pwc-archive/papers-with-abstracts huggingface dataset:
Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization
Authors: Jonathan Tremblay, Aayush Prakash, David Acuna, Mark Brophy, Varun Jampani, Cem Anil, Thang To, Eric Cameracci, Shaad Boochoon, Stan Birchfield
Date: 2018-04-18 00:00:00
Tasks: Object, object-detection, Object Detection

We present a system for training deep neural networks for object detection
using synthetic images. To handle the variability in real-world data, the
system relies upon the technique of domain randomization, in which the
parameters of the simulator$-$such as lighting, pose, object textures,
etc.$-$are randomized in non-realistic ways to force the neural network to
learn the essential features of the object of interest. We explore the
importance of these parameters, showing that it is possible to produce a
network with compelling performance using only non-artistically-generated
synthetic data. With additional fine-tuning on real data, the network yields
better performance than using real data alone. This result opens up the
possibility of using inexpensive synthetic data for training neural networks
while avoiding the need to collect large amounts of hand-annotated real-world
data or to generate high-fidelity synthetic worlds$-$both of which remain
bottlenecks for many applications. The approach is evaluated on bounding box
detection of cars on the KITTI dataset.
Benchmarking projective simulation in navigation problems
Authors: Alexey A. Melnikov, Adi Makmal, Hans J. Briegel
Date: 2018-04-23 00:00:00
Tasks: Benchmarking, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Projective simulation (PS) is a model for intelligent agents with a
deliberation capacity that is based on episodic memory. The model has been
shown to provide a flexible framework for constructing reinforcement-learning
agents, and it allows for quantum mechanical generalization, which leads to a
speed-up in deliberation time. PS agents have been applied successfully in the
context of complex skill learning in robotics, and in the design of
state-of-the-art quantum experiments. In this paper, we study the performance
of projective simulation in two benchmarking problems in navigation, namely the
grid world and the mountain car problem. The performance of PS is compared to
standard tabular reinforcement learning approaches, Q-learning and SARSA. Our
comparison demonstrates that the performance of PS and standard learning
approaches are qualitatively and quantitatively similar, while it is much
easier to choose optimal model parameters in case of projective simulation,
with a reduced computational effort of one to two orders of magnitude. Our
results show that the projective simulation model stands out for its simplicity
in terms of the number of model parameters, which makes it simple to set up the
learning agent in unknown task environments.
Zero-Shot Visual Imitation
Authors: Deepak Pathak, Parsa Mahmoudieh, Guanghao Luo, Pulkit Agrawal, Dian Chen, Yide Shentu, Evan Shelhamer, Jitendra Malik, Alexei A. Efros, Trevor Darrell
Date: 2018-04-23 00:00:00
Proceeding: ICLR 2018 1
Tasks: Imitation Learning

The current dominant paradigm for imitation learning relies on strong
supervision of expert actions to learn both 'what' and 'how' to imitate. We
pursue an alternative paradigm wherein an agent first explores the world
without any expert supervision and then distills its experience into a
goal-conditioned skill policy with a novel forward consistency loss. In our
framework, the role of the expert is only to communicate the goals (i.e., what
to imitate) during inference. The learned policy is then employed to mimic the
expert (i.e., how to imitate) after seeing just a sequence of images
demonstrating the desired task. Our method is 'zero-shot' in the sense that the
agent never has access to expert actions during training or for the task
demonstration at inference. We evaluate our zero-shot imitator in two
real-world settings: complex rope manipulation with a Baxter robot and
navigation in previously unseen office environments with a TurtleBot. Through
further experiments in VizDoom simulation, we provide evidence that better
mechanisms for exploration lead to learning a more capable policy which in turn
improves end task performance. Videos, models, and more details are available
at https://pathak22.github.io/zeroshot-imitation/
Towards Symbolic Reinforcement Learning with Common Sense
Authors: Artur d'Avila Garcez, Aimore Resende Riquetti Dutra, Eduardo Alonso
Date: 2018-04-23 00:00:00
Tasks: Common Sense Reasoning, Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

