pwc-archive/papers-with-abstracts huggingface dataset:
RNN Decoding of Linear Block Codes
Authors: Eliya Nachmani, Elad Marciano, David Burshtein, Yair Be'ery
Date: 2017-02-24 00:00:00
Tasks: Decoder

Designing a practical, low complexity, close to optimal, channel decoder for
powerful algebraic codes with short to moderate block length is an open
research problem. Recently it has been shown that a feed-forward neural network
architecture can improve on standard belief propagation decoding, despite the
large example space. In this paper we introduce a recurrent neural network
architecture for decoding linear block codes. Our method shows comparable bit
error rate results compared to the feed-forward neural network with
significantly less parameters. We also demonstrate improved performance over
belief propagation on sparser Tanner graph representations of the codes.
Furthermore, we demonstrate that the RNN decoder can be used to improve the
performance or alternatively reduce the computational complexity of the mRRD
algorithm for low complexity, close to optimal, decoding of short BCH codes.
Tight Bounds for Bandit Combinatorial Optimization
Authors: Alon Cohen, Tamir Hazan, Tomer Koren
Date: 2017-02-24 00:00:00
Tasks: Combinatorial Optimization, Decision Making, Decision Making Under Uncertainty, Sequential Decision Making

We revisit the study of optimal regret rates in bandit combinatorial
optimization---a fundamental framework for sequential decision making under
uncertainty that abstracts numerous combinatorial prediction problems. We prove
that the attainable regret in this setting grows as
$\widetilde{\Theta}(k^{3/2}\sqrt{dT})$ where $d$ is the dimension of the
problem and $k$ is a bound over the maximal instantaneous loss, disproving a
conjecture of Audibert, Bubeck, and Lugosi (2013) who argued that the optimal
rate should be of the form $\widetilde{\Theta}(k\sqrt{dT})$. Our bounds apply
to several important instances of the framework, and in particular, imply a
tight bound for the well-studied bandit shortest path problem. By that, we also
resolve an open problem posed by Cesa-Bianchi and Lugosi (2012).
Toward high-performance online HCCR: a CNN approach with DropDistortion, path signature and spatial stochastic max-pooling
Authors: Songxuan Lai, Lianwen Jin, Weixin Yang
Date: 2017-02-24 00:00:00

This paper presents an investigation of several techniques that increase the
accuracy of online handwritten Chinese character recognition (HCCR). We propose
a new training strategy named DropDistortion to train a deep convolutional
neural network (DCNN) with distorted samples. DropDistortion gradually lowers
the degree of character distortion during training, which allows the DCNN to
better generalize. Path signature is used to extract effective features for
online characters. Further improvement is achieved by employing spatial
stochastic max-pooling as a method of feature map distortion and model
averaging. Experiments were carried out on three publicly available datasets,
namely CASIA-OLHWDB 1.0, CASIA-OLHWDB 1.1, and the ICDAR2013 online HCCR
competition dataset. The proposed techniques yield state-of-the-art recognition
accuracies of 97.67%, 97.30%, and 97.99%, respectively.
Use Generalized Representations, But Do Not Forget Surface Features
Authors: Nafise Sadat Moosavi, Michael Strube
Date: 2017-02-24 00:00:00
Proceeding: WS 2017 4
Tasks: coreference-resolution, Coreference Resolution, Word Embeddings

Only a year ago, all state-of-the-art coreference resolvers were using an
extensive amount of surface features. Recently, there was a paradigm shift
towards using word embeddings and deep neural networks, where the use of
surface features is very limited. In this paper, we show that a simple SVM
model with surface features outperforms more complex neural models for
detecting anaphoric mentions. Our analysis suggests that using generalized
representations and surface features have different strength that should be
both taken into account for improving coreference resolution.
YodaNN: An Architecture for Ultra-Low Power Binary-Weight CNN Acceleration
Authors: Renzo Andri, Lukas Cavigelli, Davide Rossi, Luca Benini
Date: 2016-06-17 00:00:00
Tasks: General Classification, image-classification, Image Classification

