pwc-archive/papers-with-abstracts huggingface dataset:
Context-Dependent Fine-Grained Entity Type Tagging
Authors: Dan Gillick, Nevena Lazic, Kuzman Ganchev, Jesse Kirchner, David Huynh
Date: 2014-12-03 00:00:00
Tasks: Sentence, Vocal Bursts Type Prediction

Entity type tagging is the task of assigning category labels to each mention
of an entity in a document. While standard systems focus on a small set of
types, recent work (Ling and Weld, 2012) suggests that using a large
fine-grained label set can lead to dramatic improvements in downstream tasks.
In the absence of labeled training data, existing fine-grained tagging systems
obtain examples automatically, using resolved entities and their types
extracted from a knowledge base. However, since the appropriate type often
depends on context (e.g. Washington could be tagged either as city or
government), this procedure can result in spurious labels, leading to poorer
generalization. We propose the task of context-dependent fine type tagging,
where the set of acceptable labels for a mention is restricted to only those
deducible from the local context (e.g. sentence or document). We introduce new
resources for this task: 12,017 mentions annotated with their context-dependent
fine types, and we provide baseline experimental results on this data.
Where Is My Puppy? Retrieving Lost Dogs by Facial Features
Authors: Thierry Pinheiro Moreira, Mauricio Lisboa Perez, Rafael de Oliveira Werneck, Eduardo Valle
Date: 2015-10-09 00:00:00
Tasks: Face Recognition

A pet that goes missing is among many people's worst fears: a moment of
distraction is enough for a dog or a cat wandering off from home. Some measures
help matching lost animals to their owners; but automated visual recognition is
one that - although convenient, highly available, and low-cost - is
surprisingly overlooked. In this paper, we inaugurate that promising avenue by
pursuing face recognition for dogs. We contrast four ready-to-use human facial
recognizers (EigenFaces, FisherFaces, LBPH, and a Sparse method) to two
original solutions based upon convolutional neural networks: BARK (inspired in
architecture-optimized networks employed for human facial recognition) and WOOF
(based upon off-the-shelf OverFeat features). Human facial recognizers perform
poorly for dogs (up to 60.5% accuracy), showing that dog facial recognition is
not a trivial extension of human facial recognition. The convolutional network
solutions work much better, with BARK attaining up to 81.1% accuracy, and WOOF,
89.4%. The tests were conducted in two datasets: Flickr-dog, with 42 dogs of
two breeds (pugs and huskies); and Snoopybook, with 18 mongrel dogs.
Theory of the GMM Kernel
Authors: Ping Li, Cun-Hui Zhang
Date: 2016-08-01 00:00:00
Tasks: BIG-bench Machine Learning

We develop some theoretical results for a robust similarity measure named
"generalized min-max" (GMM). This similarity has direct applications in machine
learning as a positive definite kernel and can be efficiently computed via
probabilistic hashing. Owing to the discrete nature, the hashed values can also
be used for efficient near neighbor search. We prove the theoretical limit of
GMM and the consistency result, assuming that the data follow an elliptical
distribution, which is a very general family of distributions and includes the
multivariate $t$-distribution as a special case. The consistency result holds
as long as the data have bounded first moment (an assumption which essentially
holds for datasets commonly encountered in practice). Furthermore, we establish
the asymptotic normality of GMM. Compared to the "cosine" similarity which is
routinely adopted in current practice in statistics and machine learning, the
consistency of GMM requires much weaker conditions. Interestingly, when the
data follow the $t$-distribution with $\nu$ degrees of freedom, GMM typically
provides a better measure of similarity than "cosine" roughly when $\nu<8$
(which is already very close to normal). These theoretical results will help
explain the recent success of GMM in learning tasks.
Modeling Context Between Objects for Referring Expression Understanding
Authors: Varun K. Nagaraja, Vlad I. Morariu, Larry S. Davis
Date: 2016-08-01 00:00:00
Tasks: Multiple Instance Learning, Object, Referring Expression

