pwc-archive/papers-with-abstracts huggingface dataset:
Modeling Context in Referring Expressions
Authors: Licheng Yu, Patrick Poirson, Shan Yang, Alexander C. Berg, Tamara L. Berg
Date: 2016-07-31 00:00:00
Tasks: Referring Expression, Referring expression generation, Text Generation

Humans refer to objects in their environments all the time, especially in
dialogue with other people. We explore generating and comprehending natural
language referring expressions for objects in images. In particular, we focus
on incorporating better measures of visual context into referring expression
models and find that visual comparison to other objects within an image helps
improve performance significantly. We also develop methods to tie the language
generation process together, so that we generate expressions for all objects of
a particular category jointly. Evaluation on three recent datasets - RefCOCO,
RefCOCO+, and RefCOCOg, shows the advantages of our methods for both referring
expression generation and comprehension.
Fractional Calculus In Image Processing: A Review
Authors: Qi Yang, Dali Chen, Tiebiao Zhao, YangQuan Chen
Date: 2016-08-10 00:00:00
Tasks: Denoising, Edge Detection, Image Compression, Image Denoising, Image Enhancement, Image Registration, Image Restoration, Image Segmentation, Semantic Segmentation

Over the last decade, it has been demonstrated that many systems in science
and engineering can be modeled more accurately by fractional-order than
integer-order derivatives, and many methods are developed to solve the problem
of fractional systems. Due to the extra free parameter order, fractional-order
based methods provide additional degree of freedom in optimization performance.
Not surprisingly, many fractional-order based methods have been used in image
processing field. Herein recent studies are reviewed in ten sub-fields, which
include image enhancement, image denoising, image edge detection, image
segmentation, image registration, image recognition, image fusion, image
encryption, image compression and image restoration. In sum, it is well proved
that as a fundamental mathematic tool, fractional-order derivative shows great
success in image processing.
A Stackelberg Game Perspective on the Conflict Between Machine Learning and Data Obfuscation
Authors: Jeffrey Pawlick, Quanyan Zhu
Date: 2016-06-21 00:00:00
Tasks: BIG-bench Machine Learning

Data is the new oil; this refrain is repeated extensively in the age of
internet tracking, machine learning, and data analytics. Social network
analysis, cookie-based advertising, and government surveillance are all
evidence of the use of data for commercial and national interests. Public
pressure, however, is mounting for the protection of privacy. Frameworks such
as differential privacy offer machine learning algorithms methods to guarantee
limits to information disclosure, but they are seldom implemented. Recently,
however, developers have made significant efforts to undermine tracking through
obfuscation tools that hide user characteristics in a sea of noise. These
services highlight an emerging clash between tracking and data obfuscation. In
this paper, we conceptualize this conflict through a dynamic game between users
and a machine learning algorithm that uses empirical risk minimization. First,
a machine learner declares a privacy protection level, and then users respond
by choosing their own perturbation amounts. We study the interaction between
the users and the learner using a Stackelberg game. The utility functions
quantify accuracy using expected loss and privacy in terms of the bounds of
differential privacy. In equilibrium, we find selfish users tend to cause
significant utility loss to trackers by perturbing heavily, in a phenomenon
reminiscent of public good games. Trackers, however, can improve the balance by
proactively perturbing the data themselves. While other work in this area has
studied privacy markets and mechanism design for truthful reporting of user
information, we take a different viewpoint by considering both user and learner
perturbation.
Demystifying Fixed k-Nearest Neighbor Information Estimators
Authors: Weihao Gao, Sewoong Oh, Pramod Viswanath
Date: 2016-04-11 00:00:00

