pwc-archive/papers-with-abstracts huggingface dataset:
Adaptive Combination of l0 LMS Adaptive Filters for Sparse System Identification in Fluctuating Noise Power
Authors: Bijit Kumar Das, Mrityunjoy Chakraborty
Date: 2016-05-10 00:00:00

Recently, the l0-least mean square (l0-LMS) algorithm has been proposed to
identify sparse linear systems by employing a sparsity-promoting continuous
function as an approximation of l0 pseudonorm penalty. However, the performance
of this algorithm is sensitive to the appropriate choice of the some parameter
responsible for the zero-attracting intensity. The optimum choice for this
parameter depends on the signal-to-noise ratio (SNR) prevailing in the system.
Thus, it becomes difficult to fix a suitable value for this parameter,
particularly in a situation where SNR fluctuates over time. In this work, we
propose several adaptive combinations of differently parameterized l0-LMS to
get an overall satisfactory performance independent of the SNR, and discuss
some issues relevant to these combination structures. We also demonstrate an
efficient partial update scheme which not only reduces the number of
computations per iteration, but also achieves some interesting performance gain
compared with the full update case. Then, we propose a new recursive least
squares (RLS)-type rule to update the combining parameter more efficiently.
Finally, we extend the combination of two filters to a combination of M number
adaptive filters, which manifests further improvement for M > 2.
Performance Analysis of the Gradient Comparator LMS Algorithm
Authors: Bijit Kumar Das, Mrityunjoy Chakraborty
Date: 2016-05-10 00:00:00

The sparsity-aware zero attractor least mean square (ZA-LMS) algorithm
manifests much lower misadjustment in strongly sparse environment than its
sparsity-agnostic counterpart, the least mean square (LMS), but is shown to
perform worse than the LMS when sparsity of the impulse response decreases. The
reweighted variant of the ZA-LMS, namely RZA-LMS shows robustness against this
variation in sparsity, but at the price of increased computational complexity.
The other variants such as the l 0 -LMS and the improved proportionate
normalized LMS (IPNLMS), though perform satisfactorily, are also
computationally intensive. The gradient comparator LMS (GC-LMS) is a practical
solution of this trade-off when hardware constraint is to be considered. In
this paper, we analyse the mean and the mean square convergence performance of
the GC-LMS algorithm in detail. The analyses satisfactorily match with the
simulation results.
Maximal Sparsity with Deep Networks?
Authors: Bo Xin, Yizhou Wang, Wen Gao, David Wipf
Date: 2016-05-05 00:00:00
Proceeding: NeurIPS 2016 12

The iterations of many sparse estimation algorithms are comprised of a fixed
linear filter cascaded with a thresholding nonlinearity, which collectively
resemble a typical neural network layer. Consequently, a lengthy sequence of
algorithm iterations can be viewed as a deep network with shared, hand-crafted
layer weights. It is therefore quite natural to examine the degree to which a
learned network model might act as a viable surrogate for traditional sparse
estimation in domains where ample training data is available. While the
possibility of a reduced computational budget is readily apparent when a
ceiling is imposed on the number of layers, our work primarily focuses on
estimation accuracy. In particular, it is well-known that when a signal
dictionary has coherent columns, as quantified by a large RIP constant, then
most tractable iterative algorithms are unable to find maximally sparse
representations. In contrast, we demonstrate both theoretically and empirically
the potential for a trained deep network to recover minimal $\ell_0$-norm
representations in regimes where existing methods fail. The resulting system is
deployed on a practical photometric stereo estimation problem, where the goal
is to remove sparse outliers that can disrupt the estimation of surface normals
from a 3D scene.
Fast and High-Quality Bilateral Filtering Using Gauss-Chebyshev Approximation
Authors: Sanjay Ghosh, Kunal. N. Chaudhury
Date: 2016-05-07 00:00:00
Tasks: Vocal Bursts Intensity Prediction

