pwc-archive/papers-with-abstracts huggingface dataset:
Personalization in Goal-Oriented Dialog
Authors: Chaitanya K. Joshi, Fei Mi, Boi Faltings
Date: 2017-06-22 00:00:00
Tasks: Goal-Oriented Dialog, Multi-Task Learning

The main goal of modeling human conversation is to create agents which can
interact with people in both open-ended and goal-oriented scenarios. End-to-end
trained neural dialog systems are an important line of research for such
generalized dialog models as they do not resort to any situation-specific
handcrafting of rules. However, incorporating personalization into such systems
is a largely unexplored topic as there are no existing corpora to facilitate
such work. In this paper, we present a new dataset of goal-oriented dialogs
which are influenced by speaker profiles attached to them. We analyze the
shortcomings of an existing end-to-end dialog system based on Memory Networks
and propose modifications to the architecture which enable personalization. We
also investigate personalization in dialog as a multi-task learning problem,
and show that a single model which shares features among various profiles
outperforms separate models for each profile.
Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning
Authors: Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, Dawn Song
Date: 2017-12-15 00:00:00
Tasks: Data Poisoning, Deep Learning, Face Recognition

Deep learning models have achieved high performance on many tasks, and thus
have been applied to many security-critical scenarios. For example, deep
learning-based face recognition systems have been used to authenticate users to
access many security-sensitive applications like payment apps. Such usages of
deep learning systems provide the adversaries with sufficient incentives to
perform attacks against these systems for their adversarial purposes. In this
work, we consider a new type of attacks, called backdoor attacks, where the
attacker's goal is to create a backdoor into a learning-based authentication
system, so that he can easily circumvent the system by leveraging the backdoor.
Specifically, the adversary aims at creating backdoor instances, so that the
victim learning system will be misled to classify the backdoor instances as a
target label specified by the adversary. In particular, we study backdoor
poisoning attacks, which achieve backdoor attacks using poisoning strategies.
Different from all existing work, our studied poisoning strategies can apply
under a very weak threat model: (1) the adversary has no knowledge of the model
and the training set used by the victim system; (2) the attacker is allowed to
inject only a small amount of poisoning samples; (3) the backdoor key is hard
to notice even by human beings to achieve stealthiness. We conduct evaluation
to demonstrate that a backdoor adversary can inject only around 50 poisoning
samples, while achieving an attack success rate of above 90%. We are also the
first work to show that a data poisoning attack can create physically
implementable backdoors without touching the training process. Our work
demonstrates that backdoor poisoning attacks pose real threats to a learning
system, and thus highlights the importance of further investigation and
proposing defense strategies against them.
Inverse Reinforce Learning with Nonparametric Behavior Clustering
Authors: Siddharthan Rajasekaran, Jinwei Zhang, Jie Fu
Date: 2017-12-15 00:00:00
Tasks: Clustering, Reinforcement Learning

Inverse Reinforcement Learning (IRL) is the task of learning a single reward
function given a Markov Decision Process (MDP) without defining the reward
function, and a set of demonstrations generated by humans/experts. However, in
practice, it may be unreasonable to assume that human behaviors can be
explained by one reward function since they may be inherently inconsistent.
Also, demonstrations may be collected from various users and aggregated to
infer and predict user's behaviors. In this paper, we introduce the
Non-parametric Behavior Clustering IRL algorithm to simultaneously cluster
demonstrations and learn multiple reward functions from demonstrations that may
be generated from more than one behaviors. Our method is iterative: It
alternates between clustering demonstrations into different behavior clusters
and inverse learning the reward functions until convergence. It is built upon
the Expectation-Maximization formulation and non-parametric clustering in the
IRL setting. Further, to improve the computation efficiency, we remove the need
of completely solving multiple IRL problems for multiple clusters during the
iteration steps and introduce a resampling technique to avoid generating too
many unlikely clusters. We demonstrate the convergence and efficiency of the
proposed method through learning multiple driver behaviors from demonstrations
generated from a grid-world environment and continuous trajectories collected
from autonomous robot cars using the Gazebo robot simulator.
Highly Efficient Human Action Recognition with Quantum Genetic Algorithm Optimized Support Vector Machine
Authors: Yafeng Liu, Shimin Feng, Zhikai Zhao, Enjie Ding
Date: 2017-11-27 00:00:00
Tasks: Action Classification, Action Recognition, General Classification, Temporal Action Localization