Estimating mutual information from i.i.d. samples drawn from an unknown joint
density function is a basic statistical problem of broad interest with
multitudinous applications. The most popular estimator is one proposed by
Kraskov and St\"ogbauer and Grassberger (KSG) in 2004, and is nonparametric and
based on the distances of each sample to its $k^{\rm th}$ nearest neighboring
sample, where $k$ is a fixed small integer. Despite its widespread use (part of
scientific software packages), theoretical properties of this estimator have
been largely unexplored. In this paper we demonstrate that the estimator is
consistent and also identify an upper bound on the rate of convergence of the
bias as a function of number of samples. We argue that the superior performance
benefits of the KSG estimator stems from a curious "correlation boosting"
effect and build on this intuition to modify the KSG estimator in novel ways to
construct a superior estimator. As a byproduct of our investigations, we obtain
nearly tight rates of convergence of the $\ell_2$ error of the well known fixed
$k$ nearest neighbor estimator of differential entropy by Kozachenko and
Leonenko.
Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into Medical Image Segmentation
Authors: Naji Khosravan, Haydar Celik, Baris Turkbey, Ruida Cheng, Evan McCreedy, Matthew McAuliffe, Sandra Bednarova, Elizabeth Jones, Xinjian Chen, Peter L . Choyke, Bradford J. Wood, Ulas Bagci
Date: 2016-08-10 00:00:00
Tasks: Diagnostic, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

This study introduced a novel system, called Gaze2Segment, integrating
biological and computer vision techniques to support radiologists' reading
experience with an automatic image segmentation task. During diagnostic
assessment of lung CT scans, the radiologists' gaze information were used to
create a visual attention map. This map was then combined with a
computer-derived saliency map, extracted from the gray-scale CT images. The
visual attention map was used as an input for indicating roughly the location
of a object of interest. With computer-derived saliency information, on the
other hand, we aimed at finding foreground and background cues for the object
of interest. At the final step, these cues were used to initiate a seed-based
delineation process. Segmentation accuracy of the proposed Gaze2Segment was
found to be 86% with dice similarity coefficient and 1.45 mm with Hausdorff
distance. To the best of our knowledge, Gaze2Segment is the first true
integration of eye-tracking technology into a medical image segmentation task
without the need for any further user-interaction.
DeepCAMP: Deep Convolutional Action & Attribute Mid-Level Patterns
Authors: Ali Diba, Ali Mohammad Pazandeh, Hamed Pirsiavash, Luc van Gool
Date: 2016-08-10 00:00:00
Proceeding: CVPR 2016 6
Tasks: Attribute, Clustering

The recognition of human actions and the determination of human attributes
are two tasks that call for fine-grained classification. Indeed, often rather
small and inconspicuous objects and features have to be detected to tell their
classes apart. In order to deal with this challenge, we propose a novel
convolutional neural network that mines mid-level image patches that are
sufficiently dedicated to resolve the corresponding subtleties. In particular,
we train a newly de- signed CNN (DeepPattern) that learns discriminative patch
groups. There are two innovative aspects to this. On the one hand we pay
attention to contextual information in an origi- nal fashion. On the other
hand, we let an iteration of feature learning and patch clustering purify the
set of dedicated patches that we use. We validate our method for action clas-
sification on two challenging datasets: PASCAL VOC 2012 Action and Stanford 40
Actions, and for attribute recogni- tion we use the Berkeley Attributes of
People dataset. Our discriminative mid-level mining CNN obtains state-of-the-
art results on these datasets, without a need for annotations about parts and
poses.
Growing Graphs with Hyperedge Replacement Graph Grammars
Authors: Salvador Aguiñaga, Rodrigo Palacios, David Chiang, Tim Weninger
Date: 2016-08-10 00:00:00

Discovering the underlying structures present in large real world graphs is a
fundamental scientific problem. In this paper we show that a graph's clique
tree can be used to extract a hyperedge replacement grammar. If we store an
ordering from the extraction process, the extracted graph grammar is guaranteed
to generate an isomorphic copy of the original graph. Or, a stochastic
application of the graph grammar rules can be used to quickly create random
graphs. In experiments on large real world networks, we show that random
graphs, generated from extracted graph grammars, exhibit a wide range of
properties that are very similar to the original graphs. In addition to graph
properties like degree or eigenvector centrality, what a graph "looks like"
ultimately depends on small details in local graph substructures that are
difficult to define at a global level. We show that our generative graph model
is able to preserve these local substructures when generating new graphs and
performs well on new and difficult tests of model robustness.
Escaping Local Optima using Crossover with Emergent or Reinforced Diversity
Authors: Duc-Cuong Dang, Tobias Friedrich, Timo Kötzing, Martin S. Krejca, Per Kristian Lehre, Pietro S. Oliveto, Dirk Sudholt, Andrew M. Sutton
Date: 2016-08-10 00:00:00
Tasks: Diversity