Deep Reinforcement Learning (deep RL) has made several breakthroughs in
recent years in applications ranging from complex control tasks in unmanned
vehicles to game playing. Despite their success, deep RL still lacks several
important capacities of human intelligence, such as transfer learning,
abstraction and interpretability. Deep Symbolic Reinforcement Learning (DSRL)
seeks to incorporate such capacities to deep Q-networks (DQN) by learning a
relevant symbolic representation prior to using Q-learning. In this paper, we
propose a novel extension of DSRL, which we call Symbolic Reinforcement
Learning with Common Sense (SRL+CS), offering a better balance between
generalization and specialization, inspired by principles of common sense when
assigning rewards and aggregating Q-values. Experiments reported in this paper
show that SRL+CS learns consistently faster than Q-learning and DSRL, achieving
also a higher accuracy. In the hardest case, where agents were trained in a
deterministic environment and tested in a random environment, SRL+CS achieves
nearly 100% average accuracy compared to DSRL's 70% and DQN's 50% accuracy. To
the best of our knowledge, this is the first case of near perfect zero-shot
transfer learning using Reinforcement Learning.
Large Scale Scene Text Verification with Guided Attention
Authors: Dafang He, Yeqing Li, Alexander Gorban, Derrall Heath, Julian Ibarz, Qian Yu, Daniel Kifer, C. Lee Giles
Date: 2018-04-23 00:00:00
Tasks: Question Answering, Scene Text Detection, Text Detection, Visual Question Answering, Visual Question Answering (VQA)

Many tasks are related to determining if a particular text string exists in
an image. In this work, we propose a new framework that learns this task in an
end-to-end way. The framework takes an image and a text string as input and
then outputs the probability of the text string being present in the image.
This is the first end-to-end framework that learns such relationships between
text and images in scene text area. The framework does not require explicit
scene text detection or recognition and thus no bounding box annotations are
needed for it. It is also the first work in scene text area that tackles suh a
weakly labeled problem. Based on this framework, we developed a model called
Guided Attention. Our designed model achieves much better results than several
state-of-the-art scene text reading based solutions for a challenging Street
View Business Matching task. The task tries to find correct business names for
storefront images and the dataset we collected for it is substantially larger,
and more challenging than existing scene text dataset. This new real-world task
provides a new perspective for studying scene text related problems. We also
demonstrate the uniqueness of our task via a comparison between our problem and
a typical Visual Question Answering problem.
Light-weight Head Pose Invariant Gaze Tracking
Authors: Rajeev Ranjan, Shalini De Mello, Jan Kautz
Date: 2018-04-23 00:00:00
Tasks: Gaze Estimation, Transfer Learning, Viewpoint Estimation

Unconstrained remote gaze tracking using off-the-shelf cameras is a
challenging problem. Recently, promising algorithms for appearance-based gaze
estimation using convolutional neural networks (CNN) have been proposed.
Improving their robustness to various confounding factors including variable
head pose, subject identity, illumination and image quality remain open
problems. In this work, we study the effect of variable head pose on machine
learning regressors trained to estimate gaze direction. We propose a novel
branched CNN architecture that improves the robustness of gaze classifiers to
variable head pose, without increasing computational cost. We also present
various procedures to effectively train our gaze network including transfer
learning from the more closely related task of object viewpoint estimation and
from a large high-fidelity synthetic gaze dataset, which enable our ten times
faster gaze network to achieve competitive accuracy to its current
state-of-the-art direct competitor.
Spatio-Temporal Neural Networks for Space-Time Series Forecasting and Relations Discovery
Authors: Ali Ziat, Edouard Delasalles, Ludovic Denoyer, Patrick Gallinari
Date: 2018-04-23 00:00:00
Tasks: Decoder, Epidemiology, Time Series, Time Series Analysis, Time Series Forecasting, Traffic Prediction

