pwc-archive/papers-with-abstracts huggingface dataset:
Text Extraction From Texture Images Using Masked Signal Decomposition
Authors: Shervin Minaee, Yao Wang
Date: 2017-06-11 00:00:00
Tasks: Autonomous Driving, Optical Character Recognition, Optical Character Recognition (OCR), Text Segmentation

Text extraction is an important problem in image processing with applications
from optical character recognition to autonomous driving. Most of the
traditional text segmentation algorithms consider separating text from a simple
background (which usually has a different color from texts). In this work we
consider separating texts from a textured background, that has similar color to
texts. We look at this problem from a signal decomposition perspective, and
consider a more realistic scenario where signal components are overlaid on top
of each other (instead of adding together). When the signals are overlaid, to
separate signal components, we need to find a binary mask which shows the
support of each component. Because directly solving the binary mask is
intractable, we relax this problem to the approximated continuous problem, and
solve it by alternating optimization method. We show that the proposed
algorithm achieves significantly better results than other recent works on
several challenging images.
Sharing deep generative representation for perceived image reconstruction from human brain activity
Authors: Changde Du, Changying Du, Huiguang He
Date: 2017-04-25 00:00:00
Tasks: Bayesian Inference, Image Reconstruction

Decoding human brain activities via functional magnetic resonance imaging
(fMRI) has gained increasing attention in recent years. While encouraging
results have been reported in brain states classification tasks, reconstructing
the details of human visual experience still remains difficult. Two main
challenges that hinder the development of effective models are the perplexing
fMRI measurement noise and the high dimensionality of limited data instances.
Existing methods generally suffer from one or both of these issues and yield
dissatisfactory results. In this paper, we tackle this problem by casting the
reconstruction of visual stimulus as the Bayesian inference of missing view in
a multiview latent variable model. Sharing a common latent representation, our
joint generative model of external stimulus and brain response is not only
"deep" in extracting nonlinear features from visual images, but also powerful
in capturing correlations among voxel activities of fMRI recordings. The
nonlinearity and deep structure endow our model with strong representation
ability, while the correlations of voxel activities are critical for
suppressing noise and improving prediction. We devise an efficient variational
Bayesian method to infer the latent variables and the model parameters. To
further improve the reconstruction accuracy, the latent representations of
testing instances are enforced to be close to that of their neighbours from the
training set via posterior regularization. Experiments on three fMRI recording
datasets demonstrate that our approach can more accurately reconstruct visual
stimuli.
An Optimal Bayesian Network Based Solution Scheme for the Constrained Stochastic On-line Equi-Partitioning Problem
Authors: Sondre Glimsdal, Ole-Christoffer Granmo
Date: 2017-07-11 00:00:00
Tasks: Object

A number of intriguing decision scenarios revolve around partitioning a
collection of objects to optimize some application specific objective function.
This problem is generally referred to as the Object Partitioning Problem (OPP)
and is known to be NP-hard. We here consider a particularly challenging version
of OPP, namely, the Stochastic On-line Equi-Partitioning Problem (SO-EPP). In
SO-EPP, the target partitioning is unknown and has to be inferred purely from
observing an on-line sequence of object pairs. The paired objects belong to the
same partition with probability $p$ and to different partitions with
probability $1-p$, with $p$ also being unknown. As an additional complication,
the partitions are required to be of equal cardinality. Previously, only
sub-optimal solution strategies have been proposed for SO- EPP. In this paper,
we propose the first optimal solution strategy. In brief, the scheme that we
propose, BN-EPP, is founded on a Bayesian network representation of SO-EPP
problems. Based on probabilistic reasoning, we are not only able to infer the
underlying object partitioning with optimal accuracy. We are also able to
simultaneously infer $p$, allowing us to accelerate learning as object pairs
arrive. Furthermore, our scheme is the first to support arbitrary constraints
on the partitioning (Constrained SO-EPP). Being optimal, BN-EPP provides
superior performance compared to existing solution schemes. We additionally
introduce Walk-BN-EPP, a novel WalkSAT inspired algorithm for solving large
scale BN-EPP problems. Finally, we provide a BN-EPP based solution to the
problem of order picking, a representative real-life application of BN-EPP.
Gray-box optimization and factorized distribution algorithms: where two worlds collide
Authors: Roberto Santana
Date: 2017-07-11 00:00:00
Tasks: Evolutionary Algorithms, Vocal Bursts Valence Prediction

