pwc-archive/papers-with-abstracts huggingface dataset:
Shading Annotations in the Wild
Authors: Balazs Kovacs, Sean Bell, Noah Snavely, Kavita Bala
Date: 2017-05-02 00:00:00
Proceeding: CVPR 2017 7
Tasks: Image Relighting, Intrinsic Image Decomposition, Inverse Rendering, Shadow Removal

Understanding shading effects in images is critical for a variety of vision
and graphics problems, including intrinsic image decomposition, shadow removal,
image relighting, and inverse rendering. As is the case with other vision
tasks, machine learning is a promising approach to understanding shading - but
there is little ground truth shading data available for real-world images. We
introduce Shading Annotations in the Wild (SAW), a new large-scale, public
dataset of shading annotations in indoor scenes, comprised of multiple forms of
shading judgments obtained via crowdsourcing, along with shading annotations
automatically generated from RGB-D imagery. We use this data to train a
convolutional neural network to predict per-pixel shading information in an
image. We demonstrate the value of our data and network in an application to
intrinsic images, where we can reduce decomposition artifacts produced by
existing algorithms. Our database is available at
http://opensurfaces.cs.cornell.edu/saw/.
Out-of-focus: Learning Depth from Image Bokeh for Robotic Perception
Authors: Eric Cristofalo, Zijian Wang
Date: 2017-05-02 00:00:00
Tasks: Depth Estimation

In this project, we propose a novel approach for estimating depth from RGB
images. Traditionally, most work uses a single RGB image to estimate depth,
which is inherently difficult and generally results in poor performance, even
with thousands of data examples. In this work, we alternatively use multiple
RGB images that were captured while changing the focus of the camera's lens.
This method leverages the natural depth information correlated to the different
patterns of clarity/blur in the sequence of focal images, which helps
distinguish objects at different depths. Since no such data set exists for
learning this mapping, we collect our own data set using customized hardware.
We then use a convolutional neural network for learning the depth from the
stacked focal images. Comparative studies were conducted on both a standard
RGBD data set and our own data set (learning from both single and multiple
images), and results verified that stacked focal images yield better depth
estimation than using just single RGB image.
Recovery of structure of looped jointed objects from multiframes
Authors: Mieczysław Kłopotek
Date: 2017-05-02 00:00:00

A method to recover structural parameters of looped jointed objects from
multiframes is being developed. Each rigid part of the jointed body needs only
to be traced at two (that is at junction) points.
  This method has been linearized for 4-part loops, with recovery from at least
19 frames.
Parameterized Principal Component Analysis
Authors: Ajay Gupta, Adrian Barbu
Date: 2016-08-16 00:00:00

When modeling multivariate data, one might have an extra parameter of
contextual information that could be used to treat some observations as more
similar to others. For example, images of faces can vary by age, and one would
expect the face of a 40 year old to be more similar to the face of a 30 year
old than to a baby face. We introduce a novel manifold approximation method,
parameterized principal component analysis (PPCA) that models data with linear
subspaces that change continuously according to the extra parameter of
contextual information (e.g. age), instead of ad-hoc atlases. Special care has
been taken in the loss function and the optimization method to encourage
smoothly changing subspaces across the parameter values. The approach ensures
that each observation's projection will share information with observations
that have similar parameter values, but not with observations that have large
parameter differences. We tested PPCA on artificial data based on known, smooth
functions of an added parameter, as well as on three real datasets with
different types of parameters. We compared PPCA to PCA, sparse PCA and to
independent principal component analysis (IPCA), which groups observations by
their parameter values and projects each group using PCA with no sharing of
information for different groups. PPCA recovers the known functions with less
error and projects the datasets' test set observations with consistently less
reconstruction error than IPCA does. In some cases where the manifold is truly
nonlinear, PCA outperforms all the other manifold approximation methods
compared.
Summarized Network Behavior Prediction
Authors: Shih-Chieh Su
Date: 2017-05-02 00:00:00
Tasks: Prediction