Convolutional neural networks (CNNs) have revolutionized the world of
computer vision over the last few years, pushing image classification beyond
human accuracy. The computational effort of today's CNNs requires power-hungry
parallel processors or GP-GPUs. Recent developments in CNN accelerators for
system-on-chip integration have reduced energy consumption significantly.
Unfortunately, even these highly optimized devices are above the power envelope
imposed by mobile and deeply embedded applications and face hard limitations
caused by CNN weight I/O and storage. This prevents the adoption of CNNs in
future ultra-low power Internet of Things end-nodes for near-sensor analytics.
Recent algorithmic and theoretical advancements enable competitive
classification accuracy even when limiting CNNs to binary (+1/-1) weights
during training. These new findings bring major optimization opportunities in
the arithmetic core by removing the need for expensive multiplications, as well
as reducing I/O bandwidth and storage. In this work, we present an accelerator
optimized for binary-weight CNNs that achieves 1510 GOp/s at 1.2 V on a core
area of only 1.33 MGE (Million Gate Equivalent) or 0.19 mm$^2$ and with a power
dissipation of 895 {\mu}W in UMC 65 nm technology at 0.6 V. Our accelerator
significantly outperforms the state-of-the-art in terms of energy and area
efficiency achieving 61.2 TOp/s/W@0.6 V and 1135 GOp/s/MGE@1.2 V, respectively.
Dirichlet-vMF Mixture Model
Authors: Shaohua Li
Date: 2017-02-24 00:00:00
Tasks: Document Classification, General Classification, model

This document is about the multi-document Von-Mises-Fisher mixture model with
a Dirichlet prior, referred to as VMFMix. VMFMix is analogous to Latent
Dirichlet Allocation (LDA) in that they can capture the co-occurrence patterns
acorss multiple documents. The difference is that in VMFMix, the topic-word
distribution is defined on a continuous n-dimensional hypersphere. Hence VMFMix
is used to derive topic embeddings, i.e., representative vectors, from multiple
sets of embedding vectors. An efficient Variational Expectation-Maximization
inference algorithm is derived. The performance of VMFMix on two document
classification tasks is reported, with some preliminary analysis.
Robot gains Social Intelligence through Multimodal Deep Reinforcement Learning
Authors: Ahmed Hussain Qureshi, Yutaka Nakamura, Yuichiro Yoshikawa, Hiroshi Ishiguro
Date: 2017-02-24 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

For robots to coexist with humans in a social world like ours, it is crucial
that they possess human-like social interaction skills. Programming a robot to
possess such skills is a challenging task. In this paper, we propose a
Multimodal Deep Q-Network (MDQN) to enable a robot to learn human-like
interaction skills through a trial and error method. This paper aims to develop
a robot that gathers data during its interaction with a human and learns human
interaction behaviour from the high-dimensional sensory information using
end-to-end reinforcement learning. This paper demonstrates that the robot was
able to learn basic interaction skills successfully, after 14 days of
interacting with people.
Online Meta-learning by Parallel Algorithm Competition
Authors: Stefan Elfwing, Eiji Uchibe, Kenji Doya
Date: 2017-02-24 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, Meta-Learning, Open-Ended Question Answering, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The efficiency of reinforcement learning algorithms depends critically on a
few meta-parameters that modulates the learning updates and the trade-off
between exploration and exploitation. The adaptation of the meta-parameters is
an open question in reinforcement learning, which arguably has become more of
an issue recently with the success of deep reinforcement learning in
high-dimensional state spaces. The long learning times in domains such as Atari
2600 video games makes it not feasible to perform comprehensive searches of
appropriate meta-parameter values. We propose the Online Meta-learning by
Parallel Algorithm Competition (OMPAC) method. In the OMPAC method, several
instances of a reinforcement learning algorithm are run in parallel with small
differences in the initial values of the meta-parameters. After a fixed number
of episodes, the instances are selected based on their performance in the task
at hand. Before continuing the learning, Gaussian noise is added to the
meta-parameters with a predefined probability. We validate the OMPAC method by
improving the state-of-the-art results in stochastic SZ-Tetris and in standard
Tetris with a smaller, 10$\times$10, board, by 31% and 84%, respectively, and
by improving the results for deep Sarsa($\lambda$) agents in three Atari 2600
games by 62% or more. The experiments also show the ability of the OMPAC method
to adapt the meta-parameters according to the learning progress in different
tasks.
Speckle Reduction with Trained Nonlinear Diffusion Filtering
Authors: Wensen Feng, Yunjin Chen
Date: 2017-02-24 00:00:00
Tasks: Computational Efficiency, GPU, Image Restoration

