pwc-archive/papers-with-abstracts huggingface dataset:
A Model of Free Will for Artificial Entities
Authors: Eric Sanchis
Date: 2018-02-26 00:00:00
Tasks: Decision Making, model

The impression of free will is the feeling according to which our choices are
neither imposed from our inside nor from outside. It is the sense we are the
ultimate cause of our acts. In direct opposition with the universal
determinism, the existence of free will continues to be discussed. In this
paper, free will is linked to a decisional mechanism: an agent is provided with
free will if having performed a predictable choice Cp, it can immediately
perform another choice Cr in a random way. The intangible feeling of free will
is replaced by a decision-making process including a predictable
decision-making process immediately followed by an unpredictable decisional
one.
Yedrouj-Net: An efficient CNN for spatial steganalysis
Authors: Mehdi Yedroudj, Frederic Comby, Marc Chaumont
Date: 2018-02-26 00:00:00
Tasks: Steganalysis

For about 10 years, detecting the presence of a secret message hidden in an
image was performed with an Ensemble Classifier trained with Rich features. In
recent years, studies such as Xu et al. have indicated that well-designed
convolutional Neural Networks (CNN) can achieve comparable performance to the
two-step machine learning approaches.
  In this paper, we propose a CNN that outperforms the state-ofthe-art in terms
of error probability. The proposition is in the continuity of what has been
recently proposed and it is a clever fusion of important bricks used in various
papers. Among the essential parts of the CNN, one can cite the use of a
pre-processing filterbank and a Truncation activation function, five
convolutional layers with a Batch Normalization associated with a Scale Layer,
as well as the use of a sufficiently sized fully connected section. An
augmented database has also been used to improve the training of the CNN.
  Our CNN was experimentally evaluated against S-UNIWARD and WOW embedding
algorithms and its performances were compared with those of three other
methods: an Ensemble Classifier plus a Rich Model, and two other CNN
steganalyzers.
Towards Robust Detection of Adversarial Examples
Authors: Tianyu Pang, Chao Du, Yinpeng Dong, Jun Zhu
Date: 2017-06-02 00:00:00
Proceeding: NeurIPS 2018 12

Although the recent progress is substantial, deep learning methods can be
vulnerable to the maliciously generated adversarial examples. In this paper, we
present a novel training procedure and a thresholding test strategy, towards
robust detection of adversarial examples. In training, we propose to minimize
the reverse cross-entropy (RCE), which encourages a deep network to learn
latent representations that better distinguish adversarial examples from normal
ones. In testing, we propose to use a thresholding strategy as the detector to
filter out adversarial examples for reliable predictions. Our method is simple
to implement using standard algorithms, with little extra training cost
compared to the common cross-entropy minimization. We apply our method to
defend various attacking methods on the widely used MNIST and CIFAR-10
datasets, and achieve significant improvements on robust predictions under all
the threat models in the adversarial setting.
Max-Mahalanobis Linear Discriminant Analysis Networks
Authors: Tianyu Pang, Chao Du, Jun Zhu
Date: 2018-02-26 00:00:00
Proceeding: ICML 2018 7

A deep neural network (DNN) consists of a nonlinear transformation from an
input to a feature representation, followed by a common softmax linear
classifier. Though many efforts have been devoted to designing a proper
architecture for nonlinear transformation, little investigation has been done
on the classifier part. In this paper, we show that a properly designed
classifier can improve robustness to adversarial attacks and lead to better
prediction results. Specifically, we define a Max-Mahalanobis distribution
(MMD) and theoretically show that if the input distributes as a MMD, the linear
discriminant analysis (LDA) classifier will have the best robustness to
adversarial examples. We further propose a novel Max-Mahalanobis linear
discriminant analysis (MM-LDA) network, which explicitly maps a complicated
data distribution in the input space to a MMD in the latent feature space and
then applies LDA to make predictions. Our results demonstrate that the MM-LDA
networks are significantly more robust to adversarial attacks, and have better
performance in class-biased classification.
Dimension-free Information Concentration via Exp-Concavity
Authors: Ya-Ping Hsieh, Volkan Cevher
Date: 2018-02-26 00:00:00
Tasks: Learning Theory