This work studies the entity-wise topical behavior from massive network logs.
Both the temporal and the spatial relationships of the behavior are explored
with the learning architectures combing the recurrent neural network (RNN) and
the convolutional neural network (CNN). To make the behavioral data appropriate
for the spatial learning in CNN, several reduction steps are taken to form the
topical metrics and place them homogeneously like pixels in the images. The
experimental result shows both the temporal- and the spatial- gains when
compared to a multilayer perceptron (MLP) network. A new learning framework
called spatially connected convolutional networks (SCCN) is introduced to more
efficiently predict the behavior.
PDE approach to the problem of online prediction with expert advice: a construction of potential-based strategies
Authors: Dmitry B. Rokhlin
Date: 2017-05-02 00:00:00
Tasks: Prediction

We consider a sequence of repeated prediction games and formally pass to the
limit. The supersolutions of the resulting non-linear parabolic partial
differential equation are closely related to the potential functions in the
sense of N.\,Cesa-Bianci, G.\,Lugosi (2003). Any such supersolution gives an
upper bound for forecaster's regret and suggests a potential-based prediction
strategy, satisfying the Blackwell condition. A conventional upper bound for
the worst-case regret is justified by a simple verification argument.
An improved Ant Colony System for the Sequential Ordering Problem
Authors: Rafał Skinderowicz
Date: 2017-05-02 00:00:00

It is not rare that the performance of one metaheuristic algorithm can be
improved by incorporating ideas taken from another. In this article we present
how Simulated Annealing (SA) can be used to improve the efficiency of the Ant
Colony System (ACS) and Enhanced ACS when solving the Sequential Ordering
Problem (SOP). Moreover, we show how the very same ideas can be applied to
improve the convergence of a dedicated local search, i.e. the SOP-3-exchange
algorithm. A statistical analysis of the proposed algorithms both in terms of
finding suitable parameter values and the quality of the generated solutions is
presented based on a series of computational experiments conducted on SOP
instances from the well-known TSPLIB and SOPLIB2006 repositories. The proposed
ACS-SA and EACS-SA algorithms often generate solutions of better quality than
the ACS and EACS, respectively. Moreover, the EACS-SA algorithm combined with
the proposed SOP-3-exchange-SA local search was able to find 10 new best
solutions for the SOP instances from the SOPLIB2006 repository, thus improving
the state-of-the-art results as known from the literature. Overall, the best
known or improved solutions were found in 41 out of 48 cases.
Increasing Papers' Discoverability with Precise Semantic Labeling: the sci.AI Platform
Authors: Roman Gurinovich, Alexander Pashuk, Yuriy Petrovskiy, Alex Dmitrievskij, Oleg Kuryan, Alexei Scerbacov, Antonia Tiggre, Elena Moroz, Yuri Nikolsky
Date: 2017-05-02 00:00:00
Tasks: Retrieval

The number of published findings in biomedicine increases continually. At the
same time, specifics of the domain's terminology complicates the task of
relevant publications retrieval. In the current research, we investigate
influence of terms' variability and ambiguity on a paper's likelihood of being
retrieved. We obtained statistics that demonstrate significance of the issue
and its challenges, followed by presenting the sci.AI platform, which allows
precise terms labeling as a resolution.
Entity Linking with people entity on Wikipedia
Authors: Weiqian Yan, Kanchan Khurad
Date: 2017-05-02 00:00:00
Tasks: coreference-resolution, Coreference Resolution, Entity Linking, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

This paper introduces a new model that uses named entity recognition,
coreference resolution, and entity linking techniques, to approach the task of
linking people entities on Wikipedia people pages to their corresponding
Wikipedia pages if applicable. Our task is different from general and
traditional entity linking because we are working in a limited domain, namely,
people entities, and we are including pronouns as entities, whereas in the
past, pronouns were never considered as entities in entity linking. We have
built 2 models, both outperforms our baseline model significantly. The purpose
of our project is to build a model that could be use to generate cleaner data
for future entity linking tasks. Our contribution include a clean data set
consisting of 50Wikipedia people pages, and 2 entity linking models,
specifically tuned for this domain.
SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications
Authors: Isabelle Augenstein, Mrinal Das, Sebastian Riedel, Lakshmi Vikraman, Andrew McCallum
Date: 2017-04-10 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Knowledge Base Population