Referring expressions usually describe an object using properties of the
object and relationships of the object with other objects. We propose a
technique that integrates context between objects to understand referring
expressions. Our approach uses an LSTM to learn the probability of a referring
expression, with input features from a region and a context region. The context
regions are discovered using multiple-instance learning (MIL) since annotations
for context objects are generally not available for training. We utilize
max-margin based MIL objective functions for training the LSTM. Experiments on
the Google RefExp and UNC RefExp datasets show that modeling context between
objects provides better performance than modeling only object properties. We
also qualitatively show that our technique can ground a referring expression to
its referred region along with the supporting context region.
A Neural Approach to Blind Motion Deblurring
Authors: Ayan Chakrabarti
Date: 2016-03-15 00:00:00
Tasks: Deblurring, GPU

We present a new method for blind motion deblurring that uses a neural
network trained to compute estimates of sharp image patches from observations
that are blurred by an unknown motion kernel. Instead of regressing directly to
patch intensities, this network learns to predict the complex Fourier
coefficients of a deconvolution filter to be applied to the input patch for
restoration. For inference, we apply the network independently to all
overlapping patches in the observed image, and average its outputs to form an
initial estimate of the sharp image. We then explicitly estimate a single
global blur kernel by relating this estimate to the observed image, and finally
perform non-blind deconvolution with this kernel. Our method exhibits accuracy
and robustness close to state-of-the-art iterative methods, while being much
faster when parallelized on GPU hardware.
Detection of Epigenomic Network Community Oncomarkers
Authors: Thomas E. Bartlett, Alexey Zaikin
Date: 2015-06-17 00:00:00

In this paper we propose network methodology to infer prognostic cancer
biomarkers based on the epigenetic pattern DNA methylation. Epigenetic
processes such as DNA methylation reflect environmental risk factors, and are
increasingly recognised for their fundamental role in diseases such as cancer.
DNA methylation is a gene-regulatory pattern, and hence provides a means by
which to assess genomic regulatory interactions. Network models are a natural
way to represent and analyse groups of such interactions. The utility of
network models also increases as the quantity of data and number of variables
increase, making them increasingly relevant to large-scale genomic studies. We
propose methodology to infer prognostic genomic networks from a DNA
methylation-based measure of genomic interaction and association. We then show
how to identify prognostic biomarkers from such networks, which we term
`network community oncomarkers'. We illustrate the power of our proposed
methodology in the context of a large publicly available breast cancer dataset.
Top-down Neural Attention by Excitation Backprop
Authors: Jianming Zhang, Zhe Lin, Jonathan Brandt, Xiaohui Shen, Stan Sclaroff
Date: 2016-08-01 00:00:00

We aim to model the top-down attention of a Convolutional Neural Network
(CNN) classifier for generating task-specific attention maps. Inspired by a
top-down human visual attention model, we propose a new backpropagation scheme,
called Excitation Backprop, to pass along top-down signals downwards in the
network hierarchy via a probabilistic Winner-Take-All process. Furthermore, we
introduce the concept of contrastive attention to make the top-down attention
maps more discriminative. In experiments, we demonstrate the accuracy and
generalizability of our method in weakly supervised localization tasks on the
MS COCO, PASCAL VOC07 and ImageNet datasets. The usefulness of our method is
further validated in the text-to-region association task. On the Flickr30k
Entities dataset, we achieve promising performance in phrase localization by
leveraging the top-down attention of a CNN model that has been trained on
weakly labeled web images.
Supervised Classification of RADARSAT-2 Polarimetric Data for Different Land Features
Authors: Abhishek Maity
Date: 2016-08-01 00:00:00
Tasks: Classification, General Classification

The pixel percentage belonging to the user defined area that are assigned to
cluster in a confusion matrix for RADARSAT-2 over Vancouver area has been
analysed for classification. In this study, supervised Wishart and Support
Vector Machine (SVM) classifiers over RADARSAT-2 (RS2) fine quadpol mode Single
Look Complex (SLC) product data is computed and compared. In comparison with
conventional single channel or dual channel polarization, RADARSAT-2 is fully
polarimetric, making it to offer better land feature contrast for
classification operation.
Incremental Noising and its Fractal Behavior
Authors: Konstantinos A. Raftopoulos, Marin Ferecatu, Dionyssios D. Sourlas, Stefanos D. Kollias
Date: 2016-07-28 00:00:00