We introduce a dynamical spatio-temporal model formalized as a recurrent
neural network for forecasting time series of spatial processes, i.e. series of
observations sharing temporal and spatial dependencies. The model learns these
dependencies through a structured latent dynamical component, while a decoder
predicts the observations from the latent representations. We consider several
variants of this model, corresponding to different prior hypothesis about the
spatial relations between the series. The model is evaluated and compared to
state-of-the-art baselines, on a variety of forecasting problems representative
of different application areas: epidemiology, geo-spatial statistics and
car-traffic prediction. Besides these evaluations, we also describe experiments
showing the ability of this approach to extract relevant spatial relations.
False Information on Web and Social Media: A Survey
Authors: Srijan Kumar, Neil Shah
Date: 2018-04-23 00:00:00
Tasks: Feature Engineering, Graph Mining, Survey

False information can be created and spread easily through the web and social
media platforms, resulting in widespread real-world impact. Characterizing how
false information proliferates on social platforms and why it succeeds in
deceiving readers are critical to develop efficient detection algorithms and
tools for early detection. A recent surge of research in this area has aimed to
address the key issues using methods based on feature engineering, graph
mining, and information modeling. Majority of the research has primarily
focused on two broad categories of false information: opinion-based (e.g., fake
reviews), and fact-based (e.g., false news and hoaxes). Therefore, in this
work, we present a comprehensive survey spanning diverse aspects of false
information, namely (i) the actors involved in spreading false information,
(ii) rationale behind successfully deceiving readers, (iii) quantifying the
impact of false information, (iv) measuring its characteristics across
different dimensions, and finally, (iv) algorithms developed to detect false
information. In doing so, we create a unified framework to describe these
recent methods and highlight a number of important directions for future
research.
VectorDefense: Vectorization as a Defense to Adversarial Examples
Authors: Vishaal Munusamy Kabilan, Brandon Morris, Anh Nguyen
Date: 2018-04-23 00:00:00

Training deep neural networks on images represented as grids of pixels has
brought to light an interesting phenomenon known as adversarial examples.
Inspired by how humans reconstruct abstract concepts, we attempt to codify the
input bitmap image into a set of compact, interpretable elements to avoid being
fooled by the adversarial structures. We take the first step in this direction
by experimenting with image vectorization as an input transformation step to
map the adversarial examples back into the natural manifold of MNIST
handwritten digits. We compare our method vs. state-of-the-art input
transformations and further discuss the trade-offs between a hand-designed and
a learned transformation defense.
Optimality of Approximate Inference Algorithms on Stable Instances
Authors: Hunter Lang, David Sontag, Aravindan Vijayaraghavan
Date: 2017-11-06 00:00:00
Tasks: Structured Prediction

Approximate algorithms for structured prediction problems---such as LP
relaxations and the popular alpha-expansion algorithm (Boykov et al.
2001)---typically far exceed their theoretical performance guarantees on
real-world instances. These algorithms often find solutions that are very close
to optimal. The goal of this paper is to partially explain the performance of
alpha-expansion and an LP relaxation algorithm on MAP inference in
Ferromagnetic Potts models (FPMs). Our main results give stability conditions
under which these two algorithms provably recover the optimal MAP solution.
These theoretical results complement numerous empirical observations of good
performance.
Person Identification from Partial Gait Cycle Using Fully Convolutional Neural Network
Authors: Maryam Babaee, Linwei Li, Gerhard Rigoll
Date: 2018-04-23 00:00:00
Tasks: Gait Recognition, Person Identification