We describe the SemEval task of extracting keyphrases and relations between
them from scientific documents, which is crucial for understanding which
publications describe which processes, tasks and materials. Although this was a
new task, we had a total of 26 submissions across 3 evaluation scenarios. We
expect the task and the findings reported in this paper to be relevant for
researchers working on understanding scientific content, as well as the broader
knowledge base population and information extraction communities.
A projection pursuit framework for testing general high-dimensional hypothesis
Authors: Yinchu Zhu, Jelena Bradic
Date: 2017-05-02 00:00:00
Tasks: Variable Selection, Vocal Bursts Intensity Prediction

This article develops a framework for testing general hypothesis in
high-dimensional models where the number of variables may far exceed the number
of observations. Existing literature has considered less than a handful of
hypotheses, such as testing individual coordinates of the model parameter.
However, the problem of testing general and complex hypotheses remains widely
open. We propose a new inference method developed around the hypothesis
adaptive projection pursuit framework, which solves the testing problems in the
most general case. The proposed inference is centered around a new class of
estimators defined as $l_1$ projection of the initial guess of the unknown onto
the space defined by the null. This projection automatically takes into account
the structure of the null hypothesis and allows us to study formal inference
for a number of long-standing problems. For example, we can directly conduct
inference on the sparsity level of the model parameters and the minimum signal
strength. This is especially significant given the fact that the former is a
fundamental condition underlying most of the theoretical development in
high-dimensional statistics, while the latter is a key condition used to
establish variable selection properties. Moreover, the proposed method is
asymptotically exact and has satisfactory power properties for testing very
general functionals of the high-dimensional parameters. The simulation studies
lend further support to our theoretical claims and additionally show excellent
finite-sample size and power properties of the proposed test.
Modeling Source Syntax for Neural Machine Translation
Authors: Junhui Li, Deyi Xiong, Zhaopeng Tu, Muhua Zhu, Min Zhang, Guodong Zhou
Date: 2017-05-02 00:00:00
Proceeding: ACL 2017 7
Tasks: Machine Translation, NMT, Translation

Even though a linguistics-free sequence to sequence model in neural machine
translation (NMT) has certain capability of implicitly learning syntactic
information of source sentences, this paper shows that source syntax can be
explicitly incorporated into NMT effectively to provide further improvements.
Specifically, we linearize parse trees of source sentences to obtain structural
label sequences. On the basis, we propose three different sorts of encoders to
incorporate source syntax into NMT: 1) Parallel RNN encoder that learns word
and label annotation vectors parallelly; 2) Hierarchical RNN encoder that
learns word and label annotation vectors in a two-level hierarchy; and 3) Mixed
RNN encoder that stitchingly learns word and label annotation vectors over
sequences where words and labels are mixed. Experimentation on
Chinese-to-English translation demonstrates that all the three proposed
syntactic encoders are able to improve translation accuracy. It is interesting
to note that the simplest RNN encoder, i.e., Mixed RNN encoder yields the best
performance with an significant improvement of 1.4 BLEU points. Moreover, an
in-depth analysis from several perspectives is provided to reveal how source
syntax benefits NMT.
An Optimal Treatment Assignment Strategy to Evaluate Demand Response Effect
Authors: Pan Li, Baosen Zhang
Date: 2016-10-02 00:00:00
Tasks: Experimental Design

Demand response is designed to motivate electricity customers to modify their
loads at critical time periods. The accurate estimation of impact of demand
response signals to customers' consumption is central to any successful
program. In practice, learning these response is nontrivial because operators
can only send a limited number of signals. In addition, customer behavior also
depends on a large number of exogenous covariates. These two features lead to a
high dimensional inference problem with limited number of observations. In this
paper, we formulate this problem by using a multivariate linear model and adopt
an experimental design approach to estimate the impact of demand response
signals. We show that randomized assignment, which is widely used to estimate
the average treatment effect, is not efficient in reducing the variance of the
estimator when a large number of covariates is present. In contrast, we present
a tractable algorithm that strategically assigns demand response signals to
customers. This algorithm achieves the optimal reduction in estimation
variance, independent of the number of covariates. The results are validated
from simulations on synthetic data.
Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search
Authors: Chris Hokamp, Qun Liu
Date: 2017-04-24 00:00:00
Proceeding: ACL 2017 7
Tasks: Domain Adaptation, Machine Translation, Translation