This manuscript is about further elucidating the concept of noising. The
concept of noising first appeared in \cite{CVPR14}, in the context of curvature
estimation and vertex localization on planar shapes. There are indications that
noising can play for global methods the role smoothing plays for local methods
in this task. This manuscript is about investigating this claim by introducing
incremental noising, in a recursive deterministic manner, analogous to how
smoothing is extended to progressive smoothing in similar tasks. As
investigating the properties and behavior of incremental noising is the purpose
of this manuscript, a surprising connection between incremental noising and
progressive smoothing is revealed by the experiments. To explain this
phenomenon, the fractal and the space filling properties of the two methods
respectively, are considered in a unifying context.
COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting
Authors: Nikolaus Hansen, Anne Auger, Raymond Ros, Olaf Mersmann, Tea Tušar, Dimo Brockhoff
Date: 2016-03-29 00:00:00
Tasks: Benchmarking, Multiobjective Optimization

We introduce COCO, an open source platform for Comparing Continuous Optimizers in a black-box setting. COCO aims at automatizing the tedious and repetitive task of benchmarking numerical optimization algorithms to the greatest possible extent. The platform and the underlying methodology allow to benchmark in the same framework deterministic and stochastic solvers for both single and multiobjective optimization. We present the rationales behind the (decade-long) development of the platform as a general proposition for guidelines towards better benchmarking. We detail underlying fundamental concepts of COCO such as the definition of a problem as a function instance, the underlying idea of instances, the use of target values, and runtime defined by the number of function calls as the central performance measure. Finally, we give a quick overview of the basic code structure and the currently available test suites.
Optimal Rates of Statistical Seriation
Authors: Nicolas Flammarion, Cheng Mao, Philippe Rigollet
Date: 2016-07-08 00:00:00
Tasks: Denoising

Given a matrix the seriation problem consists in permuting its rows in such
way that all its columns have the same shape, for example, they are monotone
increasing. We propose a statistical approach to this problem where the matrix
of interest is observed with noise and study the corresponding minimax rate of
estimation of the matrices. Specifically, when the columns are either unimodal
or monotone, we show that the least squares estimator is optimal up to
logarithmic factors and adapts to matrices with a certain natural structure.
Finally, we propose a computationally efficient estimator in the monotonic case
and study its performance both theoretically and experimentally. Our work is at
the intersection of shape constrained estimation and recent work that involves
permutation learning, such as graph denoising and ranking.
Kernel Risk-Sensitive Loss: Definition, Properties and Application to Robust Adaptive Filtering
Authors: Badong Chen, Lei Xing, Bin Xu, Haiquan Zhao, Nanning Zheng, Jose C. Principe
Date: 2016-08-01 00:00:00

Nonlinear similarity measures defined in kernel space, such as correntropy,
can extract higher-order statistics of data and offer potentially significant
performance improvement over their linear counterparts especially in
non-Gaussian signal processing and machine learning. In this work, we propose a
new similarity measure in kernel space, called the kernel risk-sensitive loss
(KRSL), and provide some important properties. We apply the KRSL to adaptive
filtering and investigate the robustness, and then develop the MKRSL algorithm
and analyze the mean square convergence performance. Compared with correntropy,
the KRSL can offer a more efficient performance surface, thereby enabling a
gradient based method to achieve faster convergence speed and higher accuracy
while still maintaining the robustness to outliers. Theoretical analysis
results and superior performance of the new algorithm are confirmed by
simulation.
Color Homography
Authors: Graham D. Finlayson, Han Gong, Robert B. Fisher
Date: 2016-05-13 00:00:00

We show the surprising result that colors across a change in viewing
condition (changing light color, shading and camera) are related by a
homography. Our homography color correction application delivers improved color
fidelity compared with the linear least-square.
Color Homography Color Correction
Authors: Graham D. Finlayson, Han Gong, Robert B. Fisher
Date: 2016-07-20 00:00:00
Tasks: Camera Calibration, Image Registration

Homographies -- a mathematical formalism for relating image points across
different camera viewpoints -- are at the foundations of geometric methods in
computer vision and are used in geometric camera calibration, image
registration, and stereo vision and other tasks. In this paper, we show the
surprising result that colors across a change in viewing condition (changing
light color, shading and camera) are also related by a homography. We propose a
new color correction method based on color homography. Experiments demonstrate
that solving the color homography problem leads to more accurate calibration.
High-Dimensional Metrics in R
Authors: Victor Chernozhukov, Chris Hansen, Martin Spindler
Date: 2016-03-05 00:00:00
Tasks: regression, valid, Vocal Bursts Intensity Prediction