Gait as a biometric property for person identification plays a key role in
video surveillance and security applications. In gait recognition, normally,
gait feature such as Gait Energy Image (GEI) is extracted from one full gait
cycle. However in many circumstances, such a full gait cycle might not be
available due to occlusion. Thus, the GEI is not complete giving rise to a
degrading in gait-based person identification rate. In this paper, we address
this issue by proposing a novel method to identify individuals from gait
feature when a few (or even single) frame(s) is available. To do so, we propose
a deep learning-based approach to transform incomplete GEI to the corresponding
complete GEI obtained from a full gait cycle. More precisely, this
transformation is done gradually by training several auto encoders
independently and then combining these as a uniform model. Experimental results
on two public gait datasets, namely OULP and Casia-B demonstrate the validity
of the proposed method in dealing with very incomplete gait cycles.
ALIGNet: Partial-Shape Agnostic Alignment via Unsupervised Learning
Authors: Rana Hanocka, Noa Fish, Zhenhua Wang, Raja Giryes, Shachar Fleishman, Daniel Cohen-Or
Date: 2018-04-23 00:00:00
Tasks: Memorization

The process of aligning a pair of shapes is a fundamental operation in
computer graphics. Traditional approaches rely heavily on matching
corresponding points or features to guide the alignment, a paradigm that
falters when significant shape portions are missing. These techniques generally
do not incorporate prior knowledge about expected shape characteristics, which
can help compensate for any misleading cues left by inaccuracies exhibited in
the input shapes. We present an approach based on a deep neural network,
leveraging shape datasets to learn a shape-aware prior for source-to-target
alignment that is robust to shape incompleteness. In the absence of ground
truth alignments for supervision, we train a network on the task of shape
alignment using incomplete shapes generated from full shapes for
self-supervision. Our network, called ALIGNet, is trained to warp complete
source shapes to incomplete targets, as if the target shapes were complete,
thus essentially rendering the alignment partial-shape agnostic. We aim for the
network to develop specialized expertise over the common characteristics of the
shapes in each dataset, thereby achieving a higher-level understanding of the
expected shape space to which a local approach would be oblivious. We constrain
ALIGNet through an anisotropic total variation identity regularization to
promote piecewise smooth deformation fields, facilitating both partial-shape
agnosticism and post-deformation applications. We demonstrate that ALIGNet
learns to align geometrically distinct shapes, and is able to infer plausible
mappings even when the target shape is significantly incomplete. We show that
our network learns the common expected characteristics of shape collections,
without over-fitting or memorization, enabling it to produce plausible
deformations on unseen data during test time.
Generating Talking Face Landmarks from Speech
Authors: Sefik Emre Eskimez, Ross K. Maddox, Chenliang Xu, Zhiyao Duan
Date: 2018-03-26 00:00:00

The presence of a corresponding talking face has been shown to significantly
improve speech intelligibility in noisy conditions and for hearing impaired
population. In this paper, we present a system that can generate landmark
points of a talking face from an acoustic speech in real time. The system uses
a long short-term memory (LSTM) network and is trained on frontal videos of 27
different speakers with automatically extracted face landmarks. After training,
it can produce talking face landmarks from the acoustic speech of unseen
speakers and utterances. The training phase contains three key steps. We first
transform landmarks of the first video frame to pin the two eye points into two
predefined locations and apply the same transformation on all of the following
video frames. We then remove the identity information by transforming the
landmarks into a mean face shape across the entire training dataset. Finally,
we train an LSTM network that takes the first- and second-order temporal
differences of the log-mel spectrogram as input to predict face landmarks in
each frame. We evaluate our system using the mean-squared error (MSE) loss of
landmarks of lips between predicted and ground-truth landmarks as well as their
first- and second-order temporal differences. We further evaluate our system by
conducting subjective tests, where the subjects try to distinguish the real and
fake videos of talking face landmarks. Both tests show promising results.
Neural Network Based Reinforcement Learning for Audio-Visual Gaze Control in Human-Robot Interaction
Authors: Stéphane Lathuilière, Benoit Massé, Pablo Mesejo, Radu Horaud
Date: 2017-11-18 00:00:00
Tasks: parameter estimation, Q-Learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper introduces a novel neural network-based reinforcement learning
approach for robot gaze control. Our approach enables a robot to learn and to
adapt its gaze control strategy for human-robot interaction neither with the
use of external sensors nor with human supervision. The robot learns to focus
its attention onto groups of people from its own audio-visual experiences,
independently of the number of people, of their positions and of their physical
appearances. In particular, we use a recurrent neural network architecture in
combination with Q-learning to find an optimal action-selection policy; we
pre-train the network using a simulated environment that mimics realistic
scenarios that involve speaking/silent participants, thus avoiding the need of
tedious sessions of a robot interacting with people. Our experimental
evaluation suggests that the proposed method is robust against parameter
estimation, i.e. the parameter values yielded by the method do not have a
decisive impact on the performance. The best results are obtained when both
audio and visual information is jointly used. Experiments with the Nao robot
indicate that our framework is a step forward towards the autonomous learning
of socially acceptable gaze behavior.
On Communication Complexity of Classification Problems
Authors: Daniel M. Kane, Roi Livni, Shay Moran, Amir Yehudayoff
Date: 2017-11-16 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification, Learning Theory