We present Grid Beam Search (GBS), an algorithm which extends beam search to
allow the inclusion of pre-specified lexical constraints. The algorithm can be
used with any model that generates a sequence $ \mathbf{\hat{y}} =
\{y_{0}\ldots y_{T}\} $, by maximizing $ p(\mathbf{y} | \mathbf{x}) =
\prod\limits_{t}p(y_{t} | \mathbf{x}; \{y_{0} \ldots y_{t-1}\}) $. Lexical
constraints take the form of phrases or words that must be present in the
output sequence. This is a very general way to incorporate additional knowledge
into a model's output without requiring any modification of the model
parameters or training data. We demonstrate the feasibility and flexibility of
Lexically Constrained Decoding by conducting experiments on Neural
Interactive-Predictive Translation, as well as Domain Adaptation for Neural
Machine Translation. Experiments show that GBS can provide large improvements
in translation quality in interactive scenarios, and that, even without any
user input, GBS can be used to achieve significant gains in performance in
domain adaptation scenarios.
Scalable Surface Reconstruction from Point Clouds with Extreme Scale and Density Diversity
Authors: Christian Mostegel, Rudolf Prettenthaler, Friedrich Fraundorfer, Horst Bischof
Date: 2017-05-02 00:00:00
Proceeding: CVPR 2017 7
Tasks: Diversity, Surface Reconstruction

In this paper we present a scalable approach for robustly computing a 3D
surface mesh from multi-scale multi-view stereo point clouds that can handle
extreme jumps of point density (in our experiments three orders of magnitude).
The backbone of our approach is a combination of octree data partitioning,
local Delaunay tetrahedralization and graph cut optimization. Graph cut
optimization is used twice, once to extract surface hypotheses from local
Delaunay tetrahedralizations and once to merge overlapping surface hypotheses
even when the local tetrahedralizations do not share the same topology.This
formulation allows us to obtain a constant memory consumption per sub-problem
while at the same time retaining the density independent interpolation
properties of the Delaunay-based optimization. On multiple public datasets, we
demonstrate that our approach is highly competitive with the state-of-the-art
in terms of accuracy, completeness and outlier resilience. Further, we
demonstrate the multi-scale potential of our approach by processing a newly
recorded dataset with 2 billion points and a point density variation of more
than four orders of magnitude - requiring less than 9GB of RAM per process.
A Novel Approach to Forecasting Financial Volatility with Gaussian Process Envelopes
Authors: Syed Ali Asad Rizvi, Stephen J. Roberts, Michael A. Osborne, Favour Nyikosa
Date: 2017-05-02 00:00:00
Tasks: regression, Time Series, Time Series Analysis

In this paper we use Gaussian Process (GP) regression to propose a novel
approach for predicting volatility of financial returns by forecasting the
envelopes of the time series. We provide a direct comparison of their
performance to traditional approaches such as GARCH. We compare the forecasting
power of three approaches: GP regression on the absolute and squared returns;
regression on the envelope of the returns and the absolute returns; and
regression on the envelope of the negative and positive returns separately. We
use a maximum a posteriori estimate with a Gaussian prior to determine our
hyperparameters. We also test the effect of hyperparameter updating at each
forecasting step. We use our approaches to forecast out-of-sample volatility of
four currency pairs over a 2 year period, at half-hourly intervals. From three
kernels, we select the kernel giving the best performance for our data. We use
two published accuracy measures and four statistical loss functions to evaluate
the forecasting ability of GARCH vs GPs. In mean squared error the GP's perform
20% better than a random walk model, and 50% better than GARCH for the same
data.
Transfer Learning by Ranking for Weakly Supervised Object Annotation
Authors: Zhiyuan Shi, Parthipan Siva, Tao Xiang
Date: 2017-05-02 00:00:00
Tasks: Learning-To-Rank, Object, Transfer Learning