The concept of gray-box optimization, in juxtaposition to black-box
optimization, revolves about the idea of exploiting the problem structure to
implement more efficient evolutionary algorithms (EAs). Work on factorized
distribution algorithms (FDAs), whose factorizations are directly derived from
the problem structure, has also contributed to show how exploiting the problem
structure produces important gains in the efficiency of EAs. In this paper we
analyze the general question of using problem structure in EAs focusing on
confronting work done in gray-box optimization with related research
accomplished in FDAs. This contrasted analysis helps us to identify, in current
studies on the use problem structure in EAs, two distinct analytical
characterizations of how these algorithms work. Moreover, we claim that these
two characterizations collide and compete at the time of providing a coherent
framework to investigate this type of algorithms. To illustrate this claim, we
present a contrasted analysis of formalisms, questions, and results produced in
FDAs and gray-box optimization. Common underlying principles in the two
approaches, which are usually overlooked, are identified and discussed.
Besides, an extensive review of previous research related to different uses of
the problem structure in EAs is presented. The paper also elaborates on some of
the questions that arise when extending the use of problem structure in EAs,
such as the question of evolvability, high cardinality of the variables and
large definition sets, constrained and multi-objective problems, etc. Finally,
emergent approaches that exploit neural models to capture the problem structure
are covered.
A Separation-Based Design to Data-Driven Control for Large-Scale Partially Observed Systems
Authors: Dan Yu, Mohammadhussein Rafieisakhaei, Suman Chakravorty
Date: 2017-07-11 00:00:00

This paper studies the partially observed stochastic optimal control problem
for systems with state dynamics governed by Partial Differential Equations
(PDEs) that leads to an extremely large problem. First, an open-loop
deterministic trajectory optimization problem is solved using a black box
simulation model of the dynamical system. Next, a Linear Quadratic Gaussian
(LQG) controller is designed for the nominal trajectory-dependent linearized
system, which is identified using input-output experimental data consisting of
the impulse responses of the optimized nominal system. A computational
nonlinear heat example is used to illustrate the performance of the approach.
Learning Visual Servoing with Deep Features and Fitted Q-Iteration
Authors: Alex X. Lee, Sergey Levine, Pieter Abbeel
Date: 2017-03-31 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Visual servoing involves choosing actions that move a robot in response to
observations from a camera, in order to reach a goal configuration in the
world. Standard visual servoing approaches typically rely on manually designed
features and analytical dynamics models, which limits their generalization
capability and often requires extensive application-specific feature and model
engineering. In this work, we study how learned visual features, learned
predictive dynamics models, and reinforcement learning can be combined to learn
visual servoing mechanisms. We focus on target following, with the goal of
designing algorithms that can learn a visual servo using low amounts of data of
the target in question, to enable quick adaptation to new targets. Our approach
is based on servoing the camera in the space of learned visual features, rather
than image pixels or manually-designed keypoints. We demonstrate that standard
deep features, in our case taken from a model trained for object
classification, can be used together with a bilinear predictive model to learn
an effective visual servo that is robust to visual variation, changes in
viewing angle and appearance, and occlusions. A key component of our approach
is to use a sample-efficient fitted Q-iteration algorithm to learn which
features are best suited for the task at hand. We show that we can learn an
effective visual servo on a complex synthetic car following benchmark using
just 20 training trajectory samples for reinforcement learning. We demonstrate
substantial improvement over a conventional approach based on image pixels or
hand-designed keypoints, and we show an improvement in sample-efficiency of
more than two orders of magnitude over standard model-free deep reinforcement
learning algorithms. Videos are available at
http://rll.berkeley.edu/visual_servoing .
Lexicographic choice functions
Authors: Arthur Van Camp, Gert de Cooman, Enrique Miranda
Date: 2017-07-10 00:00:00