Information concentration of probability measures have important implications
in learning theory. Recently, it is discovered that the information content of
a log-concave distribution concentrates around their differential entropy,
albeit with an unpleasant dependence on the ambient dimension. In this work, we
prove that if the potentials of the log-concave distribution are exp-concave,
which is a central notion for fast rates in online and statistical learning,
then the concentration of information can be further improved to depend only on
the exp-concavity parameter, and hence, it can be dimension independent.
Central to our proof is a novel yet simple application of the variance
Brascamp-Lieb inequality. In the context of learning theory, our
concentration-of-information result immediately implies high-probability
results to many of the previous bounds that only hold in expectation.
Beyond Pixels: Leveraging Geometry and Shape Cues for Online Multi-Object Tracking
Authors: Sarthak Sharma, Junaid Ahmed Ansari, J. Krishna Murthy, K. Madhava Krishna
Date: 2018-02-26 00:00:00
Tasks: Multi-Object Tracking, Multiple Object Tracking, Object, Object Tracking, Online Multi-Object Tracking

This paper introduces geometry and object shape and pose costs for
multi-object tracking in urban driving scenarios. Using images from a monocular
camera alone, we devise pairwise costs for object tracks, based on several 3D
cues such as object pose, shape, and motion. The proposed costs are agnostic to
the data association method and can be incorporated into any optimization
framework to output the pairwise data associations. These costs are easy to
implement, can be computed in real-time, and complement each other to account
for possible errors in a tracking-by-detection framework. We perform an
extensive analysis of the designed costs and empirically demonstrate consistent
improvement over the state-of-the-art under varying conditions that employ a
range of object detectors, exhibit a variety in camera and object motions, and,
more importantly, are not reliant on the choice of the association framework.
We also show that, by using the simplest of associations frameworks (two-frame
Hungarian assignment), we surpass the state-of-the-art in multi-object-tracking
on road scenes. More qualitative and quantitative results can be found at the
following URL: https://junaidcs032.github.io/Geometry_ObjectShape_MOT/.
AMUSE: Multilingual Semantic Parsing for Question Answering over Linked Data
Authors: Sherzod Hakimov, Soufian Jebbara, Philipp Cimiano
Date: 2018-02-26 00:00:00
Tasks: Form, Knowledge Base Question Answering, Machine Translation, Question Answering, Semantic Parsing

The task of answering natural language questions over RDF data has received
wide interest in recent years, in particular in the context of the series of
QALD benchmarks. The task consists of mapping a natural language question to an
executable form, e.g. SPARQL, so that answers from a given KB can be extracted.
So far, most systems proposed are i) monolingual and ii) rely on a set of
hard-coded rules to interpret questions and map them into a SPARQL query. We
present the first multilingual QALD pipeline that induces a model from training
data for mapping a natural language question into logical form as probabilistic
inference. In particular, our approach learns to map universal syntactic
dependency representations to a language-independent logical form based on
DUDES (Dependency-based Underspecified Discourse Representation Structures)
that are then mapped to a SPARQL query as a deterministic second step. Our
model builds on factor graphs that rely on features extracted from the
dependency graph and corresponding semantic representations. We rely on
approximate inference techniques, Markov Chain Monte Carlo methods in
particular, as well as Sample Rank to update parameters using a ranking
objective. Our focus lies on developing methods that overcome the lexical gap
and present a novel combination of machine translation and word embedding
approaches for this purpose. As a proof of concept for our approach, we
evaluate our approach on the QALD-6 datasets for English, German & Spanish.
Constructing Category-Specific Models for Monocular Object-SLAM
Authors: Parv Parkhiya, Rishabh Khawad, J. Krishna Murthy, Brojeshwar Bhowmick, K. Madhava Krishna
Date: 2018-02-26 00:00:00
Tasks: Object, Object SLAM, Retrieval

