pwc-archive/papers-with-abstracts huggingface dataset:
Compressive Sensing Approaches for Autonomous Object Detection in Video Sequences
Authors: Danil Kuzin, Olga Isupova, Lyudmila Mihaylova
Date: 2017-04-27 00:00:00
Tasks: Compressive Sensing, object-detection, Object Detection

Video analytics requires operating with large amounts of data. Compressive
sensing allows to reduce the number of measurements required to represent the
video using the prior knowledge of sparsity of the original signal, but it
imposes certain conditions on the design matrix. The Bayesian compressive
sensing approach relaxes the limitations of the conventional approach using the
probabilistic reasoning and allows to include different prior knowledge about
the signal structure. This paper presents two Bayesian compressive sensing
methods for autonomous object detection in a video sequence from a static
camera. Their performance is compared on the real datasets with the
non-Bayesian greedy algorithm. It is shown that the Bayesian methods can
provide the same accuracy as the greedy algorithm but much faster; or if the
computational time is not critical they can provide more accurate results.
Structured Sparse Modelling with Hierarchical GP
Authors: Danil Kuzin, Olga Isupova, Lyudmila Mihaylova
Date: 2017-04-27 00:00:00
Tasks: regression

In this paper a new Bayesian model for sparse linear regression with a
spatio-temporal structure is proposed. It incorporates the structural
assumptions based on a hierarchical Gaussian process prior for spike and slab
coefficients. We design an inference algorithm based on Expectation Propagation
and evaluate the model over the real data.
Action Understanding with Multiple Classes of Actors
Authors: Chenliang Xu, Caiming Xiong, Jason J. Corso
Date: 2017-04-27 00:00:00
Tasks: Action Recognition, Action Segmentation, Action Understanding, Diversity, Temporal Action Localization, Video Understanding

Despite the rapid progress, existing works on action understanding focus
strictly on one type of action agent, which we call actor---a human adult,
ignoring the diversity of actions performed by other actors. To overcome this
narrow viewpoint, our paper marks the first effort in the computer vision
community to jointly consider algorithmic understanding of various types of
actors undergoing various actions. To begin with, we collect a large annotated
Actor-Action Dataset (A2D) that consists of 3782 short videos and 31 temporally
untrimmed long videos. We formulate the general actor-action understanding
problem and instantiate it at various granularities: video-level single- and
multiple-label actor-action recognition, and pixel-level actor-action
segmentation. We propose and examine a comprehensive set of graphical models
that consider the various types of interplay among actors and actions. Our
findings have led us to conclusive evidence that the joint modeling of actor
and action improves performance over modeling each of them independently, and
further improvement can be obtained by considering the multi-scale natural in
video understanding. Hence, our paper concludes the argument of the value of
explicit consideration of various actors in comprehensive action understanding
and provides a dataset and a benchmark for later works exploring this new
problem.
Artificial Intelligence Based Malware Analysis
Authors: Avi Pfeffer, Brian Ruttenberg, Lee Kellogg, Michael Howard, Catherine Call, Alison O'Connor, Glenn Takata, Scott Neal Reilly, Terry Patten, Jason Taylor, Robert Hall, Arun Lakhotia, Craig Miles, Dan Scofield, Jared Frank
Date: 2017-04-27 00:00:00
Tasks: Malware Analysis

Artificial intelligence methods have often been applied to perform specific
functions or tasks in the cyber-defense realm. However, as adversary methods
become more complex and difficult to divine, piecemeal efforts to understand
cyber-attacks, and malware-based attacks in particular, are not providing
sufficient means for malware analysts to understand the past, present and
future characteristics of malware.
  In this paper, we present the Malware Analysis and Attributed using Genetic
Information (MAAGI) system. The underlying idea behind the MAAGI system is that
there are strong similarities between malware behavior and biological organism
behavior, and applying biologically inspired methods to corpora of malware can
help analysts better understand the ecosystem of malware attacks. Due to the
sophistication of the malware and the analysis, the MAAGI system relies heavily
on artificial intelligence techniques to provide this capability. It has
already yielded promising results over its development life, and will hopefully
inspire more integration between the artificial intelligence and cyber--defense
communities.
A Siamese Deep Forest
Authors: Lev V. Utkin, Mikhail A. Ryabinin
Date: 2017-04-27 00:00:00