We investigate a generalisation of the coherent choice functions considered
by Seidenfeld et al. (2010), by sticking to the convexity axiom but imposing no
Archimedeanity condition. We define our choice functions on vector spaces of
options, which allows us to incorporate as special cases both Seidenfeld et
al.'s (2010) choice functions on horse lotteries and sets of desirable gambles
(Quaeghebeur, 2014), and to investigate their connections. We show that choice
functions based on sets of desirable options (gambles) satisfy Seidenfeld's
convexity axiom only for very particular types of sets of desirable options,
which are in a one-to-one relationship with the lexicographic probabilities. We
call them lexicographic choice functions. Finally, we prove that these choice
functions can be used to determine the most conservative convex choice function
associated with a given binary relation.
Automatic Understanding of Image and Video Advertisements
Authors: Zaeem Hussain, Mingda Zhang, Xiaozhong Zhang, Keren Ye, Christopher Thomas, Zuha Agha, Nathan Ong, Adriana Kovashka
Date: 2017-07-10 00:00:00
Proceeding: CVPR 2017 7

There is more to images than their objective physical content: for example,
advertisements are created to persuade a viewer to take a certain action. We
propose the novel problem of automatic advertisement understanding. To enable
research on this problem, we create two datasets: an image dataset of 64,832
image ads, and a video dataset of 3,477 ads. Our data contains rich annotations
encompassing the topic and sentiment of the ads, questions and answers
describing what actions the viewer is prompted to take and the reasoning that
the ad presents to persuade the viewer ("What should I do according to this ad,
and why should I do it?"), and symbolic references ads make (e.g. a dove
symbolizes peace). We also analyze the most common persuasive strategies ads
use, and the capabilities that computer vision systems should have to
understand these strategies. We present baseline classification results for
several prediction tasks, including automatically answering questions about the
messages of the ads.
Improving Neural Parsing by Disentangling Model Combination and Reranking Effects
Authors: Daniel Fried, Mitchell Stern, Dan Klein
Date: 2017-07-10 00:00:00
Proceeding: ACL 2017 7
Tasks: Constituency Parsing, Reranking

Recent work has proposed several generative neural models for constituency
parsing that achieve state-of-the-art results. Since direct search in these
generative models is difficult, they have primarily been used to rescore
candidate outputs from base parsers in which decoding is more straightforward.
We first present an algorithm for direct search in these generative models. We
then demonstrate that the rescoring results are at least partly due to implicit
model combination rather than reranking effects. Finally, we show that explicit
model combination can improve performance even further, resulting in new
state-of-the-art numbers on the PTB of 94.25 F1 when training only on gold data
and 94.66 F1 when using external data.
Non-Local Color Image Denoising with Convolutional Neural Networks
Authors: Stamatios Lefkimmiatis
Date: 2016-11-21 00:00:00
Proceeding: CVPR 2017 7
Tasks: Color Image Denoising, Denoising, GPU, Image Denoising

We propose a novel deep network architecture for grayscale and color image
denoising that is based on a non-local image model. Our motivation for the
overall design of the proposed network stems from variational methods that
exploit the inherent non-local self-similarity property of natural images. We
build on this concept and introduce deep networks that perform non-local
processing and at the same time they significantly benefit from discriminative
learning. Experiments on the Berkeley segmentation dataset, comparing several
state-of-the-art methods, show that the proposed non-local models achieve the
best reported denoising performance both for grayscale and color images for all
the tested noise levels. It is also worth noting that this increase in
performance comes at no extra cost on the capacity of the network compared to
existing alternative deep network architectures. In addition, we highlight a
direct link of the proposed non-local models to convolutional neural networks.
This connection is of significant importance since it allows our models to take
full advantage of the latest advances on GPU computing in deep learning and
makes them amenable to efficient implementations through their inherent
parallelism.
Learning Heuristic Search via Imitation
Authors: Mohak Bhardwaj, Sanjiban Choudhury, Sebastian Scherer
Date: 2017-07-10 00:00:00
Tasks: Heuristic Search, Motion Planning, valid