The package High-dimensional Metrics (\Rpackage{hdm}) is an evolving
collection of statistical methods for estimation and quantification of
uncertainty in high-dimensional approximately sparse models. It focuses on
providing confidence intervals and significance testing for (possibly many)
low-dimensional subcomponents of the high-dimensional parameter vector.
Efficient estimators and uniformly valid confidence intervals for regression
coefficients on target variables (e.g., treatment or policy variable) in a
high-dimensional approximately sparse regression model, for average treatment
effect (ATE) and average treatment effect for the treated (ATET), as well for
extensions of these parameters to the endogenous setting are provided. Theory
grounded, data-driven methods for selecting the penalization parameter in Lasso
regressions under heteroscedastic and non-Gaussian errors are implemented.
Moreover, joint/ simultaneous confidence intervals for regression coefficients
of a high-dimensional sparse regression are implemented, including a joint
significance test for Lasso regression. Data sets which have been used in the
literature and might be useful for classroom demonstration and for testing new
estimators are included. \R and the package \Rpackage{hdm} are open-source
software projects and can be freely downloaded from CRAN:
\texttt{http://cran.r-project.org}.
Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation
Authors: Muhammad Ghifary, W. Bastiaan Kleijn, Mengjie Zhang, David Balduzzi, Wen Li
Date: 2016-07-12 00:00:00
Tasks: Classification, Domain Adaptation, General Classification, Object Recognition, Unsupervised Domain Adaptation

In this paper, we propose a novel unsupervised domain adaptation algorithm
based on deep learning for visual object recognition. Specifically, we design a
new model called Deep Reconstruction-Classification Network (DRCN), which
jointly learns a shared encoding representation for two tasks: i) supervised
classification of labeled source data, and ii) unsupervised reconstruction of
unlabeled target data.In this way, the learnt representation not only preserves
discriminability, but also encodes useful information from the target domain.
Our new DRCN model can be optimized by using backpropagation similarly as the
standard neural networks.
  We evaluate the performance of DRCN on a series of cross-domain object
recognition tasks, where DRCN provides a considerable improvement (up to ~8% in
accuracy) over the prior state-of-the-art algorithms. Interestingly, we also
observe that the reconstruction pipeline of DRCN transforms images from the
source domain into images whose appearance resembles the target dataset. This
suggests that DRCN's performance is due to constructing a single composite
representation that encodes information about both the structure of target
images and the classification of source images. Finally, we provide a formal
analysis to justify the algorithm's objective in domain adaptation context.
Accelerating the Super-Resolution Convolutional Neural Network
Authors: Chao Dong, Chen Change Loy, Xiaoou Tang
Date: 2016-08-01 00:00:00
Tasks: CPU, Image Super-Resolution, Super-Resolution

As a successful deep model applied in image super-resolution (SR), the
Super-Resolution Convolutional Neural Network (SRCNN) has demonstrated superior
performance to the previous hand-crafted models either in speed and restoration
quality. However, the high computational cost still hinders it from practical
usage that demands real-time performance (24 fps). In this paper, we aim at
accelerating the current SRCNN, and propose a compact hourglass-shape CNN
structure for faster and better SR. We re-design the SRCNN structure mainly in
three aspects. First, we introduce a deconvolution layer at the end of the
network, then the mapping is learned directly from the original low-resolution
image (without interpolation) to the high-resolution one. Second, we
reformulate the mapping layer by shrinking the input feature dimension before
mapping and expanding back afterwards. Third, we adopt smaller filter sizes but
more mapping layers. The proposed model achieves a speed up of more than 40
times with even superior restoration quality. Further, we present the parameter
settings that can achieve real-time performance on a generic CPU while still
maintaining good performance. A corresponding transfer strategy is also
proposed for fast training and testing across different upscaling factors.
Discovering Latent States for Model Learning: Applying Sensorimotor Contingencies Theory and Predictive Processing to Model Context
Authors: Nikolas J. Hemion
Date: 2016-08-01 00:00:00
Tasks: model, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Autonomous robots need to be able to adapt to unforeseen situations and to
acquire new skills through trial and error. Reinforcement learning in principle
offers a suitable methodological framework for this kind of autonomous
learning. However current computational reinforcement learning agents mostly
learn each individual skill entirely from scratch. How can we enable artificial
agents, such as robots, to acquire some form of generic knowledge, which they
could leverage for the learning of new skills? This paper argues that, like the
brain, the cognitive system of artificial agents has to develop a world model
to support adaptive behavior and learning. Inspiration is taken from two recent
developments in the cognitive science literature: predictive processing
theories of cognition, and the sensorimotor contingencies theory of perception.
Based on these, a hypothesis is formulated about what the content of
information might be that is encoded in an internal world model, and how an
agent could autonomously acquire it. A computational model is described to
formalize this hypothesis, and is evaluated in a series of simulation
experiments.
DAVE: A Unified Framework for Fast Vehicle Detection and Annotation
Authors: Yi Zhou, Li Liu, Ling Shao, Matt Mellor
Date: 2016-07-15 00:00:00
Tasks: Fast Vehicle Detection, vehicle detection