This work studies distributed learning in the spirit of Yao's model of
communication complexity: consider a two-party setting, where each of the
players gets a list of labelled examples and they communicate in order to
jointly perform some learning task. To naturally fit into the framework of
learning theory, the players can send each other examples (as well as bits)
where each example/bit costs one unit of communication. This enables a uniform
treatment of infinite classes such as half-spaces in $\mathbb{R}^d$, which are
ubiquitous in machine learning.
  We study several fundamental questions in this model. For example, we provide
combinatorial characterizations of the classes that can be learned with
efficient communication in the proper-case as well as in the improper-case.
These findings imply unconditional separations between various learning
contexts, e.g.\ realizable versus agnostic learning, proper versus improper
learning, etc.
  The derivation of these results hinges on a type of decision problems we term
"{\it realizability problems}" where the goal is deciding whether a distributed
input sample is consistent with an hypothesis from a pre-specified class.
  From a technical perspective, the protocols we use are based on ideas from
machine learning theory and the impossibility results are based on ideas from
communication complexity theory.
Object Counts! Bringing Explicit Detections Back into Image Captioning
Authors: Josiah Wang, Pranava Madhyastha, Lucia Specia
Date: 2018-04-23 00:00:00
Proceeding: NAACL 2018 6
Tasks: Image Captioning, Language Modeling, Language Modelling, Object

The use of explicit object detectors as an intermediate step to image
captioning - which used to constitute an essential stage in early work - is
often bypassed in the currently dominant end-to-end approaches, where the
language model is conditioned directly on a mid-level image embedding. We argue
that explicit detections provide rich semantic information, and can thus be
used as an interpretable representation to better understand why end-to-end
image captioning systems work well. We provide an in-depth analysis of
end-to-end image captioning by exploring a variety of cues that can be derived
from such object detections. Our study reveals that end-to-end image captioning
systems rely on matching image representations to generate captions, and that
encoding the frequency, size and position of objects are complementary and all
play a role in forming a good image representation. It also reveals that
different object categories contribute in different ways towards image
captioning.
Projective Splitting with Forward Steps: Asynchronous and Block-Iterative Operator Splitting
Authors: Patrick R. Johnstone, Jonathan Eckstein
Date: 2018-03-19 00:00:00
Tasks: feature selection

This work is concerned with the classical problem of finding a zero of a sum of maximal monotone operators. For the projective splitting framework recently proposed by Combettes and Eckstein, we show how to replace the fundamental subproblem calculation using a backward step with one based on two forward steps. The resulting algorithms have the same kind of coordination procedure and can be implemented in the same block-iterative and highly flexible manner, but may perform backward steps on some operators and forward steps on others. Prior algorithms in the projective splitting family have used only backward steps. Forward steps can be used for any Lipschitz-continuous operators provided the stepsize is bounded by the inverse of the Lipschitz constant. If the Lipschitz constant is unknown, a simple backtracking linesearch procedure may be used. For affine operators, the stepsize can be chosen adaptively without knowledge of the Lipschitz constant and without any additional forward steps. We close the paper by empirically studying the performance of several kinds of splitting algorithms on a large-scale rare feature selection problem.