In this paper we propose the use of quantum genetic algorithm to optimize the
support vector machine (SVM) for human action recognition. The Microsoft Kinect
sensor can be used for skeleton tracking, which provides the joints' position
data. However, how to extract the motion features for representing the dynamics
of a human skeleton is still a challenge due to the complexity of human motion.
We present a highly efficient features extraction method for action
classification, that is, using the joint angles to represent a human skeleton
and calculating the variance of each angle during an action time window. Using
the proposed representation, we compared the human action classification
accuracy of two approaches, including the optimized SVM based on quantum
genetic algorithm and the conventional SVM with grid search. Experimental
results on the MSR-12 dataset show that the conventional SVM achieved an
accuracy of $ 93.85\% $. The proposed approach outperforms the conventional
method with an accuracy of $ 96.15\% $.
Data Clustering using a Hybrid of Fuzzy C-Means and Quantum-behaved Particle Swarm Optimization
Authors: Saptarshi Sengupta, Sanchita Basak, Richard Alan Peters II
Date: 2017-12-15 00:00:00
Tasks: Clustering, Quantization

Fuzzy clustering has become a widely used data mining technique and plays an
important role in grouping, traversing and selectively using data for user
specified applications. The deterministic Fuzzy C-Means (FCM) algorithm may
result in suboptimal solutions when applied to multidimensional data in
real-world, time-constrained problems. In this paper the Quantum-behaved
Particle Swarm Optimization (QPSO) with a fully connected topology is coupled
with the Fuzzy C-Means Clustering algorithm and is tested on a suite of
datasets from the UCI Machine Learning Repository. The global search ability of
the QPSO algorithm helps in avoiding stagnation in local optima while the soft
clustering approach of FCM helps to partition data based on membership
probabilities. Clustering performance indices such as F-Measure, Accuracy,
Quantization Error, Intercluster and Intracluster distances are reported for
competitive techniques such as PSO K-Means, QPSO K-Means and QPSO FCM over all
datasets considered. Experimental results indicate that QPSO FCM provides
comparable and in most cases superior results when compared to the others.
Graph-Sparse Logistic Regression
Authors: Alexander LeNail, Ludwig Schmidt, Johnathan Li, Tobias Ehrenberger, Karen Sachs, Stefanie Jegelka, Ernest Fraenkel
Date: 2017-12-15 00:00:00
Tasks: General Classification, regression

We introduce Graph-Sparse Logistic Regression, a new algorithm for
classification for the case in which the support should be sparse but connected
on a graph. We val- idate this algorithm against synthetic data and benchmark
it against L1-regularized Logistic Regression. We then explore our technique in
the bioinformatics context of proteomics data on the interactome graph. We make
all our experimental code public and provide GSLR as an open source package.
What Can This Robot Do? Learning from Appearance and Experiments
Authors: Ashwin Khadke, Manuela Veloso
Date: 2017-12-15 00:00:00

When presented with an unknown robot (subject) how can an autonomous agent
(learner) figure out what this new robot can do? The subject's appearance can
provide cues to its physical as well as cognitive capabilities. Seeing a
humanoid can make one wonder if it can kick balls, climb stairs or recognize
faces. What if the learner can request the subject to perform these tasks? We
present an approach to make the learner build a model of the subject at a task
based on the latter's appearance and refine it by experimentation. Apart from
the subject's inherent capabilities, certain extrinsic factors may affect its
performance at a task. Based on the subject's appearance and prior knowledge
about the task a learner can identify a set of potential factors, a subset of
which we assume are controllable. Our approach picks values of controllable
factors to generate the most informative experiments to test the subject at.
Additionally, we present a metric to determine if a factor should be
incorporated in the model. We present results of our approach on modeling a
humanoid robot at the task of kicking a ball. Firstly, we show that actively
picking values for controllable factors, even in noisy experiments, leads to
faster learning of the subject's model for the task. Secondly, starting from a
minimal set of factors our metric identifies the set of relevant factors to
incorporate in the model. Lastly, we show that the refined model better
represents the subject's performance at the task.
Learning when to skim and when to read
Authors: Alexander Rosenberg Johansen, Richard Socher
Date: 2017-12-15 00:00:00
Proceeding: WS 2017 8
Tasks: General Classification, Sentiment Analysis, Sentiment Classification

