pwc-archive/papers-with-abstracts huggingface dataset:
Stochastic Dual Coordinate Ascent with Adaptive Probabilities
Authors: Dominik Csiba, Zheng Qu, Peter Richtárik
Date: 2015-02-27 00:00:00

This paper introduces AdaSDCA: an adaptive variant of stochastic dual
coordinate ascent (SDCA) for solving the regularized empirical risk
minimization problems. Our modification consists in allowing the method
adaptively change the probability distribution over the dual variables
throughout the iterative process. AdaSDCA achieves provably better complexity
bound than SDCA with the best fixed probability distribution, known as
importance sampling. However, it is of a theoretical character as it is
expensive to implement. We also propose AdaSDCA+: a practical variant which in
our experiments outperforms existing non-adaptive methods.
Image Segmentation in Liquid Argon Time Projection Chamber Detector
Authors: Piotr Płoński, Dorota Stefan, Robert Sulej, Krzysztof Zaremba
Date: 2015-02-27 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

The Liquid Argon Time Projection Chamber (LAr-TPC) detectors provide
excellent imaging and particle identification ability for studying neutrinos.
An efficient and automatic reconstruction procedures are required to exploit
potential of this imaging technology. Herein, a novel method for segmentation
of images from LAr-TPC detectors is presented. The proposed approach computes a
feature descriptor for each pixel in the image, which characterizes amplitude
distribution in pixel and its neighbourhood. The supervised classifier is
employed to distinguish between pixels representing particle's track and noise.
The classifier is trained and evaluated on the hand-labeled dataset. The
proposed approach can be a preprocessing step for reconstructing algorithms
working directly on detector images.
A Dictionary Approach to EBSD Indexing
Authors: Yu-Hui Chen, Se Un Park, Dennis Wei, Gregory Newstadt, Michael Jackson, Jeff P. Simmons, Marc De Graef, Alfred O. Hero
Date: 2015-02-26 00:00:00
Tasks: Anomaly Detection, Uncertainty Quantification

We propose a framework for indexing of grain and sub-grain structures in
electron backscatter diffraction (EBSD) images of polycrystalline materials.
The framework is based on a previously introduced physics-based forward model
by Callahan and De Graef (2013) relating measured patterns to grain
orientations (Euler angle). The forward model is tuned to the microscope and
the sample symmetry group. We discretize the domain of the forward model onto a
dense grid of Euler angles and for each measured pattern we identify the most
similar patterns in the dictionary. These patterns are used to identify
boundaries, detect anomalies, and index crystal orientations. The statistical
distribution of these closest matches is used in an unsupervised binary
decision tree (DT) classifier to identify grain boundaries and anomalous
regions. The DT classifies a pattern as an anomaly if it has an abnormally low
similarity to any pattern in the dictionary. It classifies a pixel as being
near a grain boundary if the highly ranked patterns in the dictionary differ
significantly over the pixels 3x3 neighborhood. Indexing is accomplished by
computing the mean orientation of the closest dictionary matches to each
pattern. The mean orientation is estimated using a maximum likelihood approach
that models the orientation distribution as a mixture of Von Mises-Fisher
distributions over the quaternionic 3-sphere. The proposed dictionary matching
approach permits segmentation, anomaly detection, and indexing to be performed
in a unified manner with the additional benefit of uncertainty quantification.
We demonstrate the proposed dictionary-based approach on a Ni-base IN100 alloy.
Probabilistic Zero-shot Classification with Semantic Rankings
Authors: Jihun Hamm, Mikhail Belkin
Date: 2015-02-27 00:00:00
Tasks: Classification, General Classification, Semantic Similarity, Semantic Textual Similarity, zero-shot-classification, Zero-Shot Learning

In this paper we propose a non-metric ranking-based representation of
semantic similarity that allows natural aggregation of semantic information
from multiple heterogeneous sources. We apply the ranking-based representation
to zero-shot learning problems, and present deterministic and probabilistic
zero-shot classifiers which can be built from pre-trained classifiers without
retraining. We demonstrate their the advantages on two large real-world image
datasets. In particular, we show that aggregating different sources of semantic
information, including crowd-sourcing, leads to more accurate classification.
SciRecSys: A Recommendation System for Scientific Publication by Discovering Keyword Relationships
Authors: Vu Le Anh, Vo Hoang Hai, Hung Nghiep Tran, Jason J. Jung
Date: 2015-02-27 00:00:00
Tasks: Articles

