pwc-archive/papers-with-abstracts huggingface dataset:
Are Generative Classifiers More Robust to Adversarial Attacks?
Authors: Yingzhen Li, John Bradshaw, Yash Sharma
Date: 2018-02-19 00:00:00
Tasks: Adversarial Defense, Adversarial Robustness

There is a rising interest in studying the robustness of deep neural network classifiers against adversaries, with both advanced attack and defence techniques being actively developed. However, most recent work focuses on discriminative classifiers, which only model the conditional distribution of the labels given the inputs. In this paper, we propose and investigate the deep Bayes classifier, which improves classical naive Bayes with conditional deep generative models. We further develop detection methods for adversarial examples, which reject inputs with low likelihood under the generative model. Experimental results suggest that deep Bayes classifiers are more robust than deep discriminative classifiers, and that the proposed detection methods are effective against many recently proposed attacks.
Pseudorehearsal in actor-critic agents with neural network function approximation
Authors: Vladimir Marochko, Leonard Johard, Manuel Mazzara, Luca Longo
Date: 2017-12-20 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Catastrophic forgetting has a significant negative impact in reinforcement
learning. The purpose of this study is to investigate how pseudorehearsal can
change performance of an actor-critic agent with neural-network function
approximation. We tested agent in a pole balancing task and compared different
pseudorehearsal approaches. We have found that pseudorehearsal can assist
learning and decrease forgetting.
On Estimating Multi-Attribute Choice Preferences using Private Signals and Matrix Factorization
Authors: Venkata Sriram Siddhardh Nadendla, Cedric Langbort
Date: 2018-02-19 00:00:00
Tasks: Attribute

Revealed preference theory studies the possibility of modeling an agent's
revealed preferences and the construction of a consistent utility function.
However, modeling agent's choices over preference orderings is not always
practical and demands strong assumptions on human rationality and
data-acquisition abilities. Therefore, we propose a simple generative choice
model where agents are assumed to generate the choice probabilities based on
latent factor matrices that capture their choice evaluation across multiple
attributes. Since the multi-attribute evaluation is typically hidden within the
agent's psyche, we consider a signaling mechanism where agents are provided
with choice information through private signals, so that the agent's choices
provide more insight about his/her latent evaluation across multiple
attributes. We estimate the choice model via a novel multi-stage matrix
factorization algorithm that minimizes the average deviation of the factor
estimates from choice data. Simulation results are presented to validate the
estimation performance of our proposed algorithm.
Boosting Image Forgery Detection using Resampling Features and Copy-move analysis
Authors: Tajuddin Manhar Mohammed, Jason Bunk, Lakshmanan Nataraj, Jawadul H. Bappy, Arjuna Flenner, B. S. Manjunath, Shivkumar Chandrasekaran, Amit K. Roy-Chowdhury, Lawrence Peterson
Date: 2018-02-09 00:00:00
Tasks: Image Forgery Detection, Image Manipulation, Image Manipulation Detection

Realistic image forgeries involve a combination of splicing, resampling,
cloning, region removal and other methods. While resampling detection
algorithms are effective in detecting splicing and resampling, copy-move
detection algorithms excel in detecting cloning and region removal. In this
paper, we combine these complementary approaches in a way that boosts the
overall accuracy of image manipulation detection. We use the copy-move
detection method as a pre-filtering step and pass those images that are
classified as untampered to a deep learning based resampling detection
framework. Experimental results on various datasets including the 2017 NIST
Nimble Challenge Evaluation dataset comprising nearly 10,000 pristine and
tampered images shows that there is a consistent increase of 8%-10% in
detection rates, when copy-move algorithm is combined with different resampling
detection algorithms.
Weighted Linear Discriminant Analysis based on Class Saliency Information
Authors: Lei Xu, Alexandros Iosifidis, Moncef Gabbouj
Date: 2018-02-19 00:00:00
Tasks: General Classification, image-classification, Image Classification, Saliency Prediction