Population diversity is essential for avoiding premature convergence in
Genetic Algorithms (GAs) and for the effective use of crossover. Yet the
dynamics of how diversity emerges in populations are not well understood. We
use rigorous runtime analysis to gain insight into population dynamics and GA
performance for the ($\mu$+1) GA and the $\text{Jump}_k$ test function. We show
that the interplay of crossover and mutation may serve as a catalyst leading to
a sudden burst of diversity. This leads to improvements of the expected
optimisation time of order $\Omega(n/\log n)$ compared to mutation-only
algorithms like (1+1) EA. Moreover, increasing the mutation rate by an
arbitrarily small constant factor can facilitate the generation of diversity,
leading to speedups of order $\Omega(n)$. We also compare seven commonly used
diversity mechanisms and evaluate their impact on runtime bounds for the
($\mu$+1) GA. All previous results in this context only hold for
unrealistically low crossover probability $p_c=O(k/n)$, while we give analyses
for the setting of constant $p_c < 1$ in all but one case.
  For the typical case of constant $k > 2$ and constant $p_c$, we can compare
the resulting expected runtimes for different diversity mechanisms assuming an
optimal choice of $\mu$: $O(n^{k-1})$ for duplicate elimination/minim.,
$O(n^2\log n)$ for maximising the convex hull, $O(n\log n)$ for deterministic
crowding (assuming $p_c = k/n$), $O(n\log n)$ for maximising Hamming distance,
$O(n\log n)$ for fitness sharing, $O(n\log n)$ for single-receiver island
model.
  This proves a sizeable advantage of all variants of the ($\mu$+1) GA compared
to (1+1) EA, which requires time $\Theta(n^k)$. Experiments complement our
theoretical findings and further highlight the benefits of crossover and
diversity on $\text{Jump}_k$.
Estimation from Indirect Supervision with Linear Moments
Authors: Aditi Raghunathan, Roy Frostig, John Duchi, Percy Liang
Date: 2016-08-10 00:00:00
Tasks: Structured Prediction

In structured prediction problems where we have indirect supervision of the
output, maximum marginal likelihood faces two computational obstacles:
non-convexity of the objective and intractability of even a single gradient
computation. In this paper, we bypass both obstacles for a class of what we
call linear indirectly-supervised problems. Our approach is simple: we solve a
linear system to estimate sufficient statistics of the model, which we then use
to estimate parameters via convex optimization. We analyze the statistical
properties of our approach and show empirically that it is effective in two
settings: learning with local privacy constraints and learning from low-cost
count-based annotations.
Object Detection, Tracking, and Motion Segmentation for Object-level Video Segmentation
Authors: Benjamin Drayer, Thomas Brox
Date: 2016-08-10 00:00:00
Tasks: Motion Segmentation, Object, object-detection, Object Detection, Object Tracking, Segmentation, Semantic Segmentation, Video Segmentation, Video Semantic Segmentation

We present an approach for object segmentation in videos that combines
frame-level object detection with concepts from object tracking and motion
segmentation. The approach extracts temporally consistent object tubes based on
an off-the-shelf detector. Besides the class label for each tube, this provides
a location prior that is independent of motion. For the final video
segmentation, we combine this information with motion cues. The method
overcomes the typical problems of weakly supervised/unsupervised video
segmentation, such as scenes with no motion, dominant camera motion, and
objects that move as a unit. In contrast to most tracking methods, it provides
an accurate, temporally consistent segmentation of each object. We report
results on four video segmentation datasets: YouTube Objects, SegTrackv2,
egoMotion, and FBMS.
An assessment of orthographic similarity measures for several African languages
Authors: C. Maria Keet
Date: 2016-08-10 00:00:00