Speckle reduction is a prerequisite for many image processing tasks in
synthetic aperture radar (SAR) images, as well as all coherent images. In
recent years, predominant state-of-the-art approaches for despeckling are
usually based on nonlocal methods which mainly concentrate on achieving utmost
image restoration quality, with relatively low computational efficiency.
Therefore, in this study we aim to propose an efficient despeckling model with
both high computational efficiency and high recovery quality. To this end, we
exploit a newly-developed trainable nonlinear reaction diffusion(TNRD)
framework which has proven a simple and effective model for various image
restoration problems. {In the original TNRD applications, the diffusion network
is usually derived based on the direct gradient descent scheme. However, this
approach will encounter some problem for the task of multiplicative noise
reduction exploited in this study. To solve this problem, we employed a new
architecture derived from the proximal gradient descent method.} {Taking into
account the speckle noise statistics, the diffusion process for the despeckling
task is derived. We then retrain all the model parameters in the presence of
speckle noise. Finally, optimized nonlinear diffusion filtering models are
obtained, which are specialized for despeckling with various noise levels.
Experimental results substantiate that the trained filtering models provide
comparable or even better results than state-of-the-art nonlocal approaches.
Meanwhile, our proposed model merely contains convolution of linear filters
with an image, which offers high level parallelism on GPUs. As a consequence,
for images of size $512 \times 512$, our GPU implementation takes less than 0.1
seconds to produce state-of-the-art despeckling performance.}
Normalisation de la langue et de lecriture arabe : enjeux culturels regionaux et mondiaux
Authors: Henri Hudrisier, Ben Henda Mokhtar
Date: 2017-02-24 00:00:00

Arabic language and writing are now facing a resurgence of international
normative solutions that challenge most of their local or network based
operating principles. Even if the multilingual digital coding solutions,
especially those proposed by Unicode, have solved many difficulties of Arabic
writing, the linguistic aspect is still in search of more adapted solutions.
Terminology is one of the sectors in which the Arabic language requires a deep
modernization of its classical productivity models. The normative approach, in
particular that of the ISO TC37, is proposed as one of the solutions that would
allow it to combine with international standards to better integrate the
knowledge society under construction.
  La langue et lecriture arabe sont aujourdhui confrontees a une recrudescence
de solutions normatives internationales qui remettent en cause la plupart de
leurs principes de fonctionnement en site ou sur les reseaux. Meme si les
solutions du codage numerique multilingue, notamment celles proposees par
Unicode, ont resolu beaucoup de difficultes de lecriture arabe, le volet
linguistique est encore en quete de solutions plus adaptees. La terminologie
est lun des secteurs dans lequel la langue arabe necessite une modernisation
profonde de ses modeles classiques de production. La voie normative, notamment
celle du TC37 de ISO, est proposee comme une des solutions qui lui permettrait
de se mettre en synergie avec les referentiels internationaux pour mieux
integrer la societe du savoir en voie de construction.
Sequence-based Multimodal Apprenticeship Learning For Robot Perception and Decision Making
Authors: Fei Han, Xue Yang, Yu Zhang, Hao Zhang
Date: 2017-02-24 00:00:00
Tasks: Decision Making

Apprenticeship learning has recently attracted a wide attention due to its
capability of allowing robots to learn physical tasks directly from
demonstrations provided by human experts. Most previous techniques assumed that
the state space is known a priori or employed simple state representations that
usually suffer from perceptual aliasing. Different from previous research, we
propose a novel approach named Sequence-based Multimodal Apprenticeship
Learning (SMAL), which is capable to simultaneously fusing temporal information
and multimodal data, and to integrate robot perception with decision making. To
evaluate the SMAL approach, experiments are performed using both simulations
and real-world robots in the challenging search and rescue scenarios. The
empirical study has validated that our SMAL approach can effectively learn
plans for robots to make decisions using sequence of multimodal observations.
Experimental results have also showed that SMAL outperforms the baseline
methods using individual images.
Simultaneous Feature and Body-Part Learning for Real-Time Robot Awareness of Human Behaviors
Authors: Fei Han, Xue Yang, Christopher Reardon, Yu Zhang, Hao Zhang
Date: 2017-02-24 00:00:00