In this paper, we propose a new variant of Linear Discriminant Analysis to
overcome underlying drawbacks of traditional LDA and other LDA variants
targeting problems involving imbalanced classes. Traditional LDA sets
assumptions related to Gaussian class distribution and neglects influence of
outlier classes, that might hurt in performance. We exploit intuitions coming
from a probabilistic interpretation of visual saliency estimation in order to
define saliency of a class in multi-class setting. Such information is then
used to redefine the between-class and within-class scatters in a more robust
manner. Compared to traditional LDA and other weight-based LDA variants, the
proposed method has shown certain improvements on facial image classification
problems in publicly available datasets.
Meta-Learning for Resampling Recommendation Systems
Authors: Smolyakov Dmitry, Alexander Korotin, Pavel Erofeev, Artem Papanov, Evgeny Burnaev
Date: 2017-06-06 00:00:00
Tasks: Classification, General Classification, Meta-Learning, Recommendation Systems

One possible approach to tackle the class imbalance in classification tasks
is to resample a training dataset, i.e., to drop some of its elements or to
synthesize new ones. There exist several widely-used resampling methods. Recent
research showed that the choice of resampling method significantly affects the
quality of classification, which raises resampling selection problem.
Exhaustive search for optimal resampling is time-consuming and hence it is of
limited use. In this paper, we describe an alternative approach to the
resampling selection. We follow the meta-learning concept to build resampling
recommendation systems, i.e., algorithms recommending resampling for datasets
on the basis of their properties.
Satellite imagery analysis for operational damage assessment in Emergency situations
Authors: Alexey Trekin, German Novikov, Georgy Potapov, Vladimir Ignatiev, Evgeny Burnaev
Date: 2018-02-19 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Humanitarian, Management

When major disaster occurs the questions are raised how to estimate the
damage in time to support the decision making process and relief efforts by
local authorities or humanitarian teams. In this paper we consider the use of
Machine Learning and Computer Vision on remote sensing imagery to improve time
efficiency of assessment of damaged buildings in disaster affected area. We
propose a general workflow that can be useful in various disaster management
applications, and demonstrate the use of the proposed workflow for the
assessment of the damage caused by the wildfires in California in 2017.
Steering Social Activity: A Stochastic Optimal Control Point Of View
Authors: Ali Zarezade, Abir De, Utkarsh Upadhyay, Hamid R. Rabiee, Manuel Gomez-Rodriguez
Date: 2018-02-19 00:00:00
Tasks: Point Processes

User engagement in online social networking depends critically on the level
of social activity in the corresponding platform--the number of online actions,
such as posts, shares or replies, taken by their users. Can we design
data-driven algorithms to increase social activity? At a user level, such
algorithms may increase activity by helping users decide when to take an action
to be more likely to be noticed by their peers. At a network level, they may
increase activity by incentivizing a few influential users to take more
actions, which in turn will trigger additional actions by other users. In this
paper, we model social activity using the framework of marked temporal point
processes, derive an alternate representation of these processes using
stochastic differential equations (SDEs) with jumps and, exploiting this
alternate representation, develop two efficient online algorithms with provable
guarantees to steer social activity both at a user and at a network level. In
doing so, we establish a previously unexplored connection between optimal
control of jump SDEs and doubly stochastic marked temporal point processes,
which is of independent interest. Finally, we experiment both with synthetic
and real data gathered from Twitter and show that our algorithms consistently
steer social activity more effectively than the state of the art.
Can you tell where in India I am from? Comparing humans and computers on fine-grained race face classification
Authors: Harish Katti, S. P. Arun
Date: 2017-03-22 00:00:00
Tasks: Gender Classification, General Classification, object-detection, Object Detection