Natural Language Interfaces and tools such as spellcheckers and Web search in
one's own language are known to be useful in ICT-mediated communication. Most
languages in Southern Africa are under-resourced, however. Therefore, it would
be very useful if both the generic and the few language-specific NLP tools
could be reused or easily adapted across languages. This depends on the notion,
and extent, of similarity between the languages. We assess this from the angle
of orthography and corpora. Twelve versions of the Universal Declaration of
Human Rights (UDHR) are examined, showing clusters of languages, and which are
thus more or less amenable to cross-language adaptation of NLP tools, which do
not match with Guthrie zones. To examine the generalisability of these results,
we zoom in on isiZulu both quantitatively and qualitatively with four other
corpora and texts in different genres. The results show that the UDHR is a
typical text document orthographically. The results also provide insight into
usability of typical measures such as lexical diversity and genre, and that the
same statistic may mean different things in different documents. While NLTK for
Python could be used for basic analyses of text, it, and similar NLP tools,
will need considerable customization.
Fashion Landmark Detection in the Wild
Authors: Ziwei Liu, Sijie Yan, Ping Luo, Xiaogang Wang, Xiaoou Tang
Date: 2016-08-10 00:00:00
Tasks: Attribute, Pose Estimation, Retrieval

Visual fashion analysis has attracted many attentions in the recent years.
Previous work represented clothing regions by either bounding boxes or human
joints. This work presents fashion landmark detection or fashion alignment,
which is to predict the positions of functional key points defined on the
fashion items, such as the corners of neckline, hemline, and cuff. To encourage
future studies, we introduce a fashion landmark dataset with over 120K images,
where each image is labeled with eight landmarks. With this dataset, we study
fashion alignment by cascading multiple convolutional neural networks in three
stages. These stages gradually improve the accuracies of landmark predictions.
Extensive experiments demonstrate the effectiveness of the proposed method, as
well as its generalization ability to pose estimation. Fashion landmark is also
compared to clothing bounding boxes and human joints in two applications,
fashion attribute prediction and clothes retrieval, showing that fashion
landmark is a more discriminative representation to understand fashion images.
Holophrasm: a neural Automated Theorem Prover for higher-order logic
Authors: Daniel Whalen
Date: 2016-08-08 00:00:00
Tasks: Automated Theorem Proving

I propose a system for Automated Theorem Proving in higher order logic using
deep learning and eschewing hand-constructed features. Holophrasm exploits the
formalism of the Metamath language and explores partial proof trees using a
neural-network-augmented bandit algorithm and a sequence-to-sequence model for
action enumeration. The system proves 14% of its test theorems from Metamath's
set.mm module.
Hierarchical Character-Word Models for Language Identification
Authors: Aaron Jaech, George Mulcaire, Shobhit Hathi, Mari Ostendorf, Noah A. Smith
Date: 2016-08-10 00:00:00
Proceeding: WS 2016 11
Tasks: Language Identification

Social media messages' brevity and unconventional spelling pose a challenge
to language identification. We introduce a hierarchical model that learns
character and contextualized word-level representations for language
identification. Our method performs well against strong base- lines, and can
also reveal code-switching.
Domain Adaptation of Recurrent Neural Networks for Natural Language Understanding
Authors: Aaron Jaech, Larry Heck, Mari Ostendorf
Date: 2016-04-01 00:00:00
Tasks: Domain Adaptation, Multi-Task Learning, Natural Language Understanding, slot-filling, Slot Filling

The goal of this paper is to use multi-task learning to efficiently scale
slot filling models for natural language understanding to handle multiple
target tasks or domains. The key to scalability is reducing the amount of
training data needed to learn a model for a new task. The proposed multi-task
model delivers better performance with less data by leveraging patterns that it
learns from the other tasks. The approach supports an open vocabulary, which
allows the models to generalize to unseen words, which is particularly
important when very little training data is used. A newly collected
crowd-sourced data set, covering four different domains, is used to demonstrate
the effectiveness of the domain adaptation and open vocabulary techniques.
Towards Visual Type Theory as a Mathematical Tool and Mathematical User Interface
Authors: Lucius Schoenbaum
Date: 2016-08-10 00:00:00
Tasks: Management, Vocal Bursts Type Prediction

