pwc-archive/papers-with-abstracts huggingface dataset:
Fast optimization of Multithreshold Entropy Linear Classifier
Authors: Rafal Jozefowicz, Wojciech Marian Czarnecki
Date: 2015-04-18 00:00:00
Tasks: Density Estimation

Multithreshold Entropy Linear Classifier (MELC) is a density based model
which searches for a linear projection maximizing the Cauchy-Schwarz Divergence
of dataset kernel density estimation. Despite its good empirical results, one
of its drawbacks is the optimization speed. In this paper we analyze how one
can speed it up through solving an approximate problem. We analyze two methods,
both similar to the approximate solutions of the Kernel Density Estimation
querying and provide adaptive schemes for selecting a crucial parameters based
on user-specified acceptable error. Furthermore we show how one can exploit
well known conjugate gradients and L-BFGS optimizers despite the fact that the
original optimization problem should be solved on the sphere. All above methods
and modifications are tested on 10 real life datasets from UCI repository to
confirm their practical usability.
Gap Analysis of Natural Language Processing Systems with respect to Linguistic Modality
Authors: Vishal Shukla
Date: 2015-04-18 00:00:00

Modality is one of the important components of grammar in linguistics. It
lets speaker to express attitude towards, or give assessment or potentiality of
state of affairs. It implies different senses and thus has different
perceptions as per the context. This paper presents an account showing the gap
in the functionality of the current state of art Natural Language Processing
(NLP) systems. The contextual nature of linguistic modality is studied. In this
paper, the works and logical approaches employed by Natural Language Processing
systems dealing with modality are reviewed. It sees human cognition and
intelligence as multi-layered approach that can be implemented by intelligent
systems for learning. Lastly, current flow of research going on within this
field is talked providing futurology.
Web-Scale Training for Face Identification
Authors: Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, Lior Wolf
Date: 2014-06-20 00:00:00
Proceeding: CVPR 2015 6
Tasks: Face Identification, Face Recognition, Transfer Learning

Scaling machine learning methods to very large datasets has attracted considerable attention in recent years, thanks to easy access to ubiquitous sensing and data from the web. We study face recognition and show that three distinct properties have surprising effects on the transferability of deep convolutional networks (CNN): (1) The bottleneck of the network serves as an important transfer learning regularizer, and (2) in contrast to the common wisdom, performance saturation may exist in CNN's (as the number of training samples grows); we propose a solution for alleviating this by replacing the naive random subsampling of the training set with a bootstrapping process. Moreover, (3) we find a link between the representation norm and the ability to discriminate in a target domain, which sheds lights on how such networks represent faces. Based on these discoveries, we are able to improve face recognition accuracy on the widely used LFW benchmark, both in the verification (1:1) and identification (1:N) protocols, and directly compare, for the first time, with the state of the art Commercially-Off-The-Shelf system and show a sizable leap in performance.
Adaptive Stochastic Gradient Descent on the Grassmannian for Robust Low-Rank Subspace Recovery and Clustering
Authors: Jun He, Yue Zhang
Date: 2014-12-12 00:00:00
Tasks: Clustering, Stochastic Optimization

In this paper, we present GASG21 (Grassmannian Adaptive Stochastic Gradient
for $L_{2,1}$ norm minimization), an adaptive stochastic gradient algorithm to
robustly recover the low-rank subspace from a large matrix. In the presence of
column outliers, we reformulate the batch mode matrix $L_{2,1}$ norm
minimization with rank constraint problem as a stochastic optimization approach
constrained on Grassmann manifold. For each observed data vector, the low-rank
subspace $\mathcal{S}$ is updated by taking a gradient step along the geodesic
of Grassmannian. In order to accelerate the convergence rate of the stochastic
gradient method, we choose to adaptively tune the constant step-size by
leveraging the consecutive gradients. Furthermore, we demonstrate that with
proper initialization, the K-subspaces extension, K-GASG21, can robustly
cluster a large number of corrupted data vectors into a union of subspaces.
Numerical experiments on synthetic and real data demonstrate the efficiency and
accuracy of the proposed algorithms even with heavy column outliers corruption.
Local, Private, Efficient Protocols for Succinct Histograms
Authors: Raef Bassily, Adam Smith
Date: 2015-04-18 00:00:00
Tasks: Computational Efficiency