Vehicle detection and annotation for streaming video data with complex scenes
is an interesting but challenging task for urban traffic surveillance. In this
paper, we present a fast framework of Detection and Annotation for Vehicles
(DAVE), which effectively combines vehicle detection and attributes annotation.
DAVE consists of two convolutional neural networks (CNNs): a fast vehicle
proposal network (FVPN) for vehicle-like objects extraction and an attributes
learning network (ALN) aiming to verify each proposal and infer each vehicle's
pose, color and type simultaneously. These two nets are jointly optimized so
that abundant latent knowledge learned from the ALN can be exploited to guide
FVPN training. Once the system is trained, it can achieve efficient vehicle
detection and annotation for real-world traffic surveillance data. We evaluate
DAVE on a new self-collected UTS dataset and the public PASCAL VOC2007 car and
LISA 2010 datasets, with consistent improvements over existing algorithms.
hdm: High-Dimensional Metrics
Authors: Victor Chernozhukov, Chris Hansen, Martin Spindler
Date: 2016-08-01 00:00:00
Tasks: regression, valid, Vocal Bursts Intensity Prediction

In this article the package High-dimensional Metrics (\texttt{hdm}) is
introduced. It is a collection of statistical methods for estimation and
quantification of uncertainty in high-dimensional approximately sparse models.
It focuses on providing confidence intervals and significance testing for
(possibly many) low-dimensional subcomponents of the high-dimensional parameter
vector. Efficient estimators and uniformly valid confidence intervals for
regression coefficients on target variables (e.g., treatment or policy
variable) in a high-dimensional approximately sparse regression model, for
average treatment effect (ATE) and average treatment effect for the treated
(ATET), as well for extensions of these parameters to the endogenous setting
are provided. Theory grounded, data-driven methods for selecting the
penalization parameter in Lasso regressions under heteroscedastic and
non-Gaussian errors are implemented. Moreover, joint/ simultaneous confidence
intervals for regression coefficients of a high-dimensional sparse regression
are implemented. Data sets which have been used in the literature and might be
useful for classroom demonstration and for testing new estimators are included.
Crowd-sourcing NLG Data: Pictures Elicit Better Data
Authors: Jekaterina Novikova, Oliver Lemon, Verena Rieser
Date: 2016-08-01 00:00:00
Tasks: Text Generation

Recent advances in corpus-based Natural Language Generation (NLG) hold the
promise of being easily portable across domains, but require costly training
data, consisting of meaning representations (MRs) paired with Natural Language
(NL) utterances. In this work, we propose a novel framework for crowdsourcing
high quality NLG training data, using automatic quality control measures and
evaluating different MRs with which to elicit data. We show that pictorial MRs
result in better NL data being collected than logic-based MRs: utterances
elicited by pictorial MRs are judged as significantly more natural, more
informative, and better phrased, with a significant increase in average quality
ratings (around 0.5 points on a 6-point scale), compared to using the logical
MRs. As the MR becomes more complex, the benefits of pictorial stimuli
increase. The collected data will be released as part of this submission.