pwc-archive/papers-with-abstracts huggingface dataset:
Fast and Accurate Bilateral Filtering using Gauss-Polynomial Decomposition
Authors: Kunal. N. Chaudhury
Date: 2015-05-01 00:00:00

The bilateral filter is a versatile non-linear filter that has found diverse
applications in image processing, computer vision, computer graphics, and
computational photography. A widely-used form of the filter is the Gaussian
bilateral filter in which both the spatial and range kernels are Gaussian. A
direct implementation of this filter requires $O(\sigma^2)$ operations per
pixel, where $\sigma$ is the standard deviation of the spatial Gaussian. In
this paper, we propose an accurate approximation algorithm that can cut down
the computational complexity to $O(1)$ per pixel for any arbitrary $\sigma$
(constant-time implementation). This is based on the observation that the range
kernel operates via the translations of a fixed Gaussian over the range space,
and that these translated Gaussians can be accurately approximated using the
so-called Gauss-polynomials. The overall algorithm emerging from this
approximation involves a series of spatial Gaussian filtering, which can be
implemented in constant-time using separability and recursion. We present some
preliminary results to demonstrate that the proposed algorithm compares
favorably with some of the existing fast algorithms in terms of speed and
accuracy.
Image Denoising using Optimally Weighted Bilateral Filters: A Sure and Fast Approach
Authors: Kunal. N. Chaudhury, Kollipara Rithwik
Date: 2015-05-01 00:00:00
Tasks: Denoising, Image Denoising

The bilateral filter is known to be quite effective in denoising images
corrupted with small dosages of additive Gaussian noise. The denoising
performance of the filter, however, is known to degrade quickly with the
increase in noise level. Several adaptations of the filter have been proposed
in the literature to address this shortcoming, but often at a substantial
computational overhead. In this paper, we report a simple pre-processing step
that can substantially improve the denoising performance of the bilateral
filter, at almost no additional cost. The modified filter is designed to be
robust at large noise levels, and often tends to perform poorly below a certain
noise threshold. To get the best of the original and the modified filter, we
propose to combine them in a weighted fashion, where the weights are chosen to
minimize (a surrogate of) the oracle mean-squared-error (MSE). The
optimally-weighted filter is thus guaranteed to perform better than either of
the component filters in terms of the MSE, at all noise levels. We also provide
a fast algorithm for the weighted filtering. Visual and quantitative denoising
results on standard test images are reported which demonstrate that the
improvement over the original filter is significant both visually and in terms
of PSNR. Moreover, the denoising performance of the optimally-weighted
bilateral filter is competitive with the computation-intensive non-local means
filter.
A Simple Yet Effective Improvement to the Bilateral Filter for Image Denoising
Authors: Kollipara Rithwik, Kunal Narayan Chaudhury
Date: 2015-05-25 00:00:00
Tasks: Denoising, Image Denoising, SSIM

The bilateral filter has diverse applications in image processing, computer
vision, and computational photography. In particular, this non-linear filter is
quite effective in denoising images corrupted with additive Gaussian noise. The
filter, however, is known to perform poorly at large noise levels. Several
adaptations of the filter have been proposed in the literature to address this
shortcoming, but often at an added computational cost. In this paper, we report
a simple yet effective modification that improves the denoising performance of
the bilateral filter at almost no additional cost. We provide visual and
quantitative results on standard test images which show that this improvement
is significant both visually and in terms of PSNR and SSIM (often as large as 5
dB). We also demonstrate how the proposed filtering can be implemented at
reduced complexity by adapting a recent idea for fast bilateral filtering.
Clustering via Content-Augmented Stochastic Blockmodels
Authors: J. Massey Cashore, Xiaoting Zhao, Alexander A. Alemi, Yujia Liu, Peter I. Frazier
Date: 2015-05-25 00:00:00
Tasks: Articles, Clustering, Community Detection