Many recent advances in deep learning for natural language processing have
come at increasing computational cost, but the power of these state-of-the-art
models is not needed for every example in a dataset. We demonstrate two
approaches to reducing unnecessary computation in cases where a fast but weak
baseline classier and a stronger, slower model are both available. Applying an
AUC-based metric to the task of sentiment classification, we find significant
efficiency gains with both a probability-threshold method for reducing
computational cost and one that uses a secondary decision network.
Visual Based Navigation of Mobile Robots
Authors: Shailja, Soumabh Bhowmick, Jayanta Mukhopadhyay
Date: 2017-12-15 00:00:00
Tasks: Clustering

We have developed an algorithm to generate a complete map of the traversable
region for a personal assistant robot using monocular vision only. Using
multiple taken by a simple webcam, obstacle detection and avoidance algorithms
have been developed. Simple Linear Iterative Clustering (SLIC) has been used
for segmentation to reduce the memory and computation cost. A simple mapping
technique using inverse perspective mapping and occupancy grids, which is
robust, and supports very fast updates has been used to create the map for
indoor navigation.
AI2-THOR: An Interactive 3D Environment for Visual AI
Authors: Eric Kolve, Roozbeh Mottaghi, Winson Han, Eli VanderBilt, Luca Weihs, Alvaro Herrasti, Matt Deitke, Kiana Ehsani, Daniel Gordon, Yuke Zhu, Aniruddha Kembhavi, Abhinav Gupta, Ali Farhadi
Date: 2017-12-14 00:00:00
Tasks: Deep Reinforcement Learning, Imitation Learning, Navigate, object-detection, Object Detection, Question Answering, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning, Visual Question Answering, Visual Question Answering (VQA)

We introduce The House Of inteRactions (THOR), a framework for visual AI research, available at http://ai2thor.allenai.org. AI2-THOR consists of near photo-realistic 3D indoor scenes, where AI agents can navigate in the scenes and interact with objects to perform tasks. AI2-THOR enables research in many different domains including but not limited to deep reinforcement learning, imitation learning, learning by interaction, planning, visual question answering, unsupervised representation learning, object detection and segmentation, and learning models of cognition. The goal of AI2-THOR is to facilitate building visually intelligent models and push the research forward in this domain.
Inferring the parameters of a Markov process from snapshots of the steady state
Authors: Simon Lee Dettmer, Johannes Berg
Date: 2017-07-13 00:00:00

We seek to infer the parameters of an ergodic Markov process from samples
taken independently from the steady state. Our focus is on non-equilibrium
processes, where the steady state is not described by the Boltzmann measure,
but is generally unknown and hard to compute, which prevents the application of
established equilibrium inference methods. We propose a quantity we call
propagator likelihood, which takes on the role of the likelihood in equilibrium
processes. This propagator likelihood is based on fictitious transitions
between those configurations of the system which occur in the samples. The
propagator likelihood can be derived by minimising the relative entropy between
the empirical distribution and a distribution generated by propagating the
empirical distribution forward in time. Maximising the propagator likelihood
leads to an efficient reconstruction of the parameters of the underlying model
in different systems, both with discrete configurations and with continuous
configurations. We apply the method to non-equilibrium models from statistical
physics and theoretical biology, including the asymmetric simple exclusion
process (ASEP), the kinetic Ising model, and replicator dynamics.
RAN4IQA: Restorative Adversarial Nets for No-Reference Image Quality Assessment
Authors: Hongyu Ren, Diqi Chen, Yizhou Wang
Date: 2017-12-14 00:00:00
Tasks: Image Quality Assessment, No-Reference Image Quality Assessment, NR-IQA, Perceptual Distance