A Siamese Deep Forest (SDF) is proposed in the paper. It is based on the Deep
Forest or gcForest proposed by Zhou and Feng and can be viewed as a gcForest
modification. It can be also regarded as an alternative to the well-known
Siamese neural networks. The SDF uses a modified training set consisting of
concatenated pairs of vectors. Moreover, it defines the class distributions in
the deep forest as the weighted sum of the tree class probabilities such that
the weights are determined in order to reduce distances between similar pairs
and to increase them between dissimilar points. We show that the weights can be
obtained by solving a quadratic optimization problem. The SDF aims to prevent
overfitting which takes place in neural networks when only limited training
data are available. The numerical experiments illustrate the proposed distance
metric method.
Learning the structure of Bayesian Networks: A quantitative assessment of the effect of different algorithmic schemes
Authors: Stefano Beretta, Mauro Castelli, Ivo Goncalves, Roberto Henriques, Daniele Ramazzotti
Date: 2017-04-27 00:00:00

One of the most challenging tasks when adopting Bayesian Networks (BNs) is
the one of learning their structure from data. This task is complicated by the
huge search space of possible solutions, and by the fact that the problem is
NP-hard. Hence, full enumeration of all the possible solutions is not always
feasible and approximations are often required. However, to the best of our
knowledge, a quantitative analysis of the performance and characteristics of
the different heuristics to solve this problem has never been done before.
  For this reason, in this work, we provide a detailed comparison of many
different state-of-the-arts methods for structural learning on simulated data
considering both BNs with discrete and continuous variables, and with different
rates of noise in the data. In particular, we investigate the performance of
different widespread scores and algorithmic approaches proposed for the
inference and the statistical pitfalls within them.
Semantic Jitter: Dense Supervision for Visual Comparisons via Synthetic Images
Authors: Aron Yu, Kristen Grauman
Date: 2016-12-19 00:00:00
Proceeding: ICCV 2017 10
Tasks: Attribute, Image Generation, Learning-To-Rank

Distinguishing subtle differences in attributes is valuable, yet learning to
make visual comparisons remains non-trivial. Not only is the number of possible
comparisons quadratic in the number of training images, but also access to
images adequately spanning the space of fine-grained visual differences is
limited. We propose to overcome the sparsity of supervision problem via
synthetically generated images. Building on a state-of-the-art image generation
engine, we sample pairs of training images exhibiting slight modifications of
individual attributes. Augmenting real training image pairs with these
examples, we then train attribute ranking models to predict the relative
strength of an attribute in novel pairs of real images. Our results on datasets
of faces and fashion images show the great promise of bootstrapping imperfect
image generators to counteract sample sparsity for learning to rank.
Adversary Resistant Deep Neural Networks with an Application to Malware Detection
Authors: Qinglong Wang, Wenbo Guo, Kaixuan Zhang, Alexander G. Ororbia II, Xinyu Xing, C. Lee Giles, Xue Liu
Date: 2016-10-05 00:00:00
Tasks: Deep Learning, Information Retrieval, Malware Detection, Retrieval, speech-recognition, Speech Recognition