We present a new paradigm for real-time object-oriented SLAM with a monocular
camera. Contrary to previous approaches, that rely on object-level models, we
construct category-level models from CAD collections which are now widely
available. To alleviate the need for huge amounts of labeled data, we develop a
rendering pipeline that enables synthesis of large datasets from a limited
amount of manually labeled data. Using data thus synthesized, we learn
category-level models for object deformations in 3D, as well as discriminative
object features in 2D. These category models are instance-independent and aid
in the design of object landmark observations that can be incorporated into a
generic monocular SLAM framework. Where typical object-SLAM approaches usually
solve only for object and camera poses, we also estimate object shape
on-the-fly, allowing for a wide range of objects from the category to be
present in the scene. Moreover, since our 2D object features are learned
discriminatively, the proposed object-SLAM system succeeds in several scenarios
where sparse feature-based monocular SLAM fails due to insufficient features or
parallax. Also, the proposed category-models help in object instance retrieval,
useful for Augmented Reality (AR) applications. We evaluate the proposed
framework on multiple challenging real-world scenes and show --- to the best of
our knowledge --- first results of an instance-independent monocular
object-SLAM system and the benefits it enjoys over feature-based SLAM methods.
A Modification of Particle Swarm Optimization using Random Walk
Authors: Rajesh Misra, Kumar S. Ray
Date: 2017-11-16 00:00:00

Particle swarm optimization comes under lot of changes after James Kennedy
and Russell Eberhart first proposes the idea in 1995. The changes has been done
mainly on Inertia parameters in velocity updating equation so that the
convergence rate will be higher. We are proposing a novel approach where
particles movement will not be depend on its velocity rather it will be decided
by constrained biased random walk of particles. In random walk every particles
movement based on two significant parameters, one is random process like toss
of a coin and other is how much displacement a particle should have. In our
approach we exploit this idea by performing a biased random operation and based
on the outcome of that random operation, PSO particles choose the direction of
the path and move non-uniformly into the solution space. This constrained,
non-uniform movement helps the random walking particle to converge quicker then
classical PSO. In our constrained biased random walking approach, we no longer
needed velocity term (Vi), rather we introduce a new parameter (K) which is a
probabilistic function. No global best particle (PGbest), local best particle
(PLbest), Constriction parameter (W) are required rather we use a new term
called Ptarg which is loosely influenced by PGbest.We test our algorithm on
five different benchmark functions, and also compare its performance with
classical PSO and Quantum Particle Swarm Optimization (QPSO).This new approach
have been shown significantly better than basic PSO and sometime outperform
QPSO in terms of convergence, search space, number of iterations.
Gender Aware Spoken Language Translation Applied to English-Arabic
Authors: Mostafa Elaraby, Ahmed Y. Tawfik, Mahmoud Khaled, Hany Hassan, Aly Osama
Date: 2018-02-26 00:00:00
Tasks: Machine Translation, NMT, Translation

Spoken Language Translation (SLT) is becoming more widely used and becoming a
communication tool that helps in crossing language barriers. One of the
challenges of SLT is the translation from a language without gender agreement
to a language with gender agreement such as English to Arabic. In this paper,
we introduce an approach to tackle such limitation by enabling a Neural Machine
Translation system to produce gender-aware translation. We show that NMT system
can model the speaker/listener gender information to produce gender-aware
translation. We propose a method to generate data used in adapting a NMT system
to produce gender-aware. The proposed approach can achieve significant
improvement of the translation quality by 2 BLEU points.
VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning
Authors: Fanhua Shang, Kaiwen Zhou, Hongying Liu, James Cheng, Ivor W. Tsang, Lijun Zhang, DaCheng Tao, Licheng Jiao
Date: 2018-02-26 00:00:00
Tasks: BIG-bench Machine Learning

