements in embedded and mobile computer vision systems. We present a new
architecture, design and implementation as well as the first reported silicon
measurements of such an accelerator, outperforming previous work in terms of
power-, area- and I/O-efficiency. The manufactured device provides up to 196
GOp/s on 3.09 mm^2 of silicon in UMC 65nm technology and can achieve a power
efficiency of 803 GOp/s/W. The massively reduced bandwidth requirements make it
the first architecture scalable to TOp/s performance.
A Theory of Local Matching: SIFT and Beyond
Authors: Hossein Mobahi, Stefano Soatto
Date: 2016-01-19 00:00:00

Why has SIFT been so successful? Why its extension, DSP-SIFT, can further
improve SIFT? Is there a theory that can explain both? How can such theory
benefit real applications? Can it suggest new algorithms with reduced
computational complexity or new descriptors with better accuracy for matching?
We construct a general theory of local descriptors for visual matching. Our
theory relies on concepts in energy minimization and heat diffusion. We show
that SIFT and DSP-SIFT approximate the solution the theory suggests. In
particular, DSP-SIFT gives a better approximation to the theoretical solution;
justifying why DSP-SIFT outperforms SIFT. Using the developed theory, we derive
new descriptors that have fewer parameters and are potentially better in
handling affine deformations.
Learning Visual Predictive Models of Physics for Playing Billiards
Authors: Katerina Fragkiadaki, Pulkit Agrawal, Sergey Levine, Jitendra Malik
Date: 2015-11-23 00:00:00

The ability to plan and execute goal specific actions in varied, unexpected
settings is a central requirement of intelligent agents. In this paper, we
explore how an agent can be equipped with an internal model of the dynamics of
the external world, and how it can use this model to plan novel actions by
running multiple internal simulations ("visual imagination"). Our models
directly process raw visual input, and use a novel object-centric prediction
formulation based on visual glimpses centered on objects (fixations) to enforce
translational invariance of the learned physical laws. The agent gathers
training data through random interaction with a collection of different
environments, and the resulting model can then be used to plan goal-directed
actions in novel environments that the agent has not seen before. We
demonstrate that our agent can accurately plan actions for playing a simulated
billiards game, which requires pushing a ball into a target position or into
collision with another ball.
Data-Dependent Path Normalization in Neural Networks
Authors: Behnam Neyshabur, Ryota Tomioka, Ruslan Salakhutdinov, Nathan Srebro
Date: 2015-11-20 00:00:00

We propose a unified framework for neural net normalization, regularization
and optimization, which includes Path-SGD and Batch-Normalization and
interpolates between them across two different dimensions. Through this
framework we investigate issue of invariance of the optimization, data
dependence and the connection with natural gradients.
Task Loss Estimation for Sequence Prediction
Authors: Dzmitry Bahdanau, Dmitriy Serdyuk, Philémon Brakel, Nan Rosemary Ke, Jan Chorowski, Aaron Courville, Yoshua Bengio
Date: 2015-11-19 00:00:00
Tasks: Decoder, Language Modeling, Language Modelling, Prediction, speech-recognition, Speech Recognition

Often, the performance on a supervised machine learning task is evaluated
with a emph{task loss} function that cannot be optimized directly. Examples of
such loss functions include the classification error, the edit distance and the
BLEU score. A common workaround for this problem is to instead optimize a
emph{surrogate loss} function, such as for instance cross-entropy or hinge
loss. In order for this remedy to be effective, it is important to ensure that
minimization of the surrogate loss results in minimization of the task loss, a
condition that we call emph{consistency with the task loss}. In this work, we
propose another method for deriving differentiable surrogate losses that
provably meet this requirement. We focus on the broad class of models that
define a score for every input-output pair. Our idea is that this score can be
interpreted as an estimate of the task loss, and that the estimation error may
be used as a consistent surrogate loss. A distinct feature of such an approach
is that it defines the desirable value of the score for every input-output
pair. We use this property to design specialized surrogate losses for
Encoder-Decoder models often used for sequence prediction tasks. In our
experiment, we benchmark on the task of speech recognition. Using a new
surrogate loss instead of cross-entropy to train an Encoder-Decoder speech
recognizer brings a significant ~13% relative improvement in terms of Character
Error Rate (CER) in the case when no extra corpora are used for language
modeling.
PN-Net: Conjoined Triple Deep Network for Learning Local Image Descriptors
Authors: Vassileios Balntas, Edward Johns, Lilian Tang, Krystian Mikolajczyk
Date: 2016-01-19 00:00:00
Tasks: GPU

