pwc-archive/papers-with-abstracts huggingface dataset:
Learning Universal Adversarial Perturbations with Generative Models
Authors: Jamie Hayes, George Danezis
Date: 2017-08-17 00:00:00
Tasks: Graph Classification

Neural networks are known to be vulnerable to adversarial examples, inputs
that have been intentionally perturbed to remain visually similar to the source
input, but cause a misclassification. It was recently shown that given a
dataset and classifier, there exists so called universal adversarial
perturbations, a single perturbation that causes a misclassification when
applied to any input. In this work, we introduce universal adversarial
networks, a generative network that is capable of fooling a target classifier
when it's generated output is added to a clean sample from a dataset. We show
that this technique improves on known universal adversarial attacks.
Nonparametric Stochastic Contextual Bandits
Authors: Melody Y. Guan, Heinrich Jiang
Date: 2018-01-05 00:00:00
Tasks: General Classification, image-classification, Image Classification, Multi-Armed Bandits

We analyze the $K$-armed bandit problem where the reward for each arm is a
noisy realization based on an observed context under mild nonparametric
assumptions. We attain tight results for top-arm identification and a sublinear
regret of $\widetilde{O}\Big(T^{\frac{1+D}{2+D}}\Big)$, where $D$ is the
context dimension, for a modified UCB algorithm that is simple to implement
($k$NN-UCB). We then give global intrinsic dimension dependent and ambient
dimension independent regret bounds. We also discuss recovering topological
structures within the context space based on expected bandit performance and
provide an extension to infinite-armed contextual bandits. Finally, we
experimentally show the improvement of our algorithm over existing multi-armed
bandit approaches for both simulated tasks and MNIST image classification.
A relativistic extension of Hopfield neural networks via the mechanical analogy
Authors: Adriano Barra, Matteo Beccaria, Alberto Fachechi
Date: 2018-01-05 00:00:00
Tasks: Network Pruning

We propose a modification of the cost function of the Hopfield model whose
salient features shine in its Taylor expansion and result in more than pairwise
interactions with alternate signs, suggesting a unified framework for handling
both with deep learning and network pruning. In our analysis, we heavily rely
on the Hamilton-Jacobi correspondence relating the statistical model with a
mechanical system. In this picture, our model is nothing but the relativistic
extension of the original Hopfield model (whose cost function is a quadratic
form in the Mattis magnetization which mimics the non-relativistic Hamiltonian
for a free particle). We focus on the low-storage regime and solve the model
analytically by taking advantage of the mechanical analogy, thus obtaining a
complete characterization of the free energy and the associated
self-consistency equations in the thermodynamic limit. On the numerical side,
we test the performances of our proposal with MC simulations, showing that the
stability of spurious states (limiting the capabilities of the standard Hebbian
construction) is sensibly reduced due to presence of unlearning contributions
in this extended framework.
A dynamic connectome supports the emergence of stable computational function of neural circuits through reward-based learning
Authors: David Kappel, Robert Legenstein, Stefan Habenschuss, Michael Hsieh, Wolfgang Maass
Date: 2017-04-13 00:00:00
Tasks: Reinforcement Learning

Synaptic connections between neurons in the brain are dynamic because of
continuously ongoing spine dynamics, axonal sprouting, and other processes. In
fact, it was recently shown that the spontaneous synapse-autonomous component
of spine dynamics is at least as large as the component that depends on the
history of pre- and postsynaptic neural activity. These data are inconsistent
with common models for network plasticity, and raise the questions how neural
circuits can maintain a stable computational function in spite of these
continuously ongoing processes, and what functional uses these ongoing
processes might have. Here, we present a rigorous theoretical framework for
these seemingly stochastic spine dynamics and rewiring processes in the context
of reward-based learning tasks. We show that spontaneous synapse-autonomous
processes, in combination with reward signals such as dopamine, can explain the
capability of networks of neurons in the brain to configure themselves for
specific computational tasks, and to compensate automatically for later changes
in the network or task. Furthermore we show theoretically and through computer
simulations that stable computational performance is compatible with
continuously ongoing synapse-autonomous changes. After reaching good
computational performance it causes primarily a slow drift of network
architecture and dynamics in task-irrelevant dimensions, as observed for neural
activity in motor cortex and other areas. On the more abstract level of
reinforcement learning the resulting model gives rise to an understanding of
reward-driven network plasticity as continuous sampling of network
configurations.
Deep Neural Network Architectures for Modulation Classification
Authors: Xiaoyu Liu, Diyu Yang, Aly El Gamal
Date: 2017-12-01 00:00:00
Tasks: Classification, General Classification