Beyond its highly publicized victories in Go, there have been numerous
successful applications of deep learning in information retrieval, computer
vision and speech recognition. In cybersecurity, an increasing number of
companies have become excited about the potential of deep learning, and have
started to use it for various security incidents, the most popular being
malware detection. These companies assert that deep learning (DL) could help
turn the tide in the battle against malware infections. However, deep neural
networks (DNNs) are vulnerable to adversarial samples, a flaw that plagues most
if not all statistical learning models. Recent research has demonstrated that
those with malicious intent can easily circumvent deep learning-powered malware
detection by exploiting this flaw.
  In order to address this problem, previous work has developed various defense
mechanisms that either augmenting training data or enhance model's complexity.
However, after a thorough analysis of the fundamental flaw in DNNs, we discover
that the effectiveness of current defenses is limited and, more importantly,
cannot provide theoretical guarantees as to their robustness against
adversarial sampled-based attacks. As such, we propose a new adversary
resistant technique that obstructs attackers from constructing impactful
adversarial samples by randomly nullifying features within samples. In this
work, we evaluate our proposed technique against a real world dataset with
14,679 malware variants and 17,399 benign programs. We theoretically validate
the robustness of our technique, and empirically show that our technique
significantly boosts DNN robustness to adversarial samples while maintaining
high accuracy in classification. To demonstrate the general applicability of
our proposed method, we also conduct experiments using the MNIST and CIFAR-10
datasets, generally used in image recognition research.
Online Natural Gradient as a Kalman Filter
Authors: Yann Ollivier
Date: 2017-03-01 00:00:00

We cast Amari's natural gradient in statistical learning as a specific case
of Kalman filtering. Namely, applying an extended Kalman filter to estimate a
fixed unknown parameter of a probabilistic model from a series of observations,
is rigorously equivalent to estimating this parameter via an online stochastic
natural gradient descent on the log-likelihood of the observations.
  In the i.i.d. case, this relation is a consequence of the "information
filter" phrasing of the extended Kalman filter. In the recurrent (state space,
non-i.i.d.) case, we prove that the joint Kalman filter over states and
parameters is a natural gradient on top of real-time recurrent learning (RTRL),
a classical algorithm to train recurrent models.
  This exact algebraic correspondence provides relevant interpretations for
natural gradient hyperparameters such as learning rates or initialization and
regularization of the Fisher information matrix.
Learning Correspondence Structures for Person Re-identification
Authors: Weiyao Lin, Yang shen, Junchi Yan, Mingliang Xu, Jianxin Wu, Jingdong Wang, Ke Lu
Date: 2017-03-20 00:00:00
Tasks: Patch Matching, Person Re-Identification

This paper addresses the problem of handling spatial misalignments due to
camera-view changes or human-pose variations in person re-identification. We
first introduce a boosting-based approach to learn a correspondence structure
which indicates the patch-wise matching probabilities between images from a
target camera pair. The learned correspondence structure can not only capture
the spatial correspondence pattern between cameras but also handle the
viewpoint or human-pose variation in individual images. We further introduce a
global constraint-based matching process. It integrates a global matching
constraint over the learned correspondence structure to exclude cross-view
misalignments during the image patch matching process, hence achieving a more
reliable matching score between images. Finally, we also extend our approach by
introducing a multi-structure scheme, which learns a set of local
correspondence structures to capture the spatial correspondence sub-patterns
between a camera pair, so as to handle the spatial misalignments between
individual images in a more precise way. Experimental results on various
datasets demonstrate the effectiveness of our approach.
True Lies
Authors: Thomas Ågotnes, Hans van Ditmarsch, Yanjing Wang
Date: 2016-06-27 00:00:00

A true lie is a lie that becomes true when announced. In a logic of
announcements, where the announcing agent is not modelled, a true lie is a
formula (that is false and) that becomes true when announced. We investigate
true lies and other types of interaction between announced formulas, their
preconditions and their postconditions, in the setting of Gerbrandy's logic of
believed announcements, wherein agents may have or obtain incorrect beliefs.
Our results are on the satisfiability and validity of instantiations of these
semantically defined categories, on iterated announcements, including
arbitrarily often iterated announcements, and on syntactic characterization. We
close with results for iterated announcements in the logic of knowledge
(instead of belief), and for lying as private announcements (instead of public
announcements) to different agents. Detailed examples illustrate our lying
concepts.
Sparse Hierachical Extrapolated Parametric Methods for Cortical Data Analysis
Authors: Nicolas Honnorat, Christos Davatzikos
Date: 2017-04-27 00:00:00
Tasks: Dictionary Learning