Robotic motion planning problems are typically solved by constructing a
search tree of valid maneuvers from a start to a goal configuration. Limited
onboard computation and real-time planning constraints impose a limit on how
large this search tree can grow. Heuristics play a crucial role in such
situations by guiding the search towards potentially good directions and
consequently minimizing search effort. Moreover, it must infer such directions
in an efficient manner using only the information uncovered by the search up
until that time. However, state of the art methods do not address the problem
of computing a heuristic that explicitly minimizes search effort. In this
paper, we do so by training a heuristic policy that maps the partial
information from the search to decide which node of the search tree to expand.
Unfortunately, naively training such policies leads to slow convergence and
poor local minima. We present SaIL, an efficient algorithm that trains
heuristic policies by imitating "clairvoyant oracles" - oracles that have full
information about the world and demonstrate decisions that minimize search
effort. We leverage the fact that such oracles can be efficiently computed
using dynamic programming and derive performance guarantees for the learnt
heuristic. We validate the approach on a spectrum of environments which show
that SaIL consistently outperforms state of the art algorithms. Our approach
paves the way forward for learning heuristics that demonstrate an anytime
nature - finding feasible solutions quickly and incrementally refining it over
time.
A Matrix Splitting Perspective on Planning with Options
Authors: Pierre-Luc Bacon, Doina Precup
Date: 2016-12-03 00:00:00

We show that the Bellman operator underlying the options framework leads to a
matrix splitting, an approach traditionally used to speed up convergence of
iterative solvers for large linear systems of equations. Based on standard
comparison theorems for matrix splittings, we then show how the asymptotic rate
of convergence varies as a function of the inherent timescales of the options.
This new perspective highlights a trade-off between asymptotic performance and
the cost of computation associated with building a good set of options.
Defensive Player Classification in the National Basketball Association
Authors: Neil Seward
Date: 2016-12-13 00:00:00
Tasks: Classification, Clustering, General Classification

The National Basketball Association(NBA) has expanded their data gathering
and have heavily invested in new technologies to gather advanced performance
metrics on players. This expanded data set allows analysts to use unique
performance metrics in models to estimate and classify player performance.
Instead of grouping players together based on physical attributes and positions
played, analysts can group together players that play similar to each other
based on these tracked metrics. Existing methods for player classification have
typically used offensive metrics for clustering [1]. There have been attempts
to classify players using past defensive metrics, but the lack of quality
metrics has not produced promising results. The classifications presented in
the paper use newly introduced defensive metrics to find different defensive
positions for each player. Without knowing the number of categories that
players can be cast into, Gaussian Mixture Models (GMM) can be applied to find
the optimal number of clusters. In the model presented, five different
defensive player types can be identified.
Emergence of Locomotion Behaviours in Rich Environments
Authors: Nicolas Heess, Dhruva TB, Srinivasan Sriram, Jay Lemmon, Josh Merel, Greg Wayne, Yuval Tassa, Tom Erez, Ziyu Wang, S. M. Ali Eslami, Martin Riedmiller, David Silver
Date: 2017-07-07 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The reinforcement learning paradigm allows, in principle, for complex
behaviours to be learned directly from simple reward signals. In practice,
however, it is common to carefully hand-design the reward function to encourage
a particular solution, or to derive it from demonstration data. In this paper
explore how a rich environment can help to promote the learning of complex
behavior. Specifically, we train agents in diverse environmental contexts, and
find that this encourages the emergence of robust behaviours that perform well
across a suite of tasks. We demonstrate this principle for locomotion --
behaviours that are known for their sensitivity to the choice of reward. We
train several simulated bodies on a diverse set of challenging terrains and
obstacles, using a simple reward function based on forward progress. Using a
novel scalable variant of policy gradient reinforcement learning, our agents
learn to run, jump, crouch and turn as required by the environment without
explicit reward-based guidance. A visual depiction of highlights of the learned
behavior can be viewed following https://youtu.be/hx_bgoTF7bs .
Sparse inference of the drift of a high-dimensional Ornstein-Uhlenbeck process
Authors: Stéphane Gaïffas, Gustaw Matulewicz
Date: 2017-07-10 00:00:00
Tasks: Variable Selection