The bilateral filter is an edge-preserving smoother that has diverse
applications in image processing, computer vision, computer graphics, and
computational photography. The filter uses a spatial kernel along with a range
kernel to perform edge-preserving smoothing. In this paper, we consider the
Gaussian bilateral filter where both the kernels are Gaussian. A direct
implementation of the Gaussian bilateral filter requires $O(\sigma_s^2)$
operations per pixel, where $\sigma_s$ is the standard deviation of the spatial
Gaussian. In fact, it is well-known that the direct implementation is slow in
practice. We present an approximation of the Gaussian bilateral filter, whereby
we can cut down the number of operations to $O(1)$ per pixel for any arbitrary
$\sigma_s$, and yet achieve very high-quality filtering that is almost
indistinguishable from the output of the original filter. We demonstrate that
the proposed approximation is few orders faster in practice compared to the
direct implementation. We also demonstrate that the approximation is
competitive with existing fast algorithms in terms of speed and accuracy.
Transport Analysis of Infinitely Deep Neural Network
Authors: Sho Sonoda, Noboru Murata
Date: 2016-05-10 00:00:00
Tasks: Denoising

We investigated the feature map inside deep neural networks (DNNs) by
tracking the transport map. We are interested in the role of depth (why do DNNs
perform better than shallow models?) and the interpretation of DNNs (what do
intermediate layers do?) Despite the rapid development in their application,
DNNs remain analytically unexplained because the hidden layers are nested and
the parameters are not faithful. Inspired by the integral representation of
shallow NNs, which is the continuum limit of the width, or the hidden unit
number, we developed the flow representation and transport analysis of DNNs.
The flow representation is the continuum limit of the depth or the hidden layer
number, and it is specified by an ordinary differential equation with a vector
field. We interpret an ordinary DNN as a transport map or a Euler broken line
approximation of the flow. Technically speaking, a dynamical system is a
natural model for the nested feature maps. In addition, it opens a new way to
the coordinate-free treatment of DNNs by avoiding the redundant parametrization
of DNNs. Following Wasserstein geometry, we analyze a flow in three aspects:
dynamical system, continuity equation, and Wasserstein gradient flow. A key
finding is that we specified a series of transport maps of the denoising
autoencoder (DAE). Starting from the shallow DAE, this paper develops three
topics: the transport map of the deep DAE, the equivalence between the stacked
DAE and the composition of DAEs, and the development of the double continuum
limit or the integral representation of the flow representation. As partial
answers to the research questions, we found that deeper DAEs converge faster
and the extracted features are better; in addition, a deep Gaussian DAE
transports mass to decrease the Shannon entropy of the data distribution.
A Selection of Giant Radio Sources from NVSS
Authors: D. D. Proctor
Date: 2016-03-22 00:00:00
Tasks: ARC, Maximum Separation

Results of the application of pattern recognition techniques to the problem
of identifying Giant Radio Sources (GRS) from the data in the NVSS catalog are
presented and issues affecting the process are explored. Decision-tree pattern
recognition software was applied to training set source pairs developed from
known NVSS large angular size radio galaxies. The full training set consisted
of 51,195 source pairs, 48 of which were known GRS for which each lobe was
primarily represented by a single catalog component. The source pairs had a
maximum separation of 20 arc minutes and a minimum component area of 1.87
square arc minutes at the 1.4 mJy level. The importance of comparing resulting
probability distributions of the training and application sets for cases of
unknown class ratio is demonstrated. The probability of correctly ranking a
randomly selected (GRS, non-GRS) pair from the best of the tested classifiers
was determined to be 97.8 +/- 1.5%. The best classifiers were applied to the
over 870,000 candidate pairs from the entire catalog. Images of higher ranked
sources were visually screened and a table of over sixteen hundred candidates,
including morphological annotation, is presented. These systems include doubles
and triples, Wide-Angle Tail (WAT) and Narrow-Angle Tail (NAT), S- or Z-shaped
systems, and core-jets and resolved cores. While some resolved lobe systems are
recovered with this technique, generally it is expected that such systems would
require a different approach.
Communication Lower Bounds for Statistical Estimation Problems via a Distributed Data Processing Inequality
Authors: Mark Braverman, Ankit Garg, Tengyu Ma, Huy L. Nguyen, David P. Woodruff
Date: 2015-06-24 00:00:00