Robot awareness of human actions is an essential research problem in robotics
with many important real-world applications, including human-robot
collaboration and teaming. Over the past few years, depth sensors have become a
standard device widely used by intelligent robots for 3D perception, which can
also offer human skeletal data in 3D space. Several methods based on skeletal
data were designed to enable robot awareness of human actions with satisfactory
accuracy. However, previous methods treated all body parts and features equally
important, without the capability to identify discriminative body parts and
features. In this paper, we propose a novel simultaneous Feature And Body-part
Learning (FABL) approach that simultaneously identifies discriminative body
parts and features, and efficiently integrates all available information
together to enable real-time robot awareness of human behaviors. We formulate
FABL as a regression-like optimization problem with structured
sparsity-inducing norms to model interrelationships of body parts and features.
We also develop an optimization algorithm to solve the formulated problem,
which possesses a theoretical guarantee to find the optimal solution. To
evaluate FABL, three experiments were performed using public benchmark
datasets, including the MSR Action3D and CAD-60 datasets, as well as a Baxter
robot in practical assistive living applications. Experimental results show
that our FABL approach obtains a high recognition accuracy with a processing
speed of the order-of-magnitude of 10e4 Hz, which makes FABL a promising method
to enable real-time robot awareness of human behaviors in practical robotics
applications.
Learning Non-local Image Diffusion for Image Denoising
Authors: Peng Qiao, Yong Dou, Wensen Feng, Yunjin Chen
Date: 2017-02-24 00:00:00
Tasks: Denoising, Image Denoising, SSIM

Image diffusion plays a fundamental role for the task of image denoising.
Recently proposed trainable nonlinear reaction diffusion (TNRD) model defines a
simple but very effective framework for image denoising. However, as the TNRD
model is a local model, the diffusion behavior of which is purely controlled by
information of local patches, it is prone to create artifacts in the homogenous
regions and over-smooth highly textured regions, especially in the case of
strong noise levels. Meanwhile, it is widely known that the non-local
self-similarity (NSS) prior stands as an effective image prior for image
denoising, which has been widely exploited in many non-local methods. In this
work, we are highly motivated to embed the NSS prior into the TNRD model to
tackle its weaknesses. In order to preserve the expected property that
end-to-end training is available, we exploit the NSS prior by a set of
non-local filters, and derive our proposed trainable non-local reaction
diffusion (TNLRD) model for image denoising. Together with the local filters
and influence functions, the non-local filters are learned by employing
loss-specific training. The experimental results show that the trained TNLRD
model produces visually plausible recovered images with more textures and less
artifacts, compared to its local versions. Moreover, the trained TNLRD model
can achieve strongly competitive performance to recent state-of-the-art image
denoising methods in terms of peak signal-to-noise ratio (PSNR) and structural
similarity index (SSIM).
A Linear Algebraic Approach to Datalog Evaluation
Authors: Taisuke Sato
Date: 2016-07-30 00:00:00

In this paper, we propose a fundamentally new approach to Datalog evaluation.
Given a linear Datalog program DB written using N constants and binary
predicates, we first translate if-and-only-if completions of clauses in DB into
a set Eq(DB) of matrix equations with a non-linear operation where relations in
M_DB, the least Herbrand model of DB, are encoded as adjacency matrices. We
then translate Eq(DB) into another, but purely linear matrix equations
tilde_Eq(DB). It is proved that the least solution of tilde_Eq(DB) in the sense
of matrix ordering is converted to the least solution of Eq(DB) and the latter
gives M_DB as a set of adjacency matrices. Hence computing the least solution
of tilde_Eq(DB) is equivalent to computing M_DB specified by DB. For a class of
tail recursive programs and for some other types of programs, our approach
achieves O(N^3) time complexity irrespective of the number of variables in a
clause since only matrix operations costing O(N^3) or less are used.
  We conducted two experiments that compute the least Herbrand models of linear
Datalog programs. The first experiment computes transitive closure of
artificial data and real network data taken from the Koblenz Network
Collection. The second one compared the proposed approach with the
state-of-the-art symbolic systems including two Prolog systems and two ASP
systems, in terms of computation time for a transitive closure program and the
same generation program. In the experiment, it is observed that our linear
algebraic approach runs 10^1 ~ 10^4 times faster than the symbolic systems when
data is not sparse. To appear in Theory and Practice of Logic Programming
(TPLP).
Hidden Community Detection in Social Networks
Authors: Kun He, Yingru Li, Sucheta Soundarajan, John E. Hopcroft
Date: 2017-02-24 00:00:00
Tasks: Community Detection