In this work, we investigate the value of employing deep learning for the
task of wireless signal modulation recognition. Recently in [1], a framework
has been introduced by generating a dataset using GNU radio that mimics the
imperfections in a real wireless channel, and uses 10 different modulation
types. Further, a convolutional neural network (CNN) architecture was developed
and shown to deliver performance that exceeds that of expert-based approaches.
Here, we follow the framework of [1] and find deep neural network architectures
that deliver higher accuracy than the state of the art. We tested the
architecture of [1] and found it to achieve an accuracy of approximately 75% of
correctly recognizing the modulation type. We first tune the CNN architecture
of [1] and find a design with four convolutional layers and two dense layers
that gives an accuracy of approximately 83.8% at high SNR. We then develop
architectures based on the recently introduced ideas of Residual Networks
(ResNet [2]) and Densely Connected Networks (DenseNet [3]) to achieve high SNR
accuracies of approximately 83.5% and 86.6%, respectively. Finally, we
introduce a Convolutional Long Short-term Deep Neural Network (CLDNN [4]) to
achieve an accuracy of approximately 88.5% at high SNR.
Enhanced Image Classification With Data Augmentation Using Position Coordinates
Authors: Avinash Kori, Ganapathy Krishnamurthi, Balaji Srinivasan
Date: 2018-01-05 00:00:00
Tasks: Classification, Data Augmentation, General Classification, image-classification, Image Classification, Position

In this paper we propose the use of image pixel position coordinate system to
improve image classification accuracy in various applications. Specifically, we
hypothesize that the use of pixel coordinates will lead to (a) Resolution
invariant performance. Here, by resolution we mean the spacing between the
pixels rather than the size of the image matrix. (b) Overall improvement in
classification accuracy in comparison with network models trained without local
pixel coordinates. This is due to position coordinates enabling the network to
learn relationship between parts of objects, mimicking the human vision system.
We demonstrate our hypothesis using empirical results and intuitive
explanations of the feature maps learnt by deep neural networks. Specifically,
our approach showed improvements in MNIST digit classification and beats state
of the results on the SVHN database. We also show that the performance of our
networks is unaffected despite training the same using blurred images of the
MNIST database and predicting on the high resolution database.
2D-Densely Connected Convolution Neural Networks for automatic Liver and Tumor Segmentation
Authors: Krishna Chaitanya Kaluva, Mahendra Khened, Avinash Kori, Ganapathy Krishnamurthi
Date: 2018-01-05 00:00:00
Tasks: Automatic Liver And Tumor Segmentation, Segmentation, Tumor Segmentation

In this paper we propose a fully automatic 2-stage cascaded approach for
segmentation of liver and its tumors in CT (Computed Tomography) images using
densely connected fully convolutional neural network (DenseNet). We
independently train liver and tumor segmentation models and cascade them for a
combined segmentation of the liver and its tumor. The first stage involves
segmentation of liver and the second stage uses the first stage's segmentation
results for localization of liver and henceforth tumor segmentations inside
liver region. The liver model was trained on the down-sampled axial slices
$(256 \times 256)$, whereas for the tumor model no down-sampling of slices was
done, but instead it was trained on the CT axial slices windowed at three
different Hounsfield (HU) levels. On the test set our model achieved a global
dice score of 0.923 and 0.625 on liver and tumor respectively. The computed
tumor burden had an rmse of 0.044.
Semantic-aware Grad-GAN for Virtual-to-Real Urban Scene Adaption
Authors: Peilun Li, Xiaodan Liang, Daoyuan Jia, Eric P. Xing
Date: 2018-01-05 00:00:00
Tasks: Domain Adaptation, Segmentation, Semantic Segmentation