We study the tradeoff between the statistical error and communication cost of
distributed statistical estimation problems in high dimensions. In the
distributed sparse Gaussian mean estimation problem, each of the $m$ machines
receives $n$ data points from a $d$-dimensional Gaussian distribution with
unknown mean $\theta$ which is promised to be $k$-sparse. The machines
communicate by message passing and aim to estimate the mean $\theta$. We
provide a tight (up to logarithmic factors) tradeoff between the estimation
error and the number of bits communicated between the machines. This directly
leads to a lower bound for the distributed \textit{sparse linear regression}
problem: to achieve the statistical minimax error, the total communication is
at least $\Omega(\min\{n,d\}m)$, where $n$ is the number of observations that
each machine receives and $d$ is the ambient dimension. These lower results
improve upon [Sha14,SD'14] by allowing multi-round iterative communication
model. We also give the first optimal simultaneous protocol in the dense case
for mean estimation.
  As our main technique, we prove a \textit{distributed data processing
inequality}, as a generalization of usual data processing inequalities, which
might be of independent interest and useful for other problems.
You Only Look Once: Unified, Real-Time Object Detection
Authors: Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi
Date: 2015-06-08 00:00:00
Proceeding: CVPR 2016 6
Tasks: Object, Object Counting, Object Detection, Real-Time Object Detection

We present YOLO, a new approach to object detection. Prior work on object
detection repurposes classifiers to perform detection. Instead, we frame object
detection as a regression problem to spatially separated bounding boxes and
associated class probabilities. A single neural network predicts bounding boxes
and class probabilities directly from full images in one evaluation. Since the
whole detection pipeline is a single network, it can be optimized end-to-end
directly on detection performance.
  Our unified architecture is extremely fast. Our base YOLO model processes
images in real-time at 45 frames per second. A smaller version of the network,
Fast YOLO, processes an astounding 155 frames per second while still achieving
double the mAP of other real-time detectors. Compared to state-of-the-art
detection systems, YOLO makes more localization errors but is far less likely
to predict false detections where nothing exists. Finally, YOLO learns very
general representations of objects. It outperforms all other detection methods,
including DPM and R-CNN, by a wide margin when generalizing from natural images
to artwork on both the Picasso Dataset and the People-Art Dataset.
Computer Vision Approach for Low Cost, High Precision Measurement of Grapevine Trunk Diameter in Outdoor Conditions
Authors: Diego Sebastián Pérez, Facundo Bromberg, Francisco Gonzalez Antivilo
Date: 2014-05-16 00:00:00
Tasks: Management

Trunk diameter is a variable of agricultural interest, used mainly in the
prediction of fruit trees production. It is correlated with leaf area and
biomass of trees, and consequently gives a good estimate of the potential
production of the plants. This work presents a low cost, high precision method
for the measurement of trunk diameter of grapevines based on Computer Vision
techniques. Several methods based on Computer Vision and other techniques are
introduced in the literature. These methods present different advantages for
crop management: they are amenable to be operated by unknowledgeable personnel,
with lower operational costs; they result in lower stress levels to
knowledgeable personnel, avoiding the deterioration of the measurement quality
over time; and they make the measurement process amenable to be embedded in
larger autonomous systems, allowing more measurements to be taken with
equivalent costs. To date, all existing autonomous methods are either of low
precision, or have a prohibitive cost for massive agricultural adoption,
leaving the manual Vernier caliper or tape measure as the only choice in most
situations. In this work we present a semi-autonomous measurement method that
is susceptible to be fully automated, cost effective for mass adoption, and its
precision is competitive (with slight improvements) over the caliper manual
method.
Identification of refugee influx patterns in Greece via model-theoretic analysis of daily arrivals
Authors: Harris V. Georgiou
Date: 2016-05-09 00:00:00
Tasks: Dictionary Learning, Matrix Factorization / Decomposition