We introduce a new paradigm that is important for community detection in the
realm of network analysis. Networks contain a set of strong, dominant
communities, which interfere with the detection of weak, natural community
structure. When most of the members of the weak communities also belong to
stronger communities, they are extremely hard to be uncovered. We call the weak
communities the hidden community structure.
  We present a novel approach called HICODE (HIdden COmmunity DEtection) that
identifies the hidden community structure as well as the dominant community
structure. By weakening the strength of the dominant structure, one can uncover
the hidden structure beneath. Likewise, by reducing the strength of the hidden
structure, one can more accurately identify the dominant structure. In this
way, HICODE tackles both tasks simultaneously.
  Extensive experiments on real-world networks demonstrate that HICODE
outperforms several state-of-the-art community detection methods in uncovering
both the dominant and the hidden structure. In the Facebook university social
networks, we find multiple non-redundant sets of communities that are strongly
associated with residential hall, year of registration or career position of
the faculties or students, while the state-of-the-art algorithms mainly locate
the dominant ground truth category. In the Due to the difficulty of labeling
all ground truth communities in real-world datasets, HICODE provides a
promising approach to pinpoint the existing latent communities and uncover
communities for which there is no ground truth. Finding this unknown structure
is an extremely important community detection problem.
Compositional Learning of Relation Path Embedding for Knowledge Base Completion
Authors: Xixun Lin, Yanchun Liang, Fausto Giunchiglia, Xiaoyue Feng, Renchu Guan
Date: 2016-11-22 00:00:00
Tasks: Knowledge Base Completion, Relation

Large-scale knowledge bases have currently reached impressive sizes; however,
these knowledge bases are still far from complete. In addition, most of the
existing methods for knowledge base completion only consider the direct links
between entities, ignoring the vital impact of the consistent semantics of
relation paths. In this paper, we study the problem of how to better embed
entities and relations of knowledge bases into different low-dimensional spaces
by taking full advantage of the additional semantics of relation paths, and we
propose a compositional learning model of relation path embedding (RPE).
Specifically, with the corresponding relation and path projections, RPE can
simultaneously embed each entity into two types of latent spaces. It is also
proposed that type constraints could be extended from traditional
relation-specific constraints to the new proposed path-specific constraints.
The results of experiments show that the proposed model achieves significant
and consistent improvements compared with the state-of-the-art algorithms.
Viewpoint Adaptation for Rigid Object Detection
Authors: Patrick Wang, Kenneth Morton, Peter Torrione, Leslie Collins
Date: 2017-02-24 00:00:00
Tasks: Human Detection, Object, object-detection, Object Detection

An object detector performs suboptimally when applied to image data taken
from a viewpoint different from the one with which it was trained. In this
paper, we present a viewpoint adaptation algorithm that allows a trained
single-view object detector to be adapted to a new, distinct viewpoint. We
first illustrate how a feature space transformation can be inferred from a
known homography between the source and target viewpoints. Second, we show that
a variety of trained classifiers can be modified to behave as if that
transformation were applied to each testing instance. The proposed algorithm is
evaluated on a person detection task using images from the PETS 2007 and CAVIAR
datasets, as well as from a new synthetic multi-view person detection dataset.
It yields substantial performance improvements when adapting single-view person
detectors to new viewpoints, and simultaneously reduces computational
complexity. This work has the potential to improve detection performance for
cameras viewing objects from arbitrary viewpoints, while simplifying data
collection and feature extraction.