Inspired by the free-energy brain theory, which implies that human visual
system (HVS) tends to reduce uncertainty and restore perceptual details upon
seeing a distorted image, we propose restorative adversarial net (RAN), a
GAN-based model for no-reference image quality assessment (NR-IQA). RAN, which
mimics the process of HVS, consists of three components: a restorator, a
discriminator and an evaluator. The restorator restores and reconstructs input
distorted image patches, while the discriminator distinguishes the
reconstructed patches from the pristine distortion-free patches. After
restoration, we observe that the perceptual distance between the restored and
the distorted patches is monotonic with respect to the distortion level. We
further define Gain of Restoration (GoR) based on this phenomenon. The
evaluator predicts perceptual score by extracting feature representations from
the distorted and restored patches to measure GoR. Eventually, the quality
score of an input image is estimated by weighted sum of the patch scores.
Experimental results on Waterloo Exploration, LIVE and TID2013 show the
effectiveness and generalization ability of RAN compared to the
state-of-the-art NR-IQA models.
Nonparametric Neural Networks
Authors: George Philipp, Jaime G. Carbonell
Date: 2017-12-14 00:00:00

Automatically determining the optimal size of a neural network for a given
task without prior information currently requires an expensive global search
and training many networks from scratch. In this paper, we address the problem
of automatically finding a good network size during a single training cycle. We
introduce *nonparametric neural networks*, a non-probabilistic framework for
conducting optimization over all possible network sizes and prove its soundness
when network growth is limited via an L_p penalty. We train networks under this
framework by continuously adding new units while eliminating redundant units
via an L_2 penalty. We employ a novel optimization algorithm, which we term
*adaptive radial-angular gradient descent* or *AdaRad*, and obtain promising
results.
Stochastic Particle Gradient Descent for Infinite Ensembles
Authors: Atsushi Nitanda, Taiji Suzuki
Date: 2017-12-14 00:00:00
Tasks: Ensemble Learning, Stochastic Optimization

The superior performance of ensemble methods with infinite models are well
known. Most of these methods are based on optimization problems in
infinite-dimensional spaces with some regularization, for instance, boosting
methods and convex neural networks use $L^1$-regularization with the
non-negative constraint. However, due to the difficulty of handling
$L^1$-regularization, these problems require early stopping or a rough
approximation to solve it inexactly. In this paper, we propose a new ensemble
learning method that performs in a space of probability measures, that is, our
method can handle the $L^1$-constraint and the non-negative constraint in a
rigorous way. Such an optimization is realized by proposing a general purpose
stochastic optimization method for learning probability measures via
parameterization using transport maps on base models. As a result of running
the method, a transport map to output an infinite ensemble is obtained, which
forms a residual-type network. From the perspective of functional gradient
methods, we give a convergence rate as fast as that of a stochastic
optimization method for finite dimensional nonconvex problems. Moreover, we
show an interior optimality property of a local optimality condition used in
our analysis.
ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
Authors: Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, Ronald M. Summers
Date: 2017-05-05 00:00:00
Proceeding: CVPR 2017 7
Tasks: General Classification, image-classification, Image Classification, Lung Disease Classification, Multi-Label Image Classification, Open-Ended Question Answering, Weakly Supervised Classification

The chest X-ray is one of the most commonly accessible radiological
examinations for screening and diagnosis of many lung diseases. A tremendous
number of X-ray imaging studies accompanied by radiological reports are
accumulated and stored in many modern hospitals' Picture Archiving and
Communication Systems (PACS). On the other side, it is still an open question
how this type of hospital-size knowledge database containing invaluable imaging
informatics (i.e., loosely labeled) can be used to facilitate the data-hungry
deep learning paradigms in building truly large-scale high precision
computer-aided diagnosis (CAD) systems.
  In this paper, we present a new chest X-ray database, namely "ChestX-ray8",
which comprises 108,948 frontal-view X-ray images of 32,717 unique patients
with the text-mined eight disease image labels (where each image can have
multi-labels), from the associated radiological reports using natural language
processing. Importantly, we demonstrate that these commonly occurring thoracic
diseases can be detected and even spatially-located via a unified
weakly-supervised multi-label image classification and disease localization
framework, which is validated using our proposed dataset. Although the initial
quantitative results are promising as reported, deep convolutional neural
network based "reading chest X-rays" (i.e., recognizing and locating the common
disease patterns trained with only image-level labels) remains a strenuous task
for fully-automated high precision CAD systems. Data download link:
https://nihcc.app.box.com/v/ChestXray-NIHCC
DANCin SEQ2SEQ: Fooling Text Classifiers with Adversarial Text Example Generation
Authors: Catherine Wong
Date: 2017-12-14 00:00:00
Tasks: Adversarial Text, Reinforcement Learning

