pwc-archive/papers-with-abstracts huggingface dataset:
$\mathbf{D^3}$: Deep Dual-Domain Based Fast Restoration of JPEG-Compressed Images
Authors: Zhangyang Wang, Ding Liu, Shiyu Chang, Qing Ling, Yingzhen Yang, Thomas S. Huang
Date: 2016-01-16 00:00:00

In this paper, we design a Deep Dual-Domain ($\mathbf{D^3}$) based fast
restoration model to remove artifacts of JPEG compressed images. It leverages
the large learning capacity of deep networks, as well as the problem-specific
expertise that was hardly incorporated in the past design of deep
architectures. For the latter, we take into consideration both the prior
knowledge of the JPEG compression scheme, and the successful practice of the
sparsity-based dual-domain approach. We further design the One-Step Sparse
Inference (1-SI) module, as an efficient and light-weighted feed-forward
approximation of sparse coding. Extensive experiments verify the superiority of
the proposed $D^3$ model over several state-of-the-art methods. Specifically,
our best model is capable of outperforming the latest deep model for around 1
dB in PSNR, and is 30 times faster.
Learning Compact Recurrent Neural Networks
Authors: Zhiyun Lu, Vikas Sindhwani, Tara N. Sainath
Date: 2016-04-09 00:00:00
Tasks: speech-recognition, Speech Recognition

Recurrent neural networks (RNNs), including long short-term memory (LSTM)
RNNs, have produced state-of-the-art results on a variety of speech recognition
tasks. However, these models are often too large in size for deployment on
mobile devices with memory and latency constraints. In this work, we study
mechanisms for learning compact RNNs and LSTMs via low-rank factorizations and
parameter sharing schemes. Our goal is to investigate redundancies in recurrent
architectures where compression can be admitted without losing performance. A
hybrid strategy of using structured matrices in the bottom layers and shared
low-rank factors on the top layers is found to be particularly effective,
reducing the parameters of a standard LSTM by 75%, at a small cost of 0.3%
increase in WER, on a 2,000-hr English Voice Search task.
ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs
Authors: Wenpeng Yin, Hinrich Schütze, Bing Xiang, Bo-Wen Zhou
Date: 2015-12-16 00:00:00
Proceeding: TACL 2016 1
Tasks: Answer Selection, Natural Language Inference, Paraphrase Identification, Sentence

How to model a pair of sentences is a critical issue in many NLP tasks such
as answer selection (AS), paraphrase identification (PI) and textual entailment
(TE). Most prior work (i) deals with one individual task by fine-tuning a
specific system; (ii) models each sentence's representation separately, rarely
considering the impact of the other sentence; or (iii) relies fully on manually
designed, task-specific linguistic features. This work presents a general
Attention Based Convolutional Neural Network (ABCNN) for modeling a pair of
sentences. We make three contributions. (i) ABCNN can be applied to a wide
variety of tasks that require modeling of sentence pairs. (ii) We propose three
attention schemes that integrate mutual influence between sentences into CNN;
thus, the representation of each sentence takes into consideration its
counterpart. These interdependent sentence pair representations are more
powerful than isolated sentence representations. (iii) ABCNN achieves
state-of-the-art performance on AS, PI and TE tasks.
Scene-driven Retrieval in Edited Videos using Aesthetic and Semantic Deep Features
Authors: Lorenzo Baraldi, Costantino Grana, Rita Cucchiara
Date: 2016-04-09 00:00:00
Tasks: Retrieval

This paper presents a novel retrieval pipeline for video collections, which
aims to retrieve the most significant parts of an edited video for a given
query, and represent them with thumbnails which are at the same time
semantically meaningful and aesthetically remarkable. Videos are first
segmented into coherent and story-telling scenes, then a retrieval algorithm
based on deep learning is proposed to retrieve the most significant scenes for
a textual query. A ranking strategy based on deep features is finally used to
tackle the problem of visualizing the best thumbnail. Qualitative and
quantitative experiments are conducted on a collection of edited videos to
demonstrate the effectiveness of our approach.
Visual7W: Grounded Question Answering in Images
Authors: Yuke Zhu, Oliver Groth, Michael Bernstein, Li Fei-Fei
Date: 2015-11-11 00:00:00
Proceeding: CVPR 2016 6
Tasks: Multiple-choice, Multiple Choice Question Answering (MCQA), Object Recognition, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