Much of the data being created on the web contains interactions between users
and items. Stochastic blockmodels, and other methods for community detection
and clustering of bipartite graphs, can infer latent user communities and
latent item clusters from this interaction data. These methods, however,
typically ignore the items' contents and the information they provide about
item clusters, despite the tendency of items in the same latent cluster to
share commonalities in content. We introduce content-augmented stochastic
blockmodels (CASB), which use item content together with user-item interaction
data to enhance the user communities and item clusters learned. Comparisons to
several state-of-the-art benchmark methods, on datasets arising from scientists
interacting with scientific articles, show that content-augmented stochastic
blockmodels provide highly accurate clusters with respect to metrics
representative of the underlying community structure.
Affine and Regional Dynamic Time Warpng
Authors: Tsu-Wei Chen, Meena Abdelmaseeh, Daniel Stashuk
Date: 2015-05-25 00:00:00
Tasks: Dynamic Time Warping, Time Series, Time Series Alignment, Time Series Analysis

Pointwise matches between two time series are of great importance in time
series analysis, and dynamic time warping (DTW) is known to provide generally
reasonable matches. There are situations where time series alignment should be
invariant to scaling and offset in amplitude or where local regions of the
considered time series should be strongly reflected in pointwise matches. Two
different variants of DTW, affine DTW (ADTW) and regional DTW (RDTW), are
proposed to handle scaling and offset in amplitude and provide regional
emphasis respectively. Furthermore, ADTW and RDTW can be combined in two
different ways to generate alignments that incorporate advantages from both
methods, where the affine model can be applied either globally to the entire
time series or locally to each region. The proposed alignment methods
outperform DTW on specific simulated datasets, and one-nearest-neighbor
classifiers using their associated difference measures are competitive with the
difference measures associated with state-of-the-art alignment methods on real
datasets.
Structural Similarity Index SSIMplified: Is there really a simpler concept at the heart of image quality measurement?
Authors: Kieran Gerard Larkin
Date: 2015-01-29 00:00:00
Tasks: Image Quality Assessment, SSIM

The Structural Similarity Index (SSIM) is generally considered to be a
milestone in the recent history of Image Quality Assessment (IQA). Alas, SSIM's
accepted development from the product of three heuristic factors continues to
obscure it's real underlying simplicity. Starting instead from a
symmetric-antisymmetric reformulation we first show SSIM to be a contrast or
visibility function in the classic sense. Furthermore, the previously enigmatic
structural covariance is revealed to be the difference of variances. The second
step, eliminating the intrinsic quadratic nature of SSIM, allows a near linear
correlation with human observer scores, and without invoking the usual, but
arbitrary, sigmoid model fitting. We conclude that SSIM can be re-interpreted
in terms of perceptual masking: it is essentially equivalent to a normalised
error or noise visibility function (NVF), and, furthermore, the NVF alone
explains it success in modelling perceptual image quality. We use the term
Dissimilarity Quotient (DQ) for the specifically anti/symmetric SSIM derived
NVF. It seems that IQA researchers may now have two choices: 1) Continue to use
the complex SSIM formula, but noting that SSIM only works coincidentally since
the covariance term is actually the mean square error (MSE) in disguise. 2) Use
the simplest of all perceptually-masked image quality metrics, namely NVF or
DQ. On this choice Occam is clear: in the absence of differences in predictive
ability, the fewer assumptions that are made, the better.
Constrained 1-Spectral Clustering
Authors: Syama Sundar Rangapuram, Matthias Hein
Date: 2015-05-24 00:00:00
Tasks: All, Clustering, Form

An important form of prior information in clustering comes in form of
cannot-link and must-link constraints. We present a generalization of the
popular spectral clustering technique which integrates such constraints.
Motivated by the recently proposed $1$-spectral clustering for the
unconstrained problem, our method is based on a tight relaxation of the
constrained normalized cut into a continuous optimization problem. Opposite to
all other methods which have been suggested for constrained spectral
clustering, we can always guarantee to satisfy all constraints. Moreover, our
soft formulation allows to optimize a trade-off between normalized cut and the
number of violated constraints. An efficient implementation is provided which
scales to large datasets. We outperform consistently all other proposed methods
in the experiments.
Tight Continuous Relaxation of the Balanced $k$-Cut Problem
Authors: Syama Sundar Rangapuram, Pramod Kaushik Mudrakarta, Matthias Hein
Date: 2015-05-24 00:00:00
Tasks: Clustering