Many neuroimaging studies focus on the cortex, in order to benefit from
better signal to noise ratios and reduced computational burden. Cortical data
are usually projected onto a reference mesh, where subsequent analyses are
carried out. Several multiscale approaches have been proposed for analyzing
these surface data, such as spherical harmonics and graph wavelets. As far as
we know, however, the hierarchical structure of the template icosahedral meshes
used by most neuroimaging software has never been exploited for cortical data
factorization. In this paper, we demonstrate how the structure of the
ubiquitous icosahedral meshes can be exploited by data factorization methods
such as sparse dictionary learning, and we assess the optimization speed-up
offered by extrapolation methods in this context. By testing different
sparsity-inducing norms, extrapolation methods, and factorization schemes, we
compare the performances of eleven methods for analyzing four datasets: two
structural and two functional MRI datasets obtained by processing the data
publicly available for the hundred unrelated subjects of the Human Connectome
Project. Our results demonstrate that, depending on the level of details
requested, a speedup of several orders of magnitudes can be obtained.
Full-Page Text Recognition: Learning Where to Start and When to Stop
Authors: Bastien Moysset, Christopher Kermorvant, Christian Wolf
Date: 2017-04-27 00:00:00
Tasks: Line Detection, Position

Text line detection and localization is a crucial step for full page document
analysis, but still suffers from heterogeneity of real life documents. In this
paper, we present a new approach for full page text recognition. Localization
of the text lines is based on regressions with Fully Convolutional Neural
Networks and Multidimensional Long Short-Term Memory as contextual layers. In
order to increase the efficiency of this localization method, only the position
of the left side of the text lines are predicted. The text recognizer is then
in charge of predicting the end of the text to recognize. This method has shown
good results for full page text recognition on the highly heterogeneous Maurdor
dataset.
Continuous parameter working memory in a balanced chaotic neural network
Authors: Nimrod Shaham, Yoram Burak
Date: 2015-08-27 00:00:00

It has been proposed that neural noise in the cortex arises from chaotic
dynamics in the balanced state: in this model of cortical dynamics, the
excitatory and inhibitory inputs to each neuron approximately cancel, and
activity is driven by fluctuations of the synaptic inputs around their mean. It
remains unclear whether neural networks in the balanced state can perform tasks
that are highly sensitive to noise, such as storage of continuous parameters in
working memory, while also accounting for the irregular behavior of single
neurons. Here we show that continuous parameter working memory can be
maintained in the balanced state, in a neural circuit with a simple network
architecture. We show analytically that in the limit of an infinite network,
the dynamics generated by this architecture are characterized by a continuous
set of steady balanced states, allowing for the indefinite storage of a
continuous parameter. In finite networks, we show that the chaotic noise drives
diffusive motion along the approximate attractor, which gradually degrades the
stored memory. We analyze the dynamics and show that the slow diffusive motion
induces slowly decaying temporal cross correlations in the activity, which
differ substantially from those previously described in the balanced state. We
calculate the diffusivity, and show that it is inversely proportional to the
system size. For large enough (but realistic) neural population sizes, and with
suitable tuning of the network connections, the proposed balanced network can
sustain continuous parameter values in memory over time scales larger by
several orders of magnitude than the single neuron time scale.
End-to-End Multimodal Emotion Recognition using Deep Neural Networks
Authors: Panagiotis Tzirakis, George Trigeorgis, Mihalis A. Nicolaou, Björn Schuller, Stefanos Zafeiriou
Date: 2017-04-27 00:00:00
Tasks: Emotion Recognition, Multimodal Emotion Recognition, Retrieval