We give efficient protocols and matching accuracy lower bounds for frequency
estimation in the local model for differential privacy. In this model,
individual users randomize their data themselves, sending differentially
private reports to an untrusted server that aggregates them.
  We study protocols that produce a succinct histogram representation of the
data. A succinct histogram is a list of the most frequent items in the data
(often called "heavy hitters") along with estimates of their frequencies; the
frequency of all other items is implicitly estimated as 0.
  If there are $n$ users whose items come from a universe of size $d$, our
protocols run in time polynomial in $n$ and $\log(d)$. With high probability,
they estimate the accuracy of every item up to error
$O\left(\sqrt{\log(d)/(\epsilon^2n)}\right)$ where $\epsilon$ is the privacy
parameter. Moreover, we show that this much error is necessary, regardless of
computational efficiency, and even for the simple setting where only one item
appears with significant frequency in the data set.
  Previous protocols (Mishra and Sandler, 2006; Hsu, Khanna and Roth, 2012) for
this task either ran in time $\Omega(d)$ or had much worse error (about
$\sqrt[6]{\log(d)/(\epsilon^2n)}$), and the only known lower bound on error was
$\Omega(1/\sqrt{n})$.
  We also adapt a result of McGregor et al (2010) to the local setting. In a
model with public coins, we show that each user need only send 1 bit to the
server. For all known local protocols (including ours), the transformation
preserves computational efficiency.
Inverse Reinforcement Learning with Multi-Relational Chains for Robot-Centered Smart Home
Authors: Kun Li, Max Q. -H. Meng
Date: 2014-08-16 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In a robot-centered smart home, the robot observes the home states with its
own sensors, and then it can change certain object states according to an
operator's commands for remote operations, or imitate the operator's behaviors
in the house for autonomous operations. To model the robot's imitation of the
operator's behaviors in a dynamic indoor environment, we use multi-relational
chains to describe the changes of environment states, and apply inverse
reinforcement learning to encoding the operator's behaviors with a learned
reward function. We implement this approach with a mobile robot, and do five
experiments to include increasing training days, object numbers, and action
types. Besides, a baseline method by directly recording the operator's
behaviors is also implemented, and comparison is made on the accuracy of home
state evaluation and the accuracy of robot action selection. The results show
that the proposed approach handles dynamic environment well, and guides the
robot's actions in the house more accurately.
Unsupervised Dependency Parsing: Let's Use Supervised Parsers
Authors: Phong Le, Willem Zuidema
Date: 2015-04-18 00:00:00
Proceeding: HLT 2015 5
Tasks: Dependency Parsing, Reranking, Unsupervised Dependency Parsing

We present a self-training approach to unsupervised dependency parsing that
reuses existing supervised and unsupervised parsing algorithms. Our approach,
called `iterated reranking' (IR), starts with dependency trees generated by an
unsupervised parser, and iteratively improves these trees using the richer
probability models used in supervised parsing that are in turn trained on these
trees. Our system achieves 1.8% accuracy higher than the state-of-the-part
parser of Spitkovsky et al. (2013) on the WSJ corpus.
Compositional Distributional Semantics with Long Short Term Memory
Authors: Phong Le, Willem Zuidema
Date: 2015-03-09 00:00:00
Proceeding: SEMEVAL 2015 6

We are proposing an extension of the recursive neural network that makes use
of a variant of the long short-term memory architecture. The extension allows
information low in parse trees to be stored in a memory register (the `memory
cell') and used much later higher up in the parse tree. This provides a
solution to the vanishing gradient problem and allows the network to capture
long range dependencies. Experimental results show that our composition
outperformed the traditional neural-network composition on the Stanford
Sentiment Treebank.
A spectral optical flow method for determining velocities from digital imagery
Authors: Neal Hurlburt, Steve Jaffey
Date: 2015-04-17 00:00:00
Tasks: Optical Flow Estimation