The refugee crisis is perhaps the single most challenging problem for Europe
today. Hundreds of thousands of people have already traveled across dangerous
sea passages from Turkish shores to Greek islands, resulting in thousands of
dead and missing, despite the best rescue efforts from both sides. One of the
main reasons is the total lack of any early warning-alerting system, which
could provide some preparation time for the prompt and effective deployment of
resources at the hot zones. This work is such an attempt for a systemic
analysis of the refugee influx in Greece, aiming at (a) the statistical and
signal-level characterization of the smuggling networks and (b) the formulation
and preliminary assessment of such models for predictive purposes, i.e., as the
basis of such an early warning-alerting protocol. To our knowledge, this is the
first-ever attempt to design such a system, since this refugee crisis itself
and its geographical properties are unique (intense event handling, little or
no warning). The analysis employs a wide range of statistical, signal-based and
matrix factorization (decomposition) techniques, including linear &
linear-cosine regression, spectral analysis, ARMA, SVD, Probabilistic PCA, ICA,
K-SVD for Dictionary Learning, as well as fractal dimension analysis. It is
established that the behavioral patterns of the smuggling networks closely
match (as expected) the regular burst and pause periods of store-and-forward
networks in digital communications. There are also major periodic trends in the
range of 6.2-6.5 days and strong correlations in lags of four or more days,
with distinct preference in the Sunday-Monday 48-hour time frame. These results
show that such models can be used successfully for short-term forecasting of
the influx intensity, producing an invaluable operational asset for planners,
decision-makers and first-responders.
Image Classification of Grapevine Buds using Scale-Invariant Features Transform, Bag of Features and Support Vector Machines
Authors: Diego Sebastián Pérez, Facundo Bromberg, Carlos Ariel Diaz
Date: 2016-05-09 00:00:00
Tasks: General Classification, image-classification, Image Classification

In viticulture, there are several applications where bud detection in
vineyard images is a necessary task, susceptible of being automated through the
use of computer vision methods. A common and effective family of visual
detection algorithms are the scanning-window type, that slide a (usually) fixed
size window along the original image, classifying each resulting windowed-patch
as containing or not containing the target object. The simplicity of these
algorithms finds its most challenging aspect in the classification stage.
Interested in grapevine buds detection in natural field conditions, this paper
presents a classification method for images of grapevine buds ranging 100 to
1600 pixels in diameter, captured in outdoor, under natural field conditions,
in winter (i.e., no grape bunches, very few leaves, and dormant buds), without
artificial background, and with minimum equipment requirements. The proposed
method uses well-known computer vision technologies: Scale-Invariant Feature
Transform for calculating low-level features, Bag of Features for building an
image descriptor, and Support Vector Machines for training a classifier. When
evaluated over images containing buds of at least 100 pixels in diameter, the
approach achieves a recall higher than 0.9 and a precision of 0.86 over all
windowed-patches covering the whole bud and down to 60% of it, and scaled up to
window patches containing a proportion of 20%-80% of bud versus background
pixels. This robustness on the position and size of the window demonstrates its
viability for use as the classification stage in a scanning-window detection
algorithms.
Anytime Bi-Objective Optimization with a Hybrid Multi-Objective CMA-ES (HMO-CMA-ES)
Authors: Ilya Loshchilov, Tobias Glasmachers
Date: 2016-05-09 00:00:00
Tasks: Benchmarking

We propose a multi-objective optimization algorithm aimed at achieving good
anytime performance over a wide range of problems. Performance is assessed in
terms of the hypervolume metric. The algorithm called HMO-CMA-ES represents a
hybrid of several old and new variants of CMA-ES, complemented by BOBYQA as a
warm start. We benchmark HMO-CMA-ES on the recently introduced bi-objective
problem suite of the COCO framework (COmparing Continuous Optimizers),
consisting of 55 scalable continuous optimization problems, which is used by
the Black-Box Optimization Benchmarking (BBOB) Workshop 2016.
Theano: A Python framework for fast computation of mathematical expressions
Authors: The Theano Development Team, Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermueller, Dzmitry Bahdanau, Nicolas Ballas, Frédéric Bastien, Justin Bayer, Anatoly Belikov, Alexander Belopolsky, Yoshua Bengio, Arnaud Bergeron, James Bergstra, Valentin Bisson, Josh Bleecher Snyder, Nicolas Bouchard, Nicolas Boulanger-Lewandowski, Xavier Bouthillier, Alexandre de Brébisson, Olivier Breuleux, Pierre-Luc Carrier, Kyunghyun Cho, Jan Chorowski, Paul Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Mélanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian Goodfellow, Matt Graham, Caglar Gulcehre, Philippe Hamel, Iban Harlouchet, Jean-Philippe Heng, Balázs Hidasi, Sina Honari, Arjun Jain, Sébastien Jean, Kai Jia, Mikhail Korobov, Vivek Kulkarni, Alex Lamb, Pascal Lamblin, Eric Larsen, César Laurent, Sean Lee, Simon Lefrancois, Simon Lemieux, Nicholas Léonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert T. McGibbon, Roland Memisevic, Bart van Merriënboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher Pal, Razvan Pascanu, Mohammad Pezeshki, Colin Raffel, Daniel Renshaw, Matthew Rocklin, Adriana Romero, Markus Roth, Peter Sadowski, John Salvatier, François Savard, Jan Schlüter, John Schulman, Gabriel Schwartz, Iulian Vlad Serban, Dmitriy Serdyuk, Samira Shabanian, Étienne Simon, Sigurd Spieckermann, S. Ramana Subramanyam, Jakub Sygnowski, Jérémie Tanguay, Gijs van Tulder, Joseph Turian, Sebastian Urban, Pascal Vincent, Francesco Visin, Harm de Vries, David Warde-Farley, Dustin J. Webb, Matthew Willson, Kelvin Xu, Lijun Xue, Li Yao, Saizheng Zhang, Ying Zhang
Date: 2016-05-09 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, CPU, Dimensionality Reduction, General Classification, GPU