Recent advances in vision tasks (e.g., segmentation) highly depend on the
availability of large-scale real-world image annotations obtained by cumbersome
human labors. Moreover, the perception performance often drops significantly
for new scenarios, due to the poor generalization capability of models trained
on limited and biased annotations. In this work, we resort to transfer
knowledge from automatically rendered scene annotations in virtual-world to
facilitate real-world visual tasks. Although virtual-world annotations can be
ideally diverse and unlimited, the discrepant data distributions between
virtual and real-world make it challenging for knowledge transferring. We thus
propose a novel Semantic-aware Grad-GAN (SG-GAN) to perform virtual-to-real
domain adaption with the ability of retaining vital semantic information.
Beyond the simple holistic color/texture transformation achieved by prior
works, SG-GAN successfully personalizes the appearance adaption for each
semantic region in order to preserve their key characteristic for better
recognition. It presents two main contributions to traditional GANs: 1) a soft
gradient-sensitive objective for keeping semantic boundaries; 2) a
semantic-aware discriminator for validating the fidelity of personalized
adaptions with respect to each semantic region. Qualitative and quantitative
experiments demonstrate the superiority of our SG-GAN in scene adaption over
state-of-the-art GANs. Further evaluations on semantic segmentation on
Cityscapes show using adapted virtual images by SG-GAN dramatically improves
segmentation performance than original virtual data. We release our code at
https://github.com/Peilun-Li/SG-GAN.
A Multi-task Learning Approach for Improving Product Title Compression with User Search Log Data
Authors: Jingang Wang, Junfeng Tian, Long Qiu, Sheng Li, Jun Lang, Luo Si, Man Lan
Date: 2018-01-05 00:00:00
Tasks: Decoder, Multi-Task Learning, Text Summarization

It is a challenging and practical research problem to obtain effective
compression of lengthy product titles for E-commerce. This is particularly
important as more and more users browse mobile E-commerce apps and more
merchants make the original product titles redundant and lengthy for Search
Engine Optimization. Traditional text summarization approaches often require a
large amount of preprocessing costs and do not capture the important issue of
conversion rate in E-commerce. This paper proposes a novel multi-task learning
approach for improving product title compression with user search log data. In
particular, a pointer network-based sequence-to-sequence approach is utilized
for title compression with an attentive mechanism as an extractive method and
an attentive encoder-decoder approach is utilized for generating user search
queries. The encoding parameters (i.e., semantic embedding of original titles)
are shared among the two tasks and the attention distributions are jointly
optimized. An extensive set of experiments with both human annotated data and
online deployment demonstrate the advantage of the proposed research for both
compression qualities and online business values.
Negative Binomial Matrix Factorization for Recommender Systems
Authors: Olivier Gouvert, Thomas Oberlin, Cédric Févotte
Date: 2018-01-05 00:00:00
Tasks: Binarization, Recommendation Systems

We introduce negative binomial matrix factorization (NBMF), a matrix
factorization technique specially designed for analyzing over-dispersed count
data. It can be viewed as an extension of Poisson matrix factorization (PF)
perturbed by a multiplicative term which models exposure. This term brings a
degree of freedom for controlling the dispersion, making NBMF more robust to
outliers. We show that NBMF allows to skip traditional pre-processing stages,
such as binarization, which lead to loss of information. Two estimation
approaches are presented: maximum likelihood and variational Bayes inference.
We test our model with a recommendation task and show its ability to predict
user tastes with better precision than PF.
Gatekeeping Algorithms with Human Ethical Bias: The ethics of algorithms in archives, libraries and society
Authors: Martijn van Otterlo
Date: 2018-01-05 00:00:00
Tasks: Ethics