Spectral Clustering as a relaxation of the normalized/ratio cut has become
one of the standard graph-based clustering methods. Existing methods for the
computation of multiple clusters, corresponding to a balanced $k$-cut of the
graph, are either based on greedy techniques or heuristics which have weak
connection to the original motivation of minimizing the normalized cut. In this
paper we propose a new tight continuous relaxation for any balanced $k$-cut
problem and show that a related recently proposed relaxation is in most cases
loose leading to poor performance in practice. For the optimization of our
tight continuous relaxation we propose a new algorithm for the difficult
sum-of-ratios minimization problem which achieves monotonic descent. Extensive
comparisons show that our method outperforms all existing approaches for ratio
cut and other balanced $k$-cut criteria.
Deterministic Conditions for Subspace Identifiability from Incomplete Sampling
Authors: Daniel L. Pimentel-Alarcón, Robert D. Nowak, Nigel Boston
Date: 2014-10-02 00:00:00

Consider a generic $r$-dimensional subspace of $\mathbb{R}^d$, $r<d$, and
suppose that we are only given projections of this subspace onto small subsets
of the canonical coordinates. The paper establishes necessary and sufficient
deterministic conditions on the subsets for subspace identifiability.
Detecting bird sound in unknown acoustic background using crowdsourced training data
Authors: Timos Papadopoulos, Stephen Roberts, Kathy Willis
Date: 2015-05-24 00:00:00
Tasks: Novelty Detection

Biodiversity monitoring using audio recordings is achievable at a truly
global scale via large-scale deployment of inexpensive, unattended recording
stations or by large-scale crowdsourcing using recording and species
recognition on mobile devices. The ability, however, to reliably identify
vocalising animal species is limited by the fact that acoustic signatures of
interest in such recordings are typically embedded in a diverse and complex
acoustic background. To avoid the problems associated with modelling such
backgrounds, we build generative models of bird sounds and use the concept of
novelty detection to screen recordings to detect sections of data which are
likely bird vocalisations. We present detection results against various
acoustic environments and different signal-to-noise ratios. We discuss the
issues related to selecting the cost function and setting detection thresholds
in such algorithms. Our methods are designed to be scalable and automatically
applicable to arbitrary selections of species depending on the specific
geographic region and time period of deployment.
Classifying Relations by Ranking with Convolutional Neural Networks
Authors: Cicero Nogueira dos Santos, Bing Xiang, Bo-Wen Zhou
Date: 2015-04-24 00:00:00
Proceeding: IJCNLP 2015 7
Tasks: Classification, General Classification, Relation, Relation Classification, Relation Extraction, Sentence, Word Embeddings

Relation classification is an important semantic processing task for which
state-ofthe-art systems still rely on costly handcrafted features. In this work
we tackle the relation classification task using a convolutional neural network
that performs classification by ranking (CR-CNN). We propose a new pairwise
ranking loss function that makes it easy to reduce the impact of artificial
classes. We perform experiments using the the SemEval-2010 Task 8 dataset,
which is designed for the task of classifying the relationship between two
nominals marked in a sentence. Using CRCNN, we outperform the state-of-the-art
for this dataset and achieve a F1 of 84.1 without using any costly handcrafted
features. Additionally, our experimental results show that: (1) our approach is
more effective than CNN followed by a softmax classifier; (2) omitting the
representation of the artificial class Other improves both precision and
recall; and (3) using only word embeddings as input features is enough to
achieve state-of-the-art results if we consider only the text between the two
target nominals.
Deep Speaker Vectors for Semi Text-independent Speaker Verification
Authors: Lantian Li, Dong Wang, Zhiyong Zhang, Thomas Fang Zheng
Date: 2015-05-24 00:00:00
Tasks: Speaker Recognition, Speaker Verification, Text-Dependent Speaker Verification, Text-Independent Speaker Recognition, Text-Independent Speaker Verification