Faces form the basis for a rich variety of judgments in humans, yet the
underlying features remain poorly understood. Although fine-grained
distinctions within a race might more strongly constrain possible facial
features used by humans than in case of coarse categories such as race or
gender, such fine grained distinctions are relatively less studied.
Fine-grained race classification is also interesting because even humans may
not be perfectly accurate on these tasks. This allows us to compare errors made
by humans and machines, in contrast to standard object detection tasks where
human performance is nearly perfect. We have developed a novel face database of
close to 1650 diverse Indian faces labeled for fine-grained race (South vs
North India) as well as for age, weight, height and gender. We then asked close
to 130 human subjects who were instructed to categorize each face as belonging
toa Northern or Southern state in India. We then compared human performance on
this task with that of computational models trained on the ground-truth labels.
Our main results are as follows: (1) Humans are highly consistent (average
accuracy: 63.6%), with some faces being consistently classified with > 90%
accuracy and others consistently misclassified with < 30% accuracy; (2) Models
trained on ground-truth labels showed slightly worse performance (average
accuracy: 62%) but showed higher accuracy (72.2%) on faces classified with >
80% accuracy by humans. This was true for models trained on simple spatial and
intensity measurements extracted from faces as well as deep neural networks
trained on race or gender classification; (3) Using overcomplete banks of
features derived from each face part, we found that mouth shape was the single
largest contributor towards fine-grained race classification, whereas distances
between face parts was the strongest predictor of gender.
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Authors: Jun-Yan Zhu, Taesung Park, Phillip Isola, Alexei A. Efros
Date: 2017-03-30 00:00:00
Proceeding: ICCV 2017 10
Tasks: Image Colorization, Image-to-Image Translation, Multimodal Unsupervised Image-To-Image Translation, Style Transfer, Translation, Unsupervised Image-To-Image Translation

Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. However, for many tasks, paired training data will not be available. We present an approach for learning to translate an image from a source domain $X$ to a target domain $Y$ in the absence of paired examples. Our goal is to learn a mapping $G: X \rightarrow Y$ such that the distribution of images from $G(X)$ is indistinguishable from the distribution $Y$ using an adversarial loss. Because this mapping is highly under-constrained, we couple it with an inverse mapping $F: Y \rightarrow X$ and introduce a cycle consistency loss to push $F(G(X)) \approx X$ (and vice versa). Qualitative results are presented on several tasks where paired training data does not exist, including collection style transfer, object transfiguration, season transfer, photo enhancement, etc. Quantitative comparisons against several prior methods demonstrate the superiority of our approach.
Heron Inference for Bayesian Graphical Models
Authors: Daniel Rugeles, Zhen Hai, Gao Cong, Manoranjan Dash
Date: 2018-02-19 00:00:00
Tasks: Computational Efficiency, Variational Inference

Bayesian graphical models have been shown to be a powerful tool for
discovering uncertainty and causal structure from real-world data in many
application fields. Current inference methods primarily follow different kinds
of trade-offs between computational complexity and predictive accuracy. At one
end of the spectrum, variational inference approaches perform well in
computational efficiency, while at the other end, Gibbs sampling approaches are
known to be relatively accurate for prediction in practice. In this paper, we
extend an existing Gibbs sampling method, and propose a new deterministic Heron
inference (Heron) for a family of Bayesian graphical models. In addition to the
support for nontrivial distributability, one more benefit of Heron is that it
is able to not only allow us to easily assess the convergence status but also
largely improve the running efficiency. We evaluate Heron against the standard
collapsed Gibbs sampler and state-of-the-art state augmentation method in
inference for well-known graphical models. Experimental results using publicly
available real-life data have demonstrated that Heron significantly outperforms
the baseline methods for inferring Bayesian graphical models.
Human and Smart Machine Co-Learning with Brain Computer Interface
Authors: Chang-Shing Lee, Mei-Hui Wang, Li-Wei Ko, Naoyuki Kubota, Lu-An Lin, Shinya Kitaoka, Yu-Te Wang, Shun-Feng Su
Date: 2018-02-19 00:00:00
Tasks: Brain Computer Interface