In the age of algorithms, I focus on the question of how to ensure algorithms
that will take over many of our familiar archival and library tasks, will
behave according to human ethical norms that have evolved over many years. I
start by characterizing physical archives in the context of related
institutions such as libraries and museums. In this setting I analyze how
ethical principles, in particular about access to information, have been
formalized and communicated in the form of ethical codes, or: codes of
conducts. After that I describe two main developments: digitalization, in which
physical aspects of the world are turned into digital data, and
algorithmization, in which intelligent computer programs turn this data into
predictions and decisions. Both affect interactions that were once physical but
now digital. In this new setting I survey and analyze the ethical aspects of
algorithms and how they shape a vision on the future of archivists and
librarians, in the form of algorithmic documentalists, or: codementalists.
Finally I outline a general research strategy, called IntERMEeDIUM, to obtain
algorithms that obey are human ethical values encoded in code of ethics.
Moving Vehicle Detection Using AdaBoost and Haar-Like Feature in Surveillance Videos
Authors: Mohammad Mahdi Moghimi, Maryam Nayeri, Majid Pourahmadi, Mohammad Kazem Moghimi
Date: 2018-01-05 00:00:00
Tasks: Management, vehicle detection

Vehicle detection is a technology which its aim is to locate and show the
vehicle size in digital images. In this technology, vehicles are detected in
presence of other things like trees and buildings. It has an important role in
many computer vision applications such as vehicle tracking, analyzing the
traffic scene and efficient traffic management. In this paper, vehicles
detected based on the boosting technique by Viola Jones. Our proposed system is
tested in some real scenes of surveillance videos with different light
conditions. The experimental results show that the accuracy,completeness, and
quality of the proposed vehicle detection method are better than the previous
techniques (about 94%, 92%, and 87%, respectively). Thus, our proposed approach
is robust and efficient to detect vehicles in surveillance videos and their
applications.
Cross-Sensor Iris Recognition: LG4000-to-LG2200 Comparison
Authors: Nicolaie Popescu-Bodorin, Lucian Stefanita Grigore, Valentina Emilia Balas, Cristina Madalina Noaica, Ionut Axenie, Justinian Popa, Cristian Munteanu, Victor Stroescu, Ionut Manu, Alexandru Herea, Kartal Horasanli, Iulia Maria Motoc
Date: 2018-01-05 00:00:00
Tasks: Iris Recognition

Cross-sensor comparison experimental results reported here show that the
procedure defined and simulated during the Cross-Sensor Comparison Competition
2013 by our team for migrating / upgrading LG2200 based to LG4000 based
biometric systems leads to better LG4000-to-LG2200 cross-sensor iris
recognition results than previously reported, both in terms of user comfort and
in terms of system safety. On the other hand, LG2200-to-LG400 migration/upgrade
procedure defined and implemented by us is applicable to solve interoperability
issues between LG2200 based and LG4000 based systems, but also to other pairs
of systems having the same shift in the quality of acquired images.
Efficient Image Evidence Analysis of CNN Classification Results
Authors: Keyang Zhou, Bernhard Kainz
Date: 2018-01-05 00:00:00
Tasks: Classification, General Classification, Medical Image Analysis, Self-Driving Cars

Convolutional neural networks (CNNs) define the current state-of-the-art for
image recognition. With their emerging popularity, especially for critical
applications like medical image analysis or self-driving cars, confirmability
is becoming an issue. The black-box nature of trained predictors make it
difficult to trace failure cases or to understand the internal reasoning
processes leading to results. In this paper we introduce a novel efficient
method to visualise evidence that lead to decisions in CNNs. In contrast to
network fixation or saliency map methods, our method is able to illustrate the
evidence for or against a classifier's decision in input pixel space
approximately 10 times faster than previous methods. We also show that our
approach is less prone to noise and can focus on the most relevant input
regions, thus making it more accurate and interpretable. Moreover, by making
simplifications we link our method with other visualisation methods, providing
a general explanation for gradient-based visualisation techniques. We believe
that our work makes network introspection more feasible for debugging and
understanding deep convolutional networks. This will increase trust between
humans and deep learning models.
Accelerated Training for Massive Classification via Dynamic Class Selection
Authors: Xingcheng Zhang, Lei Yang, Junjie Yan, Dahua Lin
Date: 2018-01-05 00:00:00
Tasks: Classification, Face Recognition, General Classification