Recent research shows that deep neural networks (DNNs) can be used to extract
deep speaker vectors (d-vectors) that preserve speaker characteristics and can
be used in speaker verification. This new method has been tested on
text-dependent speaker verification tasks, and improvement was reported when
combined with the conventional i-vector method.
  This paper extends the d-vector approach to semi text-independent speaker
verification tasks, i.e., the text of the speech is in a limited set of short
phrases. We explore various settings of the DNN structure used for d-vector
extraction, and present a phone-dependent training which employs the posterior
features obtained from an ASR system. The experimental results show that it is
possible to apply d-vectors on semi text-independent speaker recognition, and
the phone-dependent training improves system performance.
Compute Less to Get More: Using ORC to Improve Sparse Filtering
Authors: Johannes Lederer, Sergio Guadarrama
Date: 2014-09-16 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Sparse Filtering is a popular feature learning algorithm for image
classification pipelines. In this paper, we connect the performance of Sparse
Filtering with spectral properties of the corresponding feature matrices. This
connection provides new insights into Sparse Filtering; in particular, it
suggests early stopping of Sparse Filtering. We therefore introduce the Optimal
Roundness Criterion (ORC), a novel stopping criterion for Sparse Filtering. We
show that this stopping criterion is related with pre-processing procedures
such as Statistical Whitening and demonstrate that it can make image
classification with Sparse Filtering considerably faster and more accurate.
Don't Fall for Tuning Parameters: Tuning-Free Variable Selection in High Dimensions With the TREX
Authors: Johannes Lederer, Christian Müller
Date: 2014-04-02 00:00:00
Tasks: Computational Efficiency, Variable Selection

Lasso is a seminal contribution to high-dimensional statistics, but it hinges
on a tuning parameter that is difficult to calibrate in practice. A partial
remedy for this problem is Square-Root Lasso, because it inherently calibrates
to the noise variance. However, Square-Root Lasso still requires the
calibration of a tuning parameter to all other aspects of the model. In this
study, we introduce TREX, an alternative to Lasso with an inherent calibration
to all aspects of the model. This adaptation to the entire model renders TREX
an estimator that does not require any calibration of tuning parameters. We
show that TREX can outperform cross-validated Lasso in terms of variable
selection and computational efficiency. We also introduce a bootstrapped
version of TREX that can further improve variable selection. We illustrate the
promising performance of TREX both on synthetic data and on a recent
high-dimensional biological data set that considers riboflavin production in B.
subtilis.
Domain Adaptation Extreme Learning Machines for Drift Compensation in E-nose Systems
Authors: Lei Zhang, David Zhang
Date: 2015-05-24 00:00:00
Tasks: Computational Efficiency, Domain Adaptation

This paper addresses an important issue, known as sensor drift that behaves a
nonlinear dynamic property in electronic nose (E-nose), from the viewpoint of
machine learning. Traditional methods for drift compensation are laborious and
costly due to the frequent acquisition and labeling process for gases samples
recalibration. Extreme learning machines (ELMs) have been confirmed to be
efficient and effective learning techniques for pattern recognition and
regression. However, ELMs primarily focus on the supervised, semi-supervised
and unsupervised learning problems in single domain (i.e. source domain). To
our best knowledge, ELM with cross-domain learning capability has never been
studied. This paper proposes a unified framework, referred to as Domain
Adaptation Extreme Learning Machine (DAELM), which learns a robust classifier
by leveraging a limited number of labeled data from target domain for drift
compensation as well as gases recognition in E-nose systems, without loss of
the computational efficiency and learning ability of traditional ELM. In the
unified framework, two algorithms called DAELM-S and DAELM-T are proposed for
the purpose of this paper, respectively. In order to percept the differences
among ELM, DAELM-S and DAELM-T, two remarks are provided. Experiments on the
popular sensor drift data with multiple batches collected by E-nose system
clearly demonstrate that the proposed DAELM significantly outperforms existing
drift compensation methods without cumbersome measures, and also bring new
perspectives for ELM.
The evolutionary origins of hierarchy
Authors: Henok Mengistu, Joost Huizinga, Jean-Baptiste Mouret, Jeff Clune
Date: 2015-05-23 00:00:00