Most existing approaches to training object detectors rely on fully
supervised learning, which requires the tedious manual annotation of object
location in a training set. Recently there has been an increasing interest in
developing weakly supervised approach to detector training where the object
location is not manually annotated but automatically determined based on binary
(weak) labels indicating if a training image contains the object. This is a
challenging problem because each image can contain many candidate object
locations which partially overlaps the object of interest. Existing approaches
focus on how to best utilise the binary labels for object location annotation.
In this paper we propose to solve this problem from a very different
perspective by casting it as a transfer learning problem. Specifically, we
formulate a novel transfer learning based on learning to rank, which
effectively transfers a model for automatic annotation of object location from
an auxiliary dataset to a target dataset with completely unrelated object
categories. We show that our approach outperforms existing state-of-the-art
weakly supervised approach to annotating objects in the challenging VOC
dataset.
Deep Neural Machine Translation with Linear Associative Unit
Authors: Mingxuan Wang, Zhengdong Lu, Jie zhou, Qun Liu
Date: 2017-05-02 00:00:00
Proceeding: ACL 2017 7
Tasks: Decoder, Machine Translation, NMT, Translation

Deep Neural Networks (DNNs) have provably enhanced the state-of-the-art
Neural Machine Translation (NMT) with their capability in modeling complex
functions and capturing complex linguistic structures. However NMT systems with
deep architecture in their encoder or decoder RNNs often suffer from severe
gradient diffusion due to the non-linear recurrent activations, which often
make the optimization much more difficult. To address this problem we propose
novel linear associative units (LAU) to reduce the gradient propagation length
inside the recurrent unit. Different from conventional approaches (LSTM unit
and GRU), LAUs utilizes linear associative connections between input and output
of the recurrent unit, which allows unimpeded information flow through both
space and time direction. The model is quite simple, but it is surprisingly
effective. Our empirical study on Chinese-English translation shows that our
model with proper configuration can improve by 11.7 BLEU upon Groundhog and the
best reported results in the same setting. On WMT14 English-German task and a
larger WMT14 English-French task, our model achieves comparable results with
the state-of-the-art.
Exploring Context with Deep Structured models for Semantic Segmentation
Authors: Guosheng Lin, Chunhua Shen, Anton Van Den Hengel, Ian Reid
Date: 2016-03-10 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

State-of-the-art semantic image segmentation methods are mostly based on
training deep convolutional neural networks (CNNs). In this work, we proffer to
improve semantic segmentation with the use of contextual information. In
particular, we explore `patch-patch' context and `patch-background' context in
deep CNNs. We formulate deep structured models by combining CNNs and
Conditional Random Fields (CRFs) for learning the patch-patch context between
image regions. Specifically, we formulate CNN-based pairwise potential
functions to capture semantic correlations between neighboring patches.
Efficient piecewise training of the proposed deep structured model is then
applied in order to avoid repeated expensive CRF inference during the course of
back propagation. For capturing the patch-background context, we show that a
network design with traditional multi-scale image inputs and sliding pyramid
pooling is very effective for improving performance. We perform comprehensive
evaluation of the proposed method. We achieve new state-of-the-art performance
on a number of challenging semantic segmentation datasets including $NYUDv2$,
$PASCAL$-$VOC2012$, $Cityscapes$, $PASCAL$-$Context$, $SUN$-$RGBD$,
$SIFT$-$flow$, and $KITTI$ datasets. Particularly, we report an
intersection-over-union score of $77.8$ on the $PASCAL$-$VOC2012$ dataset.
A comparative study of counterfactual estimators
Authors: Thomas Nedelec, Nicolas Le Roux, Vianney Perchet
Date: 2017-04-03 00:00:00
Tasks: counterfactual

We provide a comparative study of several widely used off-policy estimators
(Empirical Average, Basic Importance Sampling and Normalized Importance
Sampling), detailing the different regimes where they are individually
suboptimal. We then exhibit properties optimal estimators should possess. In
the case where examples have been gathered using multiple policies, we show
that fused estimators dominate basic ones but can still be improved.