Automatic affect recognition is a challenging task due to the various
modalities emotions can be expressed with. Applications can be found in many
domains including multimedia retrieval and human computer interaction. In
recent years, deep neural networks have been used with great success in
determining emotional states. Inspired by this success, we propose an emotion
recognition system using auditory and visual modalities. To capture the
emotional content for various styles of speaking, robust features need to be
extracted. To this purpose, we utilize a Convolutional Neural Network (CNN) to
extract features from the speech, while for the visual modality a deep residual
network (ResNet) of 50 layers. In addition to the importance of feature
extraction, a machine learning algorithm needs also to be insensitive to
outliers while being able to model the context. To tackle this problem, Long
Short-Term Memory (LSTM) networks are utilized. The system is then trained in
an end-to-end fashion where - by also taking advantage of the correlations of
the each of the streams - we manage to significantly outperform the traditional
approaches based on auditory and visual handcrafted features for the prediction
of spontaneous and natural emotions on the RECOLA database of the AVEC 2016
research challenge on emotion recognition.
Saliency Benchmarking Made Easy: Separating Models, Maps and Metrics
Authors: Matthias Kümmerer, Thomas S. A. Wallis, Matthias Bethge
Date: 2017-04-27 00:00:00
Proceeding: ECCV 2018 9
Tasks: All, Benchmarking

Dozens of new models on fixation prediction are published every year and
compared on open benchmarks such as MIT300 and LSUN. However, progress in the
field can be difficult to judge because models are compared using a variety of
inconsistent metrics. Here we show that no single saliency map can perform well
under all metrics. Instead, we propose a principled approach to solve the
benchmarking problem by separating the notions of saliency models, maps and
metrics. Inspired by Bayesian decision theory, we define a saliency model to be
a probabilistic model of fixation density prediction and a saliency map to be a
metric-specific prediction derived from the model density which maximizes the
expected performance on that metric given the model density. We derive these
optimal saliency maps for the most commonly used saliency metrics (AUC, sAUC,
NSS, CC, SIM, KL-Div) and show that they can be computed analytically or
approximated with high precision. We show that this leads to consistent
rankings in all metrics and avoids the penalties of using one saliency map for
all metrics. Our method allows researchers to have their model compete on many
different metrics with state-of-the-art in those metrics: "good" models will
perform well in all metrics.
Learning Representations by Stochastic Meta-Gradient Descent in Neural Networks
Authors: Vivek Veeriah, Shangtong Zhang, Richard S. Sutton
Date: 2016-12-09 00:00:00
Tasks: Incremental Learning

Representations are fundamental to artificial intelligence. The performance
of a learning system depends on the type of representation used for
representing the data. Typically, these representations are hand-engineered
using domain knowledge. More recently, the trend is to learn these
representations through stochastic gradient descent in multi-layer neural
networks, which is called backprop. Learning the representations directly from
the incoming data stream reduces the human labour involved in designing a
learning system. More importantly, this allows in scaling of a learning system
for difficult tasks. In this paper, we introduce a new incremental learning
algorithm called crossprop, which learns incoming weights of hidden units based
on the meta-gradient descent approach, that was previously introduced by Sutton
(1992) and Schraudolph (1999) for learning step-sizes. The final update
equation introduces an additional memory parameter for each of these weights
and generalizes the backprop update equation. From our experiments, we show
that crossprop learns and reuses its feature representation while tackling new
and unseen tasks whereas backprop relearns a new feature representation.
Slice-to-volume medical image registration: a survey
Authors: Enzo Ferrante, Nikos Paragios
Date: 2017-02-06 00:00:00
Tasks: Image Reconstruction, Image Registration, Medical Image Registration, Survey

During the last decades, the research community of medical imaging has
witnessed continuous advances in image registration methods, which pushed the
limits of the state-of-the-art and enabled the development of novel medical
procedures. A particular type of image registration problem, known as
slice-to-volume registration, played a fundamental role in areas like image
guided surgeries and volumetric image reconstruction. However, to date, and
despite the extensive literature available on this topic, no survey has been
written to discuss this challenging problem. This paper introduces the first
comprehensive survey of the literature about slice-to-volume registration,
presenting a categorical study of the algorithms according to an ad-hoc
taxonomy and analyzing advantages and disadvantages of every category. We draw
some general conclusions from this analysis and present our perspectives on the
future of the field.