Theano is a Python library that allows to define, optimize, and evaluate
mathematical expressions involving multi-dimensional arrays efficiently. Since
its introduction, it has been one of the most used CPU and GPU mathematical
compilers - especially in the machine learning community - and has shown steady
performance improvements. Theano is being actively and continuously developed
since 2008, multiple frameworks have been built on top of it and it has been
used to produce many state-of-the-art machine learning models.
  The present article is structured as follows. Section I provides an overview
of the Theano software and its community. Section II presents the principal
features of Theano and how to use them, and compares them with other similar
projects. Section III focuses on recently-introduced functionalities and
improvements. Section IV compares the performance of Theano against Torch7 and
TensorFlow on several machine learning models. Section V discusses current
limitations of Theano and potential ways of improving it.
Building a Large Scale Dataset for Image Emotion Recognition: The Fine Print and The Benchmark
Authors: Quanzeng You, Jiebo Luo, Hailin Jin, Jianchao Yang
Date: 2016-05-09 00:00:00
Tasks: Benchmarking, Emotion Recognition

Psychological research results have confirmed that people can have different
emotional reactions to different visual stimuli. Several papers have been
published on the problem of visual emotion analysis. In particular, attempts
have been made to analyze and predict people's emotional reaction towards
images. To this end, different kinds of hand-tuned features are proposed. The
results reported on several carefully selected and labeled small image data
sets have confirmed the promise of such features. While the recent successes of
many computer vision related tasks are due to the adoption of Convolutional
Neural Networks (CNNs), visual emotion analysis has not achieved the same level
of success. This may be primarily due to the unavailability of confidently
labeled and relatively large image data sets for visual emotion analysis. In
this work, we introduce a new data set, which started from 3+ million weakly
labeled images of different emotions and ended up 30 times as large as the
current largest publicly available visual emotion data set. We hope that this
data set encourages further research on visual emotion analysis. We also
perform extensive benchmarking analyses on this large data set using the state
of the art methods including CNNs.
Population fluctuation promotes cooperation in networks
Authors: Steve Miller, Joshua Knowles
Date: 2014-07-30 00:00:00

We consider the problem of explaining the emergence and evolution of
cooperation in dynamic network-structured populations. Building on seminal work
by Poncela et al, which shows how cooperation (in one-shot prisoner's dilemma)
is supported in growing populations by an evolutionary preferential attachment
(EPA) model, we investigate the effect of fluctuations in the population size.
We find that the fluctuating model is more robust than Poncela et al's in that
cooperation flourishes for a wide variety of initial conditions. In terms of
both the temptation to defect, and the types of strategies present in the
founder network, the fluctuating population is found to lead more securely to
cooperation. Further, we find that this model will also support the emergence
of cooperation from pre-existing non-cooperative random networks. This model,
like Poncela et al's, does not require agents to have memory, recognition of
other agents, or other cognitive abilities, and so may suggest a more general
explanation of the emergence of cooperation in early evolutionary transitions,
than mechanisms such as kin selection, direct and indirect reciprocity.