We have seen great progress in basic perceptual tasks such as object
recognition and detection. However, AI models still fail to match humans in
high-level vision tasks due to the lack of capacities for deeper reasoning.
Recently the new task of visual question answering (QA) has been proposed to
evaluate a model's capacity for deep image understanding. Previous works have
established a loose, global association between QA sentences and images.
However, many questions and answers, in practice, relate to local regions in
the images. We establish a semantic link between textual descriptions and image
regions by object-level grounding. It enables a new type of QA with visual
answers, in addition to textual answers used in previous work. We study the
visual QA tasks in a grounded setting with a large collection of 7W
multiple-choice QA pairs. Furthermore, we evaluate human performance and
several baseline models on the QA tasks. Finally, we propose a novel LSTM model
with spatial attention to tackle the 7W QA tasks.
Dissimilarity-based Sparse Subset Selection
Authors: Ehsan Elhamifar, Guillermo Sapiro, S. Shankar Sastry
Date: 2014-07-25 00:00:00
Tasks: Clustering, Recommendation Systems, Time Series Analysis

Finding an informative subset of a large collection of data points or models
is at the center of many problems in computer vision, recommender systems,
bio/health informatics as well as image and natural language processing. Given
pairwise dissimilarities between the elements of a `source set' and a `target
set,' we consider the problem of finding a subset of the source set, called
representatives or exemplars, that can efficiently describe the target set. We
formulate the problem as a row-sparsity regularized trace minimization problem.
Since the proposed formulation is, in general, NP-hard, we consider a convex
relaxation. The solution of our optimization finds representatives and the
assignment of each element of the target set to each representative, hence,
obtaining a clustering. We analyze the solution of our proposed optimization as
a function of the regularization parameter. We show that when the two sets
jointly partition into multiple groups, our algorithm finds representatives
from all groups and reveals clustering of the sets. In addition, we show that
the proposed framework can effectively deal with outliers. Our algorithm works
with arbitrary dissimilarities, which can be asymmetric or violate the triangle
inequality. To efficiently implement our algorithm, we consider an Alternating
Direction Method of Multipliers (ADMM) framework, which results in quadratic
complexity in the problem size. We show that the ADMM implementation allows to
parallelize the algorithm, hence further reducing the computational time.
Finally, by experiments on real-world datasets, we show that our proposed
algorithm improves the state of the art on the two problems of scene
categorization using representative images and time-series modeling and
segmentation using representative~models.
Towards an Indexical Model of Situated Language Comprehension for Cognitive Agents in Physical Worlds
Authors: Shiwali Mohan, Aaron Mininger, John Laird
Date: 2016-04-09 00:00:00

We propose a computational model of situated language comprehension based on the Indexical Hypothesis that generates meaning representations by translating amodal linguistic symbols to modal representations of beliefs, knowledge, and experience external to the linguistic system. This Indexical Model incorporates multiple information sources, including perceptions, domain knowledge, and short-term and long-term experiences during comprehension. We show that exploiting diverse information sources can alleviate ambiguities that arise from contextual use of underspecific referring expressions and unexpressed argument alternations of verbs. The model is being used to support linguistic interactions in Rosie, an agent implemented in Soar that learns from instruction.
Randomized Robust Subspace Recovery for High Dimensional Data Matrices
Authors: Mostafa Rahmani, George Atia
Date: 2015-05-21 00:00:00
Tasks: Vocal Bursts Intensity Prediction