Hierarchical organization -- the recursive composition of sub-modules -- is
ubiquitous in biological networks, including neural, metabolic, ecological, and
genetic regulatory networks, and in human-made systems, such as large
organizations and the Internet. To date, most research on hierarchy in networks
has been limited to quantifying this property. However, an open, important
question in evolutionary biology is why hierarchical organization evolves in
the first place. It has recently been shown that modularity evolves because of
the presence of a cost for network connections. Here we investigate whether
such connection costs also tend to cause a hierarchical organization of such
modules. In computational simulations, we find that networks without a
connection cost do not evolve to be hierarchical, even when the task has a
hierarchical structure. However, with a connection cost, networks evolve to be
both modular and hierarchical, and these networks exhibit higher overall
performance and evolvability (i.e. faster adaptation to new environments).
Additional analyses confirm that hierarchy independently improves adaptability
after controlling for modularity. Overall, our results suggest that the same
force--the cost of connections--promotes the evolution of both hierarchy and
modularity, and that these properties are important drivers of network
performance and adaptability. In addition to shedding light on the emergence of
hierarchy across the many domains in which it appears, these findings will also
accelerate future research into evolving more complex, intelligent
computational brains in the fields of artificial intelligence and robotics.
Image Data Compression for Covariance and Histogram Descriptors
Authors: Matt J. Kusner, Nicholas I. Kolkin, Stephen Tyree, Kilian Q. Weinberger
Date: 2014-12-04 00:00:00
Tasks: Data Compression, General Classification

Covariance and histogram image descriptors provide an effective way to
capture information about images. Both excel when used in combination with
special purpose distance metrics. For covariance descriptors these metrics
measure the distance along the non-Euclidean Riemannian manifold of symmetric
positive definite matrices. For histogram descriptors the Earth Mover's
distance measures the optimal transport between two histograms. Although more
precise, these distance metrics are very expensive to compute, making them
impractical in many applications, even for data sets of only a few thousand
examples. In this paper we present two methods to compress the size of
covariance and histogram datasets with only marginal increases in test error
for k-nearest neighbor classification. Specifically, we show that we can reduce
data sets to 16% and in some cases as little as 2% of their original size,
while approximately matching the test error of kNN classification on the full
training set. In fact, because the compressed set is learned in a supervised
fashion, it sometimes even outperforms the full data set, while requiring only
a fraction of the space and drastically reducing test-time computation.
The Minimum Spanning Tree of Maximum Entropy
Authors: Samuel de Sousa, Walter G. Kropatsch
Date: 2015-05-23 00:00:00
Tasks: graph construction, Graph Matching

In computer vision, we have the problem of creating graphs out of
unstructured point-sets, i.e. the data graph. A common approach for this
problem consists of building a triangulation which might not always lead to the
best solution. Small changes in the location of the points might generate
graphs with unstable configurations and the topology of the graph could change
significantly. After building the data-graph, one could apply Graph Matching
techniques to register the original point-sets. In this paper, we propose a
data graph technique based on the Minimum Spanning Tree of Maximum Entropty
(MSTME). We aim at a data graph construction which could be more stable than
the Delaunay triangulation with respect to small variations in the neighborhood
of points. Our technique aims at creating data graphs which could help the
point-set registration process. We propose an algorithm with a single free
parameter that weighs the importance between the total weight cost and the
entropy of the current spanning tree. We compare our algorithm on a number of
different databases with the Delaunay triangulation.
A Frobenius Model of Information Structure in Categorical Compositional Distributional Semantics
Authors: Dimitri Kartsaklis, Mehrnoosh Sadrzadeh
Date: 2015-05-23 00:00:00
Proceeding: WS 2015 7
Tasks: Sentence

The categorical compositional distributional model of Coecke, Sadrzadeh and
Clark provides a linguistically motivated procedure for computing the meaning
of a sentence as a function of the distributional meaning of the words therein.
The theoretical framework allows for reasoning about compositional aspects of
language and offers structural ways of studying the underlying relationships.
While the model so far has been applied on the level of syntactic structures, a
sentence can bring extra information conveyed in utterances via intonational
means. In the current paper we extend the framework in order to accommodate
this additional information, using Frobenius algebraic structures canonically
induced over the basis of finite-dimensional vector spaces. We detail the
theory, provide truth-theoretic and distributional semantics for meanings of
intonationally-marked utterances, and present justifications and extensive
examples.