Massive classification, a classification task defined over a vast number of
classes (hundreds of thousands or even millions), has become an essential part
of many real-world systems, such as face recognition. Existing methods,
including the deep networks that achieved remarkable success in recent years,
were mostly devised for problems with a moderate number of classes. They would
meet with substantial difficulties, e.g. excessive memory demand and
computational cost, when applied to massive problems. We present a new method
to tackle this problem. This method can efficiently and accurately identify a
small number of "active classes" for each mini-batch, based on a set of dynamic
class hierarchies constructed on the fly. We also develop an adaptive
allocation scheme thereon, which leads to a better tradeoff between performance
and cost. On several large-scale benchmarks, our method significantly reduces
the training cost and memory demand, while maintaining competitive performance.
VulDeePecker: A Deep Learning-Based System for Vulnerability Detection
Authors: Zhen Li, Deqing Zou, Shouhuai Xu, Xinyu Ou, Hai Jin, Sujuan Wang, Zhijun Deng, Yuyi Zhong
Date: 2018-01-05 00:00:00
Tasks: Deep Learning, Vulnerability Detection

The automatic detection of software vulnerabilities is an important research
problem. However, existing solutions to this problem rely on human experts to
define features and often miss many vulnerabilities (i.e., incurring high false
negative rate). In this paper, we initiate the study of using deep
learning-based vulnerability detection to relieve human experts from the
tedious and subjective task of manually defining features. Since deep learning
is motivated to deal with problems that are very different from the problem of
vulnerability detection, we need some guiding principles for applying deep
learning to vulnerability detection. In particular, we need to find
representations of software programs that are suitable for deep learning. For
this purpose, we propose using code gadgets to represent programs and then
transform them into vectors, where a code gadget is a number of (not
necessarily consecutive) lines of code that are semantically related to each
other. This leads to the design and implementation of a deep learning-based
vulnerability detection system, called Vulnerability Deep Pecker
(VulDeePecker). In order to evaluate VulDeePecker, we present the first
vulnerability dataset for deep learning approaches. Experimental results show
that VulDeePecker can achieve much fewer false negatives (with reasonable false
positives) than other approaches. We further apply VulDeePecker to 3 software
products (namely Xen, Seamonkey, and Libav) and detect 4 vulnerabilities, which
are not reported in the National Vulnerability Database but were "silently"
patched by the vendors when releasing later versions of these products; in
contrast, these vulnerabilities are almost entirely missed by the other
vulnerability detection systems we experimented with.
Reasons and Means to Model Preferences as Incomplete
Authors: Olivier Cailloux, Sébastien Destercke
Date: 2018-01-05 00:00:00
Tasks: model, Relation

Literature involving preferences of artificial agents or human beings often
assume their preferences can be represented using a complete transitive binary
relation. Much has been written however on different models of preferences. We
review some of the reasons that have been put forward to justify more complex
modeling, and review some of the techniques that have been proposed to obtain
models of such preferences.
Program Evaluation and Causal Inference with High-Dimensional Data
Authors: Alexandre Belloni, Victor Chernozhukov, Ivan Fernández-Val, Christian Hansen
Date: 2013-11-11 00:00:00
Tasks: Causal Inference, valid, Vocal Bursts Intensity Prediction

In this paper, we provide efficient estimators and honest confidence bands
for a variety of treatment effects including local average (LATE) and local
quantile treatment effects (LQTE) in data-rich environments. We can handle very
many control variables, endogenous receipt of treatment, heterogeneous
treatment effects, and function-valued outcomes. Our framework covers the
special case of exogenous receipt of treatment, either conditional on controls
or unconditionally as in randomized control trials. In the latter case, our
approach produces efficient estimators and honest bands for (functional)
average treatment effects (ATE) and quantile treatment effects (QTE). To make
informative inference possible, we assume that key reduced form predictive
relationships are approximately sparse. This assumption allows the use of
regularization and selection methods to estimate those relations, and we
provide methods for post-regularization and post-selection inference that are
uniformly valid (honest) across a wide-range of models. We show that a key
ingredient enabling honest inference is the use of orthogonal or doubly robust
moment conditions in estimating certain reduced form functional parameters. We
illustrate the use of the proposed methods with an application to estimating
the effect of 401(k) eligibility and participation on accumulated assets.