This paper explores and analyzes two randomized designs for robust Principal
Component Analysis (PCA) employing low-dimensional data sketching. In one
design, a data sketch is constructed using random column sampling followed by
low dimensional embedding, while in the other, sketching is based on random
column and row sampling. Both designs are shown to bring about substantial
savings in complexity and memory requirements for robust subspace learning over
conventional approaches that use the full scale data. A characterization of the
sample and computational complexity of both designs is derived in the context
of two distinct outlier models, namely, sparse and independent outlier models.
The proposed randomized approach can provably recover the correct subspace with
computational and sample complexity that are almost independent of the size of
the data. The results of the mathematical analysis are confirmed through
numerical simulations using both synthetic and real data.
Symmetries and control in generative neural nets
Authors: Galin Georgiev
Date: 2015-11-09 00:00:00

We study generative nets which can control and modify observations, after
being trained on real-life datasets. In order to zoom-in on an object, some
spatial, color and other attributes are learned by classifiers in specialized
attention nets. In field-theoretical terms, these learned symmetry statistics
form the gauge group of the data set. Plugging them in the generative layers of
auto-classifiers-encoders (ACE) appears to be the most direct way to
simultaneously: i) generate new observations with arbitrary attributes, from a
given class, ii) describe the low-dimensional manifold encoding the "essence"
of the data, after superfluous attributes are factored out, and iii)
organically control, i.e., move or modify objects within given observations. We
demonstrate the sharp improvement of the generative qualities of shallow ACE,
with added spatial and color symmetry statistics, on the distorted MNIST and
CIFAR10 datasets.
Application of Multifractal Analysis to Segmentation of Water Bodies in Optical and Synthetic Aperture Radar Satellite Images
Authors: Victor Manuel San Martin, Alejandra Figliola
Date: 2016-04-08 00:00:00

A method for segmenting water bodies in optical and synthetic aperture radar
(SAR) satellite images is proposed. It makes use of the textural features of
the different regions in the image for segmentation. The method consists in a
multiscale analysis of the images, which allows us to study the images
regularity both, locally and globally. As results of the analysis, coarse
multifractal spectra of studied images and a group of images that associates
each position (pixel) with its corresponding value of local regularity (or
singularity) spectrum are obtained. Thresholds are then applied to the
multifractal spectra of the images for the classification. These thresholds are
selected after studying the characteristics of the spectra under the assumption
that water bodies have larger local regularity than other soil types.
Classifications obtained by the multifractal method are compared quantitatively
with those obtained by neural networks trained to classify the pixels of the
images in covered against uncovered by water. In optical images, the
classifications are also compared with those derived using the so-called
Normalized Differential Water Index (NDWI).
Machine Learning for Visual Navigation of Unmanned Ground Vehicles
Authors: Artem A. Lenskiy, Jong-Soo Lee
Date: 2016-04-08 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Scene Segmentation, Visual Navigation

The use of visual information for the navigation of unmanned ground vehicles
in a cross-country environment recently received great attention. However,
until now, the use of textural information has been somewhat less effective
than color or laser range information. This manuscript reviews the recent
achievements in cross-country scene segmentation and addresses their
shortcomings. It then describes a problem related to classification of high
dimensional texture features. Finally, it compares three machine learning
algorithms aimed at resolving this problem. The experimental results for each
machine learning algorithm with the discussion of comparisons are given at the
end of the manuscript.
Leveraging Network Dynamics for Improved Link Prediction
Authors: Alireza Hajibagheri, Gita Sukthankar, Kiran Lakkaraju
Date: 2016-04-08 00:00:00
Tasks: Link Prediction, Prediction, Time Series, Time Series Analysis

The aim of link prediction is to forecast connections that are most likely to
occur in the future, based on examples of previously observed links. A key
insight is that it is useful to explicitly model network dynamics, how
frequently links are created or destroyed when doing link prediction. In this
paper, we introduce a new supervised link prediction framework, RPM (Rate
Prediction Model). In addition to network similarity measures, RPM uses the
predicted rate of link modifications, modeled using time series data; it is
implemented in Spark-ML and trained with the original link distribution, rather
than a small balanced subset. We compare the use of this network dynamics model
to directly creating time series of network similarity measures. Our
experiments show that RPM, which leverages predicted rates, outperforms the use
of network similarity measures, either individually or within a time series.
Image segmentation of cross-country scenes captured in IR spectrum
Authors: Artem Lenskiy
Date: 2016-04-08 00:00:00
Tasks: Autonomous Navigation, Image Segmentation, Segmentation, Self-Driving Cars, Semantic Segmentation