In this paper we propose a new approach for learning local descriptors for
matching image patches. It has recently been demonstrated that descriptors
based on convolutional neural networks (CNN) can significantly improve the
matching performance. Unfortunately their computational complexity is
prohibitive for any practical application. We address this problem and propose
a CNN based descriptor with improved matching performance, significantly
reduced training and execution time, as well as low dimensionality.
  We propose to train the network with triplets of patches that include a
positive and negative pairs. To that end we introduce a new loss function that
exploits the relations within the triplets. We compare our approach to recently
introduced MatchNet and DeepCompare and demonstrate the advantages of our
descriptor in terms of performance, memory footprint and speed i.e. when run in
GPU, the extraction time of our 128 dimensional feature is comparable to the
fastest available binary descriptors such as BRIEF and ORB.
Foveation-based Mechanisms Alleviate Adversarial Examples
Authors: Yan Luo, Xavier Boix, Gemma Roig, Tomaso Poggio, Qi Zhao
Date: 2015-11-19 00:00:00
Tasks: Foveation, Translation

We show that adversarial examples, i.e., the visually imperceptible
perturbations that result in Convolutional Neural Networks (CNNs) fail, can be
alleviated with a mechanism based on foveations---applying the CNN in different
image regions. To see this, first, we report results in ImageNet that lead to a
revision of the hypothesis that adversarial perturbations are a consequence of
CNNs acting as a linear classifier: CNNs act locally linearly to changes in the
image regions with objects recognized by the CNN, and in other regions the CNN
may act non-linearly. Then, we corroborate that when the neural responses are
linear, applying the foveation mechanism to the adversarial example tends to
significantly reduce the effect of the perturbation. This is because,
hypothetically, the CNNs for ImageNet are robust to changes of scale and
translation of the object produced by the foveation, but this property does not
generalize to transformations of the perturbation. As a result, the accuracy
after a foveation is almost the same as the accuracy of the CNN without the
adversarial perturbation, even if the adversarial perturbation is calculated
taking into account a foveation.
Convexified Modularity Maximization for Degree-corrected Stochastic Block Models
Authors: Yudong Chen, Xiao-Dong Li, Jiaming Xu
Date: 2015-12-28 00:00:00
Tasks: Clustering, Community Detection, Stochastic Block Model

The stochastic block model (SBM) is a popular framework for studying
community detection in networks. This model is limited by the assumption that
all nodes in the same community are statistically equivalent and have equal
expected degrees. The degree-corrected stochastic block model (DCSBM) is a
natural extension of SBM that allows for degree heterogeneity within
communities. This paper proposes a convexified modularity maximization approach
for estimating the hidden communities under DCSBM. Our approach is based on a
convex programming relaxation of the classical (generalized) modularity
maximization formulation, followed by a novel doubly-weighted $ \ell_1 $-norm $
k $-median procedure. We establish non-asymptotic theoretical guarantees for
both approximate clustering and perfect clustering. Our approximate clustering
results are insensitive to the minimum degree, and hold even in sparse regime
with bounded average degrees. In the special case of SBM, these theoretical
results match the best-known performance guarantees of computationally feasible
algorithms. Numerically, we provide an efficient implementation of our
algorithm, which is applied to both synthetic and real-world networks.
Experiment results show that our method enjoys competitive performance compared
to the state of the art in the literature.
Online Optimization in Dynamic Environments
Authors: Eric C. Hall, Rebecca M. Willett
Date: 2013-07-23 00:00:00
Tasks: compressed sensing, Point Processes, Texture Classification

High-velocity streams of high-dimensional data pose significant "big data"
analysis challenges across a range of applications and settings. Online
learning and online convex programming play a significant role in the rapid
recovery of important or anomalous information from these large datastreams.
While recent advances in online learning have led to novel and rapidly
converging algorithms, these methods are unable to adapt to nonstationary
environments arising in real-world problems. This paper describes a dynamic
mirror descent framework which addresses this challenge, yielding low
theoretical regret bounds and accurate, adaptive, and computationally efficient
algorithms which are applicable to broad classes of problems. The methods are
capable of learning and adapting to an underlying and possibly time-varying
dynamical model. Empirical results in the context of dynamic texture analysis,
solar flare detection, sequential compressed sensing of a dynamic scene,
traffic surveillance,tracking self-exciting point processes and network
behavior in the Enron email corpus support the core theoretical findings.
Using Deep Learning for Detecting Spoofing Attacks on Speech Signals
Authors: Alan Godoy, Flávio Simões, José Augusto Stuchi, Marcus de Assis Angeloni, Mário Uliani, Ricardo Violato
Date: 2015-08-07 00:00:00
Tasks: Deep Learning, Speaker Verification