A visual type theory is a cognitive tool that has much in common with
language, and may be regarded as an exceptional form of spatial text adjunct. A
mathematical visual type theory, called NPM, has been under development that
can be viewed as an early-stage project in mathematical knowledge management
and mathematical user interface development. We discuss in greater detail the
notion of a visual type theory, report on progress towards a usable
mathematical visual type theory, and discuss the outlook for future work on
this project.
Dynamic Principal Component Analysis: Identifying the Relationship between Multiple Air Pollutants
Authors: Oleg Melnikov, Loren H. Raun, Katherine B. Ensor
Date: 2016-08-10 00:00:00
Tasks: Time Series Analysis

The dynamic nature of air quality chemistry and transport makes it difficult
to identify the mixture of air pollutants for a region. In this study of air
quality in the Houston metropolitan area we apply dynamic principal component
analysis (DPCA) to a normalized multivariate time series of daily concentration
measurements of five pollutants (O3, CO, NO2, SO2, PM2.5) from January 1, 2009
through December 31, 2011 for each of the 24 hours in a day. The resulting
dynamic components are examined by hour across days for the 3 year period.
Diurnal and seasonal patterns are revealed underlining times when DPCA performs
best and two principal components (PCs) explain most variability in the
multivariate series. DPCA is shown to be superior to static principal component
analysis (PCA) in discovery of linear relations among transformed pollutant
measurements. DPCA captures the time-dependent correlation structure of the
underlying pollutants recorded at up to 34 monitoring sites in the region. In
winter mornings the first principal component (PC1) (mainly CO and NO2)
explains up to 70% of variability. Augmenting with the second principal
component (PC2) (mainly driven by SO2) the explained variability rises to 90%.
In the afternoon, O3 gains prominence in the second principal component. The
seasonal profile of PCs' contribution to variance loses its distinction in the
afternoon, yet cumulatively PC1 and PC2 still explain up to 65% of variability
in ambient air data. DPCA provides a strategy for identifying the changing air
quality profile for the region studied.
Convex Factorization Machine for Regression
Authors: Makoto Yamada, Wenzhao Lian, Amit Goyal, Jianhui Chen, Kishan Wimalawarne, Suleiman A. Khan, Samuel Kaski, Hiroshi Mamitsuka, Yi Chang
Date: 2015-07-04 00:00:00
Tasks: regression

We propose the convex factorization machine (CFM), which is a convex variant
of the widely used Factorization Machines (FMs). Specifically, we employ a
linear+quadratic model and regularize the linear term with the
$\ell_2$-regularizer and the quadratic term with the trace norm regularizer.
Then, we formulate the CFM optimization as a semidefinite programming problem
and propose an efficient optimization procedure with Hazan's algorithm. A key
advantage of CFM over existing FMs is that it can find a globally optimal
solution, while FMs may get a poor locally optimal solution since the objective
function of FMs is non-convex. In addition, the proposed algorithm is simple
yet effective and can be implemented easily. Finally, CFM is a general
factorization method and can also be used for other factorization problems
including including multi-view matrix factorization and tensor completion
problems. Through synthetic and movielens datasets, we first show that the
proposed CFM achieves results competitive to FMs. Furthermore, in a
toxicogenomics prediction task, we show that CFM outperforms a state-of-the-art
tensor factorization method.
Robust Object Tracking with a Hierarchical Ensemble Framework
Authors: Mengmeng Wang, Yong liu
Date: 2015-09-23 00:00:00
Tasks: Object, Object Tracking

Autonomous robots enjoy a wide popularity nowadays and have been applied in
many applications, such as home security, entertainment, delivery, navigation
and guidance. It is vital to robots to track objects accurately in these
applications, so it is necessary to focus on tracking algorithms to improve the
robustness and accuracy. In this paper, we propose a robust object tracking
algorithm based on a hierarchical ensemble framework which can incorporate
information including individual pixel features, local patches and holistic
target models. The framework combines multiple ensemble models simultaneously
instead of using a single ensemble model individually. A discriminative model
which accounts for the matching degree of local patches is adopted via a bottom
ensemble layer, and a generative model which exploits holistic templates is
used to search for the object through the middle ensemble layer as well as an
adaptive Kalman filter. We test the proposed tracker on challenging benchmark
image sequences. Both qualitative and quantitative evaluations demonstrate that
the proposed tracker performs superiorly against several state-of-the-art
algorithms, especially when the appearance changes dramatically and the
occlusions occur.