Computer vision has become a major source of information for autonomous
navigation of robots of various types, self-driving cars, military robots and
mars/lunar rovers are some examples. Nevertheless, the majority of methods
focus on analysing images captured in visible spectrum. In this manuscript we
elaborate on the problem of segmenting cross-country scenes captured in IR
spectrum. For this purpose we proposed employing salient features. Salient
features are robust to variations in scale, brightness and view angle. We
suggest the Speeded-Up Robust Features as a basis for our salient features for
a number of reasons discussed in the paper. We also provide a comparison of two
SURF implementations. The SURF features are extracted from images of different
terrain types. For every feature we estimate a terrain class membership
function. The membership values are obtained by means of either the multi-layer
perceptron or nearest neighbours. The features' class membership values and
their spatial positions are then applied to estimate class membership values
for all pixels in the image. To decrease the effect of segmentation blinking
that is caused by rapid switching between different terrain types and to speed
up segmentation, we are tracking camera position and predict features'
positions. The comparison of the multi-layer perception and the nearest
neighbour classifiers is presented in the paper. The error rate of the terrain
segmentation using the nearest neighbours obtained on the testing set is
16.6+-9.17%.
Wavelet-Based Semantic Features for Hyperspectral Signature Discrimination
Authors: Siwei Feng, Yuki Itoh, Mario Parente, Marco F. Duarte
Date: 2016-02-11 00:00:00
Tasks: Classification, General Classification

Hyperspectral signature classification is a quantitative analysis approach
for hyperspectral imagery which performs detection and classification of the
constituent materials at the pixel level in the scene. The classification
procedure can be operated directly on hyperspectral data or performed by using
some features extracted from the corresponding hyperspectral signatures
containing information like the signature's energy or shape. In this paper, we
describe a technique that applies non-homogeneous hidden Markov chain (NHMC)
models to hyperspectral signature classification. The basic idea is to use
statistical models (such as NHMC) to characterize wavelet coefficients which
capture the spectrum semantics (i.e., structural information) at multiple
levels. Experimental results show that the approach based on NHMC models can
outperform existing approaches relevant in classification tasks.
The (1+1) Elitist Black-Box Complexity of LeadingOnes
Authors: Carola Doerr, Johannes Lengler
Date: 2016-04-08 00:00:00
Tasks: Evolutionary Algorithms

One important goal of black-box complexity theory is the development of
complexity models allowing to derive meaningful lower bounds for whole classes
of randomized search heuristics. Complementing classical runtime analysis,
black-box models help us understand how algorithmic choices such as the
population size, the variation operators, or the selection rules influence the
optimization time. One example for such a result is the $\Omega(n \log n)$
lower bound for unary unbiased algorithms on functions with a unique global
optimum [Lehre/Witt, GECCO 2010], which tells us that higher arity operators or
biased sampling strategies are needed when trying to beat this bound. In lack
of analyzing techniques, almost no non-trivial bounds are known for other
restricted models. Proving such bounds therefore remains to be one of the main
challenges in black-box complexity theory.
  With this paper we contribute to our technical toolbox for lower bound
computations by proposing a new type of information-theoretic argument. We
regard the permutation- and bit-invariant version of \textsc{LeadingOnes} and
prove that its (1+1) elitist black-box complexity is $\Omega(n^2)$, a bound
that is matched by (1+1)-type evolutionary algorithms. The (1+1) elitist
complexity of \textsc{LeadingOnes} is thus considerably larger than its
unrestricted one, which is known to be of order $n\log\log n$ [Afshani et al.,
2013].
Bayesian Neighbourhood Component Analysis
Authors: Dong Wang, Xiaoyang Tan
Date: 2016-04-08 00:00:00
Tasks: Bayesian Optimization, Metric Learning