Given the observation of a high-dimensional Ornstein-Uhlenbeck (OU) process
in continuous time, we proceed to the inference of the drift parameter under a
row-sparsity assumption. Towards that aim, we consider the negative
log-likelihood of the process, penalized by an $\ell^1$-penalization (Lasso and
Adaptive Lasso). We provide both non-asymptotic and asymptotic results for this
procedure, by means of a sharp oracle inequality, and a limit theorem in the
long-time asymptotics, including asymptotic consistency for variable selection.
As a by-product, we point out the fact that for the Ornstein-Uhlenbeck process,
one does not need an assumption of restricted eigenvalue type in order to
derive fast rates for the Lasso, while it is well-known to be mandatory for
linear regression for instance. Numerical results illustrate the benefits of
this penalized procedure compared to standard maximum likelihood approaches
both on simulations and real-world financial data.
Foot anthropometry device and single object image thresholding
Authors: Amir Mohammad Esmaieeli Sikaroudi, Sasan Ghaffari, Ali Yousefi, Hassan Sadeghi Naeini
Date: 2017-07-10 00:00:00
Tasks: Object, object-detection, Object Detection

This paper introduces a device, algorithm and graphical user interface to
obtain anthropometric measurements of foot. Presented device facilitates
obtaining scale of image and image processing by taking one image from side
foot and underfoot simultaneously. Introduced image processing algorithm
minimizes a noise criterion, which is suitable for object detection in single
object images and outperforms famous image thresholding methods when lighting
condition is poor. Performance of image-based method is compared to manual
method. Image-based measurements of underfoot in average was 4mm less than
actual measures. Mean absolute error of underfoot length was 1.6mm, however
length obtained from side foot had 4.4mm mean absolute error. Furthermore,
based on t-test and f-test results, no significant difference between manual
and image-based anthropometry observed. In order to maintain anthropometry
process performance in different situations user interface designed for
handling changes in light conditions and altering speed of the algorithm.
Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features
Authors: Matteo Pagliardini, Prakhar Gupta, Martin Jaggi
Date: 2017-03-07 00:00:00
Proceeding: NAACL 2018 6
Tasks: Sentence, Sentence Embeddings, Word Embeddings

The recent tremendous success of unsupervised word embeddings in a multitude
of applications raises the obvious question if similar methods could be derived
to improve embeddings (i.e. semantic representations) of word sequences as
well. We present a simple but efficient unsupervised objective to train
distributed representations of sentences. Our method outperforms the
state-of-the-art unsupervised models on most benchmark tasks, highlighting the
robustness of the produced general-purpose sentence embeddings.
Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resize
Authors: Andrew Aitken, Christian Ledig, Lucas Theis, Jose Caballero, Zehan Wang, Wenzhe Shi
Date: 2017-07-10 00:00:00

The most prominent problem associated with the deconvolution layer is the
presence of checkerboard artifacts in output images and dense labels. To combat
this problem, smoothness constraints, post processing and different
architecture designs have been proposed. Odena et al. highlight three sources
of checkerboard artifacts: deconvolution overlap, random initialization and
loss functions. In this note, we proposed an initialization method for
sub-pixel convolution known as convolution NN resize. Compared to sub-pixel
convolution initialized with schemes designed for standard convolution kernels,
it is free from checkerboard artifacts immediately after initialization.
Compared to resize convolution, at the same computational complexity, it has
more modelling power and converges to solutions with smaller test errors.