Machine learning has become a very popular approach for cybernetics systems,
and it has always been considered important research in the Computational
Intelligence area. Nevertheless, when it comes to smart machines, it is not
just about the methodologies. We need to consider systems and cybernetics as
well as include human in the loop. The purpose of this article is as follows:
(1) To integrate the open source Facebook AI Research (FAIR) DarkForest program
of Facebook with Item Response Theory (IRT), to the new open learning system,
namely, DDF learning system; (2) To integrate DDF Go with Robot namely Robotic
DDF Go system; (3) To invite the professional Go players to attend the activity
to play Go games on site with a smart machine. The research team will apply
this technology to education, such as, playing games to enhance the children
concentration on learning mathematics, languages, and other topics. With the
detected brainwaves, the robot will be able to speak some words that are very
much to the point for the students and to assist the teachers in classroom in
the future.
Neural Language Modeling by Jointly Learning Syntax and Lexicon
Authors: Yikang Shen, Zhouhan Lin, Chin-wei Huang, Aaron Courville
Date: 2017-11-02 00:00:00
Proceeding: ICLR 2018 1
Tasks: Constituency Grammar Induction, Language Modeling, Language Modelling

We propose a neural language model capable of unsupervised syntactic
structure induction. The model leverages the structure information to form
better semantic representations and better language modeling. Standard
recurrent neural networks are limited by their structure and fail to
efficiently use syntactic information. On the other hand, tree-structured
recursive networks usually require additional structural supervision at the
cost of human expert annotation. In this paper, We propose a novel neural
language model, called the Parsing-Reading-Predict Networks (PRPN), that can
simultaneously induce the syntactic structure from unannotated sentences and
leverage the inferred structure to learn a better language model. In our model,
the gradient can be directly back-propagated from the language model loss into
the neural parsing network. Experiments show that the proposed model can
discover the underlying syntactic structure and achieve state-of-the-art
performance on word/character-level language model tasks.
Image Forensics: Detecting duplication of scientific images with manipulation-invariant image similarity
Authors: M. Cicconet, H. Elliott, D. L. Richmond, D. Wainstock, M. Walsh
Date: 2018-02-19 00:00:00
Tasks: Image Forensics, Image Manipulation, Translation

Manipulation and re-use of images in scientific publications is a concerning problem that currently lacks a scalable solution. Current tools for detecting image duplication are mostly manual or semi-automated, despite the availability of an overwhelming target dataset for a learning-based approach. This paper addresses the problem of determining if, given two images, one is a manipulated version of the other by means of copy, rotation, translation, scale, perspective transform, histogram adjustment, or partial erasing. We propose a data-driven solution based on a 3-branch Siamese Convolutional Neural Network. The ConvNet model is trained to map images into a 128-dimensional space, where the Euclidean distance between duplicate images is smaller than or equal to 1, and the distance between unique images is greater than 1. Our results suggest that such an approach has the potential to improve surveillance of the published and in-peer-review literature for image manipulation.
Motion Compensated Dynamic MRI Reconstruction with Local Affine Optical Flow Estimation
Authors: Ningning Zhao, Daniel O'Connor, Adrian Basarab, Dan Ruan, Peng Hu, Ke Sheng
Date: 2017-07-22 00:00:00
Tasks: compressed sensing, Image Reconstruction, Motion Compensation, Motion Estimation, MRI Reconstruction, Optical Flow Estimation

This paper proposes a novel framework to reconstruct the dynamic magnetic
resonance images (DMRI) with motion compensation (MC). Due to the inherent
motion effects during DMRI acquisition, reconstruction of DMRI using motion
estimation/compensation (ME/MC) has been studied under a compressed sensing
(CS) scheme. In this paper, by embedding the intensity-based optical flow (OF)
constraint into the traditional CS scheme, we are able to couple the DMRI
reconstruction with motion field estimation. The formulated optimization
problem is solved by a primal-dual algorithm with linesearch due to its
efficiency when dealing with non-differentiable problems. With the estimated
motion field, the DMRI reconstruction is refined through MC. By employing the
multi-scale coarse-to-fine strategy, we are able to update the
variables(temporal image sequences and motion vectors) and to refine the image
reconstruction alternately. Moreover, the proposed framework is capable of
handling a wide class of prior information (regularizations) for DMRI
reconstruction, such as sparsity, low rank and total variation. Experiments on
various DMRI data, ranging from in vivo lung to cardiac dataset, validate the
reconstruction quality improvement using the proposed scheme in comparison to
several state-of-the-art algorithms.
Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object Detection
Authors: Alexander Wong, Mohammad Javad Shafiee, Francis Li, Brendan Chwyl
Date: 2018-02-19 00:00:00
Tasks: Object, object-detection, Object Detection, Object Localization, Real-Time Object Detection