We present a method for determining surface flows from solar images based
upon optical flow techniques. We apply the method to sets of images obtained by
a variety of solar imagers to assess its performance. The {\tt opflow3d}
procedure is shown to extract accurate velocity estimates when provided perfect
test data and quickly generates results consistent with completely distinct
methods when applied on global scales. We also validate it in detail by
comparing it to an established method when applied to high-resolution datasets
and find that it provides comparable results without the need to tune, filter
or otherwise preprocess the images before its application.
Deep Karaoke: Extracting Vocals from Musical Mixtures Using a Convolutional Deep Neural Network
Authors: Andrew J. R. Simpson, Gerard Roma, Mark D. Plumbley
Date: 2015-04-17 00:00:00
Tasks: Speech Separation

Identification and extraction of singing voice from within musical mixtures
is a key challenge in source separation and machine audition. Recently, deep
neural networks (DNN) have been used to estimate 'ideal' binary masks for
carefully controlled cocktail party speech separation problems. However, it is
not yet known whether these methods are capable of generalizing to the
discrimination of voice and non-voice in the context of musical mixtures. Here,
we trained a convolutional DNN (of around a billion parameters) to provide
probabilistic estimates of the ideal binary mask for separation of vocal sounds
from real-world musical mixtures. We contrast our DNN results with more
traditional linear methods. Our approach may be useful for automatic removal of
vocal sounds from musical mixtures for 'karaoke' type applications.
Biometrics for Child Vaccination and Welfare: Persistence of Fingerprint Recognition for Infants and Toddlers
Authors: Anil K. Jain, Sunpreet S. Arora, Lacey Best-Rowden, Kai Cao, Prem Sewak Sudhish, Anjoo Bhatnagar
Date: 2015-04-17 00:00:00

With a number of emerging applications requiring biometric recognition of
children (e.g., tracking child vaccination schedules, identifying missing
children and preventing newborn baby swaps in hospitals), investigating the
temporal stability of biometric recognition accuracy for children is important.
The persistence of recognition accuracy of three of the most commonly used
biometric traits (fingerprints, face and iris) has been investigated for
adults. However, persistence of biometric recognition accuracy has not been
studied systematically for children in the age group of 0-4 years. Given that
very young children are often uncooperative and do not comprehend or follow
instructions, in our opinion, among all biometric modalities, fingerprints are
the most viable for recognizing children. This is primarily because it is
easier to capture fingerprints of young children compared to other biometric
traits, e.g., iris, where a child needs to stare directly towards the camera to
initiate iris capture. In this report, we detail our initiative to investigate
the persistence of fingerprint recognition for children in the age group of 0-4
years. Based on preliminary results obtained for the data collected in the
first phase of our study, use of fingerprints for recognition of 0-4 year-old
children appears promising.
Performance Evaluation of Machine Learning Algorithms in Post-operative Life Expectancy in the Lung Cancer Patients
Authors: Kwetishe Joro Danjuma
Date: 2015-04-17 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification, Ingenuity, Prognosis

The nature of clinical data makes it difficult to quickly select, tune and
apply machine learning algorithms to clinical prognosis. As a result, a lot of
time is spent searching for the most appropriate machine learning algorithms
applicable in clinical prognosis that contains either binary-valued or
multi-valued attributes. The study set out to identify and evaluate the
performance of machine learning classification schemes applied in clinical
prognosis of post-operative life expectancy in the lung cancer patients.
Multilayer Perceptron, J48, and the Naive Bayes algorithms were used to train
and test models on Thoracic Surgery datasets obtained from the University of
California Irvine machine learning repository. Stratified 10-fold
cross-validation was used to evaluate baseline performance accuracy of the
classifiers. The comparative analysis shows that multilayer perceptron
performed best with classification accuracy of 82.3%, J48 came out second with
classification accuracy of 81.8%, and Naive Bayes came out the worst with
classification accuracy of 74.4%. The quality and outcome of the chosen machine
learning algorithms depends on the ingenuity of the clinical miner.
Convex Learning of Multiple Tasks and their Structure
Authors: Carlo Ciliberto, Youssef Mroueh, Tomaso Poggio, Lorenzo Rosasco
Date: 2015-04-13 00:00:00
Tasks: Multi-Task Learning