Machine learning models are powerful but fallible. Generating adversarial
examples - inputs deliberately crafted to cause model misclassification or
other errors - can yield important insight into model assumptions and
vulnerabilities. Despite significant recent work on adversarial example
generation targeting image classifiers, relatively little work exists exploring
adversarial example generation for text classifiers; additionally, many
existing adversarial example generation algorithms require full access to
target model parameters, rendering them impractical for many real-world
attacks. In this work, we introduce DANCin SEQ2SEQ, a GAN-inspired algorithm
for adversarial text example generation targeting largely black-box text
classifiers. We recast adversarial text example generation as a reinforcement
learning problem, and demonstrate that our algorithm offers preliminary but
promising steps towards generating semantically meaningful adversarial text
examples in a real-world attack scenario.
Conditional Generative Adversarial Networks for Emoji Synthesis with Word Embedding Manipulation
Authors: Dianna Radpour, Vivek Bheda
Date: 2017-12-12 00:00:00
Tasks: Image Generation, Word Embeddings

Emojis have become a very popular part of daily digital communication. Their
appeal comes largely in part due to their ability to capture and elicit
emotions in a more subtle and nuanced way than just plain text is able to. In
line with recent advances in the field of deep learning, there are far reaching
implications and applications that generative adversarial networks (GANs) can
have for image generation. In this paper, we present a novel application of
deep convolutional GANs (DC-GANs) with an optimized training procedure. We show
that via incorporation of word embeddings conditioned on Google's word2vec
model into the network, the generator is able to synthesize highly realistic
emojis that are virtually identical to the real ones.
Variance-based regularization with convex objectives
Authors: John Duchi, Hongseok Namkoong
Date: 2016-10-08 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: General Classification, Stochastic Optimization

We develop an approach to risk minimization and stochastic optimization that
provides a convex surrogate for variance, allowing near-optimal and
computationally efficient trading between approximation and estimation error.
Our approach builds off of techniques for distributionally robust optimization
and Owen's empirical likelihood, and we provide a number of finite-sample and
asymptotic results characterizing the theoretical performance of the estimator.
In particular, we show that our procedure comes with certificates of
optimality, achieving (in some scenarios) faster rates of convergence than
empirical risk minimization by virtue of automatically balancing bias and
variance. We give corroborating empirical evidence showing that in practice,
the estimator indeed trades between variance and absolute performance on a
training sample, improving out-of-sample (test) performance over standard
empirical risk minimization for a number of classification problems.
Three IQs of AI Systems and their Testing Methods
Authors: Feng Liu, Yong Shi, Ying Liu
Date: 2017-12-14 00:00:00

The rapid development of artificial intelligence has brought the artificial
intelligence threat theory as well as the problem about how to evaluate the
intelligence level of intelligent products. Both need to find a quantitative
method to evaluate the intelligence level of intelligence systems, including
human intelligence. Based on the standard intelligence system and the extended
Von Neumann architecture, this paper proposes General IQ, Service IQ and Value
IQ evaluation methods for intelligence systems, depending on different
evaluation purposes. Among them, the General IQ of intelligence systems is to
answer the question of whether the artificial intelligence can surpass the
human intelligence, which is reflected in putting the intelligence systems on
an equal status and conducting the unified evaluation. The Service IQ and Value
IQ of intelligence systems are used to answer the question of how the
intelligent products can better serve the human, reflecting the intelligence
and required cost of each intelligence system as a product in the process of
serving human.