Learning a good distance metric in feature space potentially improves the
performance of the KNN classifier and is useful in many real-world
applications. Many metric learning algorithms are however based on the point
estimation of a quadratic optimization problem, which is time-consuming,
susceptible to overfitting, and lack a natural mechanism to reason with
parameter uncertainty, an important property useful especially when the
training set is small and/or noisy. To deal with these issues, we present a
novel Bayesian metric learning method, called Bayesian NCA, based on the
well-known Neighbourhood Component Analysis method, in which the metric
posterior is characterized by the local label consistency constraints of
observations, encoded with a similarity graph instead of independent pairwise
constraints. For efficient Bayesian optimization, we explore the variational
lower bound over the log-likelihood of the original NCA objective. Experiments
on several publicly available datasets demonstrate that the proposed method is
able to learn robust metric measures from small size dataset and/or from
challenging training set with labels contaminated by errors. The proposed
method is also shown to outperform a previous pairwise constrained Bayesian
metric learning method.
The "Sprekend Nederland" project and its application to accent location
Authors: David A. van Leeuwen, Rosemary Orr
Date: 2016-02-08 00:00:00

This paper describes the data collection effort that is part of the project
Sprekend Nederland (The Netherlands Talking), and discusses its potential use
in Automatic Accent Location. We define Automatic Accent Location as the task
to describe the accent of a speaker in terms of the location of the speaker and
its history. We discuss possible ways of describing accent location, the
consequence these have for the task of automatic accent location, and potential
evaluation metrics.
A system of serial computation for classified rules prediction in non-regular ontology trees
Authors: Kennedy E. Ehimwenma, Paul Crowther, Martin Beer
Date: 2016-04-08 00:00:00
Tasks: Decision Making, Inductive Learning

Objects or structures that are regular take uniform dimensions. Based on the
concepts of regular models, our previous research work has developed a system
of a regular ontology that models learning structures in a multiagent system
for uniform pre-assessments in a learning environment. This regular ontology
has led to the modelling of a classified rules learning algorithm that predicts
the actual number of rules needed for inductive learning processes and decision
making in a multiagent system. But not all processes or models are regular.
Thus this paper presents a system of polynomial equation that can estimate and
predict the required number of rules of a non-regular ontology model given some
defined parameters.
A method for locally approximating regularized iterative tomographic reconstruction methods
Authors: D. M. Pelt, K. J. Batenburg
Date: 2016-04-08 00:00:00
Tasks: Object

In many applications of tomography, the acquired projections are either
limited in number or contain a significant amount of noise. In these cases,
standard reconstruction methods tend to produce artifacts that can make further
analysis difficult. Advanced regularized iterative methods, such as total
variation minimization, are often able to achieve a higher reconstruction
quality by exploiting prior knowledge about the scanned object. In practice,
however, these methods often have prohibitively long computation times or large
memory requirements. Furthermore, since they are based on minimizing a global
objective function, regularized iterative methods need to reconstruct the
entire scanned object, even when one is only interested in a (small) region of
the reconstructed image.
  In this paper, we present a method to approximate regularized iterative
reconstruction methods inside a (small) region of the scanned object. The
method only performs computations inside the region of interest, ensuring low
computational requirements. Reconstruction results for different phantom images
and types of regularization are given, showing that reconstructions of the
proposed local method are almost identical to those of the global regularized
iterative methods that are approximated, even for relatively small regions of
interest. Furthermore, we show that larger regions can be reconstructed
efficiently by reconstructing several small regions in parallel and combining
them into a single reconstruction afterwards.
Well-posedness of a nonlinear integro-differential problem and its rearranged formulation
Authors: Gonzalo Galiano, Emanuele Schiavi, Julián Velasco
Date: 2015-06-07 00:00:00

We study the existence and uniqueness of solutions of a nonlinear
integro-differential problem which we reformulate introducing the notion of the
decreasing rearrangement of the solution. A dimensional reduction of the
problem is obtained and a detailed analysis of the properties of the solutions
of the model is provided. Finally, a fast numerical method is devised and
implemented to show the performance of the model when typical image processing
tasks such as filtering and segmentation are performed.