Reducing the amount of human supervision is a key problem in machine learning
and a natural approach is that of exploiting the relations (structure) among
different tasks. This is the idea at the core of multi-task learning. In this
context a fundamental question is how to incorporate the tasks structure in the
learning problem.We tackle this question by studying a general computational
framework that allows to encode a-priori knowledge of the tasks structure in
the form of a convex penalty; in this setting a variety of previously proposed
methods can be recovered as special cases, including linear and non-linear
approaches. Within this framework, we show that tasks and their structure can
be efficiently learned considering a convex optimization problem that can be
approached by means of block coordinate methods such as alternating
minimization and for which we prove convergence to the global minimum.
Visual Scene Representations: Contrast, Scaling and Occlusion
Authors: Stefano Soatto, Jingming Dong, Nikolaos Karianakis
Date: 2014-12-20 00:00:00
Tasks: Two-sample testing

We study the structure of representations, defined as approximations of
minimal sufficient statistics that are maximal invariants to nuisance factors,
for visual data subject to scaling and occlusion of line-of-sight. We derive
analytical expressions for such representations and show that, under certain
restrictive assumptions, they are related to features commonly in use in the
computer vision community. This link highlights the condition tacitly assumed
by these descriptors, and also suggests ways to improve and generalize them.
This new interpretation draws connections to the classical theories of
sampling, hypothesis testing and group invariance.
A comparison of dense region detectors for image search and fine-grained classification
Authors: Ahmet Iscen, Giorgos Tolias, Philippe-Henri Gosselin, Hervé Jégou
Date: 2014-10-29 00:00:00
Tasks: General Classification, image-classification, Image Classification, Image Retrieval, Retrieval

We consider a pipeline for image classification or search based on coding
approaches like Bag of Words or Fisher vectors. In this context, the most
common approach is to extract the image patches regularly in a dense manner on
several scales. This paper proposes and evaluates alternative choices to
extract patches densely. Beyond simple strategies derived from regular interest
region detectors, we propose approaches based on super-pixels, edges, and a
bank of Zernike filters used as detectors. The different approaches are
evaluated on recent image retrieval and fine-grain classification benchmarks.
Our results show that the regular dense detector is outperformed by other
methods in most situations, leading us to improve the state of the art in
comparable setups on standard retrieval and fined-grain benchmarks. As a
byproduct of our study, we show that existing methods for blob and super-pixel
extraction achieve high accuracy if the patches are extracted along the edges
and not around the detected regions.
Testing Closeness With Unequal Sized Samples
Authors: Bhaswar B. Bhattacharya, Gregory Valiant
Date: 2015-04-17 00:00:00
Proceeding: NeurIPS 2015 12

We consider the problem of closeness testing for two discrete distributions
in the practically relevant setting of \emph{unequal} sized samples drawn from
each of them. Specifically, given a target error parameter $\varepsilon > 0$,
$m_1$ independent draws from an unknown distribution $p,$ and $m_2$ draws from
an unknown distribution $q$, we describe a test for distinguishing the case
that $p=q$ from the case that $||p-q||_1 \geq \varepsilon$. If $p$ and $q$ are
supported on at most $n$ elements, then our test is successful with high
probability provided $m_1\geq n^{2/3}/\varepsilon^{4/3}$ and $m_2 =
\Omega(\max\{\frac{n}{\sqrt m_1\varepsilon^2}, \frac{\sqrt
n}{\varepsilon^2}\});$ we show that this tradeoff is optimal throughout this
range, to constant factors. These results extend the recent work of Chan et al.
who established the sample complexity when the two samples have equal sizes,
and tightens the results of Acharya et al. by polynomials factors in both $n$
and $\varepsilon$. As a consequence, we obtain an algorithm for estimating the
mixing time of a Markov chain on $n$ states up to a $\log n$ factor that uses
$\tilde{O}(n^{3/2} \tau_{mix})$ queries to a "next node" oracle, improving upon
the $\tilde{O}(n^{5/3}\tau_{mix})$ query algorithm of Batu et al. Finally, we
note that the core of our testing algorithm is a relatively simple statistic
that seems to perform well in practice, both on synthetic data and on natural
language data.
The Nataf-Beta Random Field Classifier: An Extension of the Beta Conjugate Prior to Classification Problems
Authors: James-A. Goulet
Date: 2015-04-17 00:00:00
Tasks: Attribute, Classification, General Classification