In this paper, we propose a simple variant of the original SVRG, called
variance reduced stochastic gradient descent (VR-SGD). Unlike the choices of
snapshot and starting points in SVRG and its proximal variant, Prox-SVRG, the
two vectors of VR-SGD are set to the average and last iterate of the previous
epoch, respectively. The settings allow us to use much larger learning rates,
and also make our convergence analysis more challenging. We also design two
different update rules for smooth and non-smooth objective functions,
respectively, which means that VR-SGD can tackle non-smooth and/or non-strongly
convex problems directly without any reduction techniques. Moreover, we analyze
the convergence properties of VR-SGD for strongly convex problems, which show
that VR-SGD attains linear convergence. Different from its counterparts that
have no convergence guarantees for non-strongly convex problems, we also
provide the convergence guarantees of VR-SGD for this case, and empirically
verify that VR-SGD with varying learning rates achieves similar performance to
its momentum accelerated variant that has the optimal convergence rate
$\mathcal{O}(1/T^2)$. Finally, we apply VR-SGD to solve various machine
learning problems, such as convex and non-convex empirical risk minimization,
and leading eigenvalue computation. Experimental results show that VR-SGD
converges significantly faster than SVRG and Prox-SVRG, and usually outperforms
state-of-the-art accelerated methods, e.g., Katyusha.
Accessible Melanoma Detection using Smartphones and Mobile Image Analysis
Authors: T. -T. Do, T. Hoang, V. Pomponiu, Y. Zhou, Z. Chen, N. -M. Cheung, D. Koh, A. Tan, S. -H. Tan
Date: 2017-11-27 00:00:00
Tasks: feature selection, Segmentation

We investigate the design of an entire mobile imaging system for early
detection of melanoma. Different from previous work, we focus on
smartphone-captured visible light images. Our design addresses two major
challenges. First, images acquired using a smartphone under loosely-controlled
environmental conditions may be subject to various distortions, and this makes
melanoma detection more difficult. Second, processing performed on a smartphone
is subject to stringent computation and memory constraints. In our work, we
propose a detection system that is optimized to run entirely on the
resource-constrained smartphone. Our system intends to localize the skin lesion
by combining a lightweight method for skin detection with a hierarchical
segmentation approach using two fast segmentation methods. Moreover, we study
an extensive set of image features and propose new numerical features to
characterize a skin lesion. Furthermore, we propose an improved feature
selection algorithm to determine a small set of discriminative features used by
the final lightweight system. In addition, we study the human-computer
interface (HCI) design to understand the usability and acceptance issues of the
proposed system.
Verifying Controllers Against Adversarial Examples with Bayesian Optimization
Authors: Shromona Ghosh, Felix Berkenkamp, Gireeja Ranade, Shaz Qadeer, Ashish Kapoor
Date: 2018-02-23 00:00:00
Tasks: Bayesian Optimization, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent successes in reinforcement learning have lead to the development of
complex controllers for real-world robots. As these robots are deployed in
safety-critical applications and interact with humans, it becomes critical to
ensure safety in order to avoid causing harm. A first step in this direction is
to test the controllers in simulation. To be able to do this, we need to
capture what we mean by safety and then efficiently search the space of all
behaviors to see if they are safe. In this paper, we present an active-testing
framework based on Bayesian Optimization. We specify safety constraints using
logic and exploit structure in the problem in order to test the system for
adversarial counter examples that violate the safety specifications. These
specifications are defined as complex boolean combinations of smooth functions
on the trajectories and, unlike reward functions in reinforcement learning, are
expressive and impose hard constraints on the system. In our framework, we
exploit regularity assumptions on individual functions in form of a Gaussian
Process (GP) prior. We combine these into a coherent optimization framework
using problem structure. The resulting algorithm is able to provably verify
complex safety specifications or alternatively find counter examples.
Experimental results show that the proposed method is able to find adversarial
examples quickly.
EiTAKA at SemEval-2018 Task 1: An Ensemble of N-Channels ConvNet and XGboost Regressors for Emotion Analysis of Tweets
Authors: Mohammed Jabreel, Antonio Moreno
Date: 2018-02-26 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Emotion Recognition, General Classification, Ordinal Classification, regression