Object detection is a major challenge in computer vision, involving both
object classification and object localization within a scene. While deep neural
networks have been shown in recent years to yield very powerful techniques for
tackling the challenge of object detection, one of the biggest challenges with
enabling such object detection networks for widespread deployment on embedded
devices is high computational and memory requirements. Recently, there has been
an increasing focus in exploring small deep neural network architectures for
object detection that are more suitable for embedded devices, such as Tiny YOLO
and SqueezeDet. Inspired by the efficiency of the Fire microarchitecture
introduced in SqueezeNet and the object detection performance of the
single-shot detection macroarchitecture introduced in SSD, this paper
introduces Tiny SSD, a single-shot detection deep convolutional neural network
for real-time embedded object detection that is composed of a highly optimized,
non-uniform Fire sub-network stack and a non-uniform sub-network stack of
highly optimized SSD-based auxiliary convolutional feature layers designed
specifically to minimize model size while maintaining object detection
performance. The resulting Tiny SSD possess a model size of 2.3MB (~26X smaller
than Tiny YOLO) while still achieving an mAP of 61.3% on VOC 2007 (~4.2% higher
than Tiny YOLO). These experimental results show that very small deep neural
network architectures can be designed for real-time object detection that are
well-suited for embedded scenarios.
Approximate message passing for nonconvex sparse regularization with stability and asymptotic analysis
Authors: Ayaka Sakata, Yingying Xu
Date: 2017-11-08 00:00:00

We analyse a linear regression problem with nonconvex regularization called
smoothly clipped absolute deviation (SCAD) under an overcomplete Gaussian basis
for Gaussian random data. We propose an approximate message passing (AMP)
algorithm considering nonconvex regularization, namely SCAD-AMP, and
analytically show that the stability condition corresponds to the de
Almeida--Thouless condition in spin glass literature. Through asymptotic
analysis, we show the correspondence between the density evolution of SCAD-AMP
and the replica symmetric solution. Numerical experiments confirm that for a
sufficiently large system size, SCAD-AMP achieves the optimal performance
predicted by the replica method. Through replica analysis, a phase transition
between replica symmetric (RS) and replica symmetry breaking (RSB) region is
found in the parameter space of SCAD. The appearance of the RS region for a
nonconvex penalty is a significant advantage that indicates the region of
smooth landscape of the optimization problem. Furthermore, we analytically show
that the statistical representation performance of the SCAD penalty is better
than that of L1-based methods, and the minimum representation error under RS
assumption is obtained at the edge of the RS/RSB phase. The correspondence
between the convergence of the existing coordinate descent algorithm and RS/RSB
transition is also indicated.
Accelerated Primal-Dual Policy Optimization for Safe Reinforcement Learning
Authors: Qingkai Liang, Fanyu Que, Eytan Modiano
Date: 2018-02-19 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Safe Reinforcement Learning

Constrained Markov Decision Process (CMDP) is a natural framework for
reinforcement learning tasks with safety constraints, where agents learn a
policy that maximizes the long-term reward while satisfying the constraints on
the long-term cost. A canonical approach for solving CMDPs is the primal-dual
method which updates parameters in primal and dual spaces in turn. Existing
methods for CMDPs only use on-policy data for dual updates, which results in
sample inefficiency and slow convergence. In this paper, we propose a policy
search method for CMDPs called Accelerated Primal-Dual Optimization (APDO),
which incorporates an off-policy trained dual variable in the dual update
procedure while updating the policy in primal space with on-policy likelihood
ratio gradient. Experimental results on a simulated robot locomotion task show
that APDO achieves better sample efficiency and faster convergence than
state-of-the-art approaches for CMDPs.