This paper presents the Nataf-Beta Random Field Classifier, a discriminative
approach that extends the applicability of the Beta conjugate prior to
classification problems. The approach's key feature is to model the probability
of a class conditional on attribute values as a random field whose marginals
are Beta distributed, and where the parameters of marginals are themselves
described by random fields. Although the classification accuracy of the
approach proposed does not statistically outperform the best accuracies
reported in the literature, it ranks among the top tier for the six benchmark
datasets tested. The Nataf-Beta Random Field Classifier is suited as a general
purpose classification approach for real-continuous and real-integer attribute
value problems.
GReTA - a novel Global and Recursive Tracking Algorithm in three dimensions
Authors: Alessandro Attanasi, Andrea Cavagna, Lorenzo Del Castello, Irene Giardina, Asja Jelic, Stefania Melillo, Leonardo Parisi, Fabio Pellacini, Edward Shen, Edmondo Silvestri, Massimiliano Viale
Date: 2013-05-07 00:00:00
Tasks: global-optimization

Tracking multiple moving targets allows quantitative measure of the dynamic
behavior in systems as diverse as animal groups in biology, turbulence in fluid
dynamics and crowd and traffic control. In three dimensions, tracking several
targets becomes increasingly hard since optical occlusions are very likely,
i.e. two featureless targets frequently overlap for several frames. Occlusions
are particularly frequent in biological groups such as bird flocks, fish
schools, and insect swarms, a fact that has severely limited collective animal
behavior field studies in the past. This paper presents a 3D tracking method
that is robust in the case of severe occlusions. To ensure robustness, we adopt
a global optimization approach that works on all objects and frames at once. To
achieve practicality and scalability, we employ a divide and conquer
formulation, thanks to which the computational complexity of the problem is
reduced by orders of magnitude. We tested our algorithm with synthetic data,
with experimental data of bird flocks and insect swarms and with public
benchmark datasets, and show that our system yields high quality trajectories
for hundreds of moving targets with severe overlap. The results obtained on
very heterogeneous data show the potential applicability of our method to the
most diverse experimental situations.
Color Constancy Using CNNs
Authors: Simone Bianco, Claudio Cusano, Raimondo Schettini
Date: 2015-04-17 00:00:00
Tasks: Color Constancy, regression

In this work we describe a Convolutional Neural Network (CNN) to accurately
predict the scene illumination. Taking image patches as input, the CNN works in
the spatial domain without using hand-crafted features that are employed by
most previous methods. The network consists of one convolutional layer with max
pooling, one fully connected layer and three output nodes. Within the network
structure, feature learning and regression are integrated into one optimization
process, which leads to a more effective model for estimating scene
illumination. This approach achieves state-of-the-art performance on a standard
dataset of RAW images. Preliminary experiments on images with spatially varying
illumination demonstrate the stability of the local illuminant estimation
ability of our CNN.
Hyperspectral pansharpening: a review
Authors: Laetitia Loncan, Luis B. Almeida, José M. Bioucas-Dias, Xavier Briottet, Jocelyn Chanussot, Nicolas Dobigeon, Sophie Fabre, Wenzhi Liao, Giorgio A. Licciardi, Miguel Simões, Jean-Yves Tourneret, Miguel A. Veganzones, Gemine Vivone, Qi Wei, Naoto Yokoya
Date: 2015-04-17 00:00:00
Tasks: Pansharpening

Pansharpening aims at fusing a panchromatic image with a multispectral one,
to generate an image with the high spatial resolution of the former and the
high spectral resolution of the latter. In the last decade, many algorithms
have been presented in the literature for pansharpening using multispectral
data. With the increasing availability of hyperspectral systems, these methods
are now being adapted to hyperspectral images. In this work, we compare new
pansharpening techniques designed for hyperspectral data with some of the state
of the art methods for multispectral pansharpening, which have been adapted for
hyperspectral data. Eleven methods from different classes (component
substitution, multiresolution analysis, hybrid, Bayesian and matrix
factorization) are analyzed. These methods are applied to three datasets and
their effectiveness and robustness are evaluated with widely used performance
indicators. In addition, all the pansharpening techniques considered in this
paper have been implemented in a MATLAB toolbox that is made available to the
community.