This paper describes our system that has been used in Task1 Affect in Tweets.
We combine two different approaches. The first one called N-Stream ConvNets,
which is a deep learning approach where the second one is XGboost regresseor
based on a set of embedding and lexicons based features. Our system was
evaluated on the testing sets of the tasks outperforming all other approaches
for the Arabic version of valence intensity regression task and valence ordinal
classification task.
Depth Masked Discriminative Correlation Filter
Authors: Uğur Kart, Joni-Kristian Kämäräinen, Jiří Matas, Lixin Fan, Francesco Cricri
Date: 2018-02-26 00:00:00
Tasks: Object Tracking

Depth information provides a strong cue for occlusion detection and handling,
but has been largely omitted in generic object tracking until recently due to
lack of suitable benchmark datasets and applications. In this work, we propose
a Depth Masked Discriminative Correlation Filter (DM-DCF) which adopts novel
depth segmentation based occlusion detection that stops correlation filter
updating and depth masking which adaptively adjusts the spatial support for
correlation filter. In Princeton RGBD Tracking Benchmark, our DM-DCF is among
the state-of-the-art in overall ranking and the winner on multiple categories.
Moreover, since it is based on DCF, ``DM-DCF`` runs an order of magnitude
faster than its competitors making it suitable for time constrained
applications.
Interpreting Complex Regression Models
Authors: Noa Avigdor-Elgrabli, Alex Libov, Michael Viderman, Ran Wolff
Date: 2018-02-26 00:00:00
Tasks: BIG-bench Machine Learning, Feature Engineering, regression

Interpretation of a machine learning induced models is critical for feature
engineering, debugging, and, arguably, compliance. Yet, best of breed machine
learning models tend to be very complex. This paper presents a method for model
interpretation which has the main benefit that the simple interpretations it
provides are always grounded in actual sets of learning examples. The method is
validated on the task of interpreting a complex regression model in the context
of both an academic problem -- predicting the year in which a song was recorded
and an industrial one -- predicting mail user churn.
Flexible Stereo: Constrained, Non-rigid, Wide-baseline Stereo Vision for Fixed-wing Aerial Platforms
Authors: Timo Hinzmann, Tim Taubner, Roland Siegwart
Date: 2017-12-19 00:00:00

This paper proposes a computationally efficient method to estimate the
time-varying relative pose between two visual-inertial sensor rigs mounted on
the flexible wings of a fixed-wing unmanned aerial vehicle (UAV). The estimated
relative poses are used to generate highly accurate depth maps in real-time and
can be employed for obstacle avoidance in low-altitude flights or landing
maneuvers. The approach is structured as follows: Initially, a wing model is
identified by fitting a probability density function to measured deviations
from the nominal relative baseline transformation. At run-time, the prior
knowledge about the wing model is fused in an Extended Kalman filter~(EKF)
together with relative pose measurements obtained from solving a relative
perspective N-point problem (PNP), and the linear accelerations and angular
velocities measured by the two inertial measurement units (IMU) which are
rigidly attached to the cameras. Results obtained from extensive synthetic
experiments demonstrate that our proposed framework is able to estimate highly
accurate baseline transformations and depth maps.
A representer theorem for deep neural networks
Authors: Michael Unser
Date: 2018-02-26 00:00:00

We propose to optimize the activation functions of a deep neural network by
adding a corresponding functional regularization to the cost function. We
justify the use of a second-order total-variation criterion. This allows us to
derive a general representer theorem for deep neural networks that makes a
direct connection with splines and sparsity. Specifically, we show that the
optimal network configuration can be achieved with activation functions that
are nonuniform linear splines with adaptive knots. The bottom line is that the
action of each neuron is encoded by a spline whose parameters (including the
number of knots) are optimized during the training procedure. The scheme
results in a computational structure that is compatible with the existing
deep-ReLU, parametric ReLU, APL (adaptive piecewise-linear) and MaxOut
architectures. It also suggests novel optimization challenges, while making the
link with $\ell_1$ minimization and sparsity-promoting techniques explicit.