It is well known that speaker verification systems are subject to spoofing
attacks. The Automatic Speaker Verification Spoofing and Countermeasures
Challenge -- ASVSpoof2015 -- provides a standard spoofing database, containing
attacks based on synthetic speech, along with a protocol for experiments. This
paper describes CPqD's systems submitted to the ASVSpoof2015 Challenge, based
on deep neural networks, working both as a classifier and as a feature
extraction module for a GMM and a SVM classifier. Results show the validity of
this approach, achieving less than 0.5\% EER for known attacks.
3D Gaze Estimation from 2D Pupil Positions on Monocular Head-Mounted Eye Trackers
Authors: Mohsen Mansouryar, Julian Steil, Yusuke Sugano, Andreas Bulling
Date: 2016-01-11 00:00:00
Tasks: Gaze Estimation

3D gaze information is important for scene-centric attention analysis but
accurate estimation and analysis of 3D gaze in real-world environments remains
challenging. We present a novel 3D gaze estimation method for monocular
head-mounted eye trackers. In contrast to previous work, our method does not
aim to infer 3D eyeball poses but directly maps 2D pupil positions to 3D gaze
directions in scene camera coordinate space. We first provide a detailed
discussion of the 3D gaze estimation task and summarize different methods,
including our own. We then evaluate the performance of different 3D gaze
estimation approaches using both simulated and real data. Through experimental
validation, we demonstrate the effectiveness of our method in reducing parallax
error, and we identify research challenges for the design of 3D calibration
procedures.
Understanding Deep Convolutional Networks
Authors: Stéphane Mallat
Date: 2016-01-19 00:00:00

Deep convolutional networks provide state of the art classifications and
regressions results over many high-dimensional problems. We review their
architecture, which scatters data with a cascade of linear filter weights and
non-linearities. A mathematical framework is introduced to analyze their
properties. Computations of invariants involve multiscale contractions, the
linearization of hierarchical symmetries, and sparse separations. Applications
are discussed.
Time Series Classification using the Hidden-Unit Logistic Model
Authors: Wenjie Pei, Hamdi Dibeklioğlu, David M. J. Tax, Laurens van der Maaten
Date: 2015-06-16 00:00:00
Tasks: Action Recognition, Action Unit Detection, Classification, Facial Action Unit Detection, General Classification, speech-recognition, Speech Recognition, Temporal Action Localization, Time Series, Time Series Analysis, Time Series Classification

We present a new model for time series classification, called the hidden-unit
logistic model, that uses binary stochastic hidden units to model latent
structure in the data. The hidden units are connected in a chain structure that
models temporal dependencies in the data. Compared to the prior models for time
series classification such as the hidden conditional random field, our model
can model very complex decision boundaries because the number of latent states
grows exponentially with the number of hidden units. We demonstrate the strong
performance of our model in experiments on a variety of (computer vision)
tasks, including handwritten character recognition, speech recognition, facial
expression, and action recognition. We also present a state-of-the-art system
for facial action unit detection based on the hidden-unit logistic model.
PupilNet: Convolutional Neural Networks for Robust Pupil Detection
Authors: Wolfgang Fuhl, Thiago Santini, Gjergji Kasneci, Enkelejda Kasneci
Date: 2016-01-19 00:00:00
Tasks: Position, Pupil Detection

Real-time, accurate, and robust pupil detection is an essential prerequisite
for pervasive video-based eye-tracking. However, automated pupil detection in
real-world scenarios has proven to be an intricate challenge due to fast
illumination changes, pupil occlusion, non centered and off-axis eye recording,
and physiological eye characteristics. In this paper, we propose and evaluate a
method based on a novel dual convolutional neural network pipeline. In its
first stage the pipeline performs coarse pupil position identification using a
convolutional neural network and subregions from a downscaled input image to
decrease computational costs. Using subregions derived from a small window
around the initial pupil position estimate, the second pipeline stage employs
another convolutional neural network to refine this position, resulting in an
increased pupil detection rate up to 25% in comparison with the best performing
state-of-the-art algorithm. Annotated data sets can be made available upon
request.