In this work, we propose a new approach for discovering various relationships
among keywords over the scientific publications based on a Markov Chain model.
It is an important problem since keywords are the basic elements for
representing abstract objects such as documents, user profiles, topics and many
things else. Our model is very effective since it combines four important
factors in scientific publications: content, publicity, impact and randomness.
Particularly, a recommendation system (called SciRecSys) has been presented to
support users to efficiently find out relevant articles.
Exact tensor completion using t-SVD
Authors: Zemin Zhang, Shuchin Aeron
Date: 2015-02-16 00:00:00
Tasks: Matrix Completion

In this paper we focus on the problem of completion of multidimensional
arrays (also referred to as tensors) from limited sampling. Our approach is
based on a recently proposed tensor-Singular Value Decomposition (t-SVD) [1].
Using this factorization one can derive notion of tensor rank, referred to as
the tensor tubal rank, which has optimality properties similar to that of
matrix rank derived from SVD. As shown in [2] some multidimensional data, such
as panning video sequences exhibit low tensor tubal rank and we look at the
problem of completing such data under random sampling of the data cube. We show
that by solving a convex optimization problem, which minimizes the tensor
nuclear norm obtained as the convex relaxation of tensor tubal rank, one can
guarantee recovery with overwhelming probability as long as samples in
proportion to the degrees of freedom in t-SVD are observed. In this sense our
results are order-wise optimal. The conditions under which this result holds
are very similar to the incoherency conditions for the matrix completion,
albeit we define incoherency under the algebraic set-up of t-SVD. We show the
performance of the algorithm on some real data sets and compare it with other
existing approaches based on tensor flattening and Tucker decomposition.
Second-order Quantile Methods for Experts and Combinatorial Games
Authors: Wouter M. Koolen, Tim van Erven
Date: 2015-02-27 00:00:00
Tasks: Decision Making, Sequential Decision Making

We aim to design strategies for sequential decision making that adjust to the
difficulty of the learning problem. We study this question both in the setting
of prediction with expert advice, and for more general combinatorial decision
tasks. We are not satisfied with just guaranteeing minimax regret rates, but we
want our algorithms to perform significantly better on easy data. Two popular
ways to formalize such adaptivity are second-order regret bounds and quantile
bounds. The underlying notions of 'easy data', which may be paraphrased as "the
learning problem has small variance" and "multiple decisions are useful", are
synergetic. But even though there are sophisticated algorithms that exploit one
of the two, no existing algorithm is able to adapt to both.
  In this paper we outline a new method for obtaining such adaptive algorithms,
based on a potential function that aggregates a range of learning rates (which
are essential tuning parameters). By choosing the right prior we construct
efficient algorithms and show that they reap both benefits by proving the first
bounds that are both second-order and incorporate quantiles.
Non-stochastic Best Arm Identification and Hyperparameter Optimization
Authors: Kevin Jamieson, Ameet Talwalkar
Date: 2015-02-27 00:00:00
Tasks: Hyperparameter Optimization

Motivated by the task of hyperparameter optimization, we introduce the
non-stochastic best-arm identification problem. Within the multi-armed bandit
literature, the cumulative regret objective enjoys algorithms and analyses for
both the non-stochastic and stochastic settings while to the best of our
knowledge, the best-arm identification framework has only been considered in
the stochastic setting. We introduce the non-stochastic setting under this
framework, identify a known algorithm that is well-suited for this setting, and
analyze its behavior. Next, by leveraging the iterative nature of standard
machine learning algorithms, we cast hyperparameter optimization as an instance
of non-stochastic best-arm identification, and empirically evaluate our
proposed algorithm on this task. Our empirical results show that, by allocating
more resources to promising hyperparameter settings, we typically achieve
comparable test accuracies an order of magnitude faster than baseline methods.
Generative Modeling of Hidden Functional Brain Networks
Authors: Shaurabh Nandy, Richard M. Golden
Date: 2014-12-20 00:00:00
Tasks: Functional Connectivity, Time Series, Time Series Analysis

Functional connectivity refers to the temporal statistical relationship
between spatially distinct brain regions and is usually inferred from the time
series coherence/correlation in brain activity between regions of interest. In
human functional brain networks, the network structure is often inferred from
functional magnetic resonance imaging (fMRI) blood oxygen level dependent
(BOLD) signal. Since the BOLD signal is a proxy for neuronal activity, it is of
interest to learn the latent functional network structure. Additionally,
despite a core set of observations about functional networks such as
small-worldness, modularity, exponentially truncated degree distributions, and
presence of various types of hubs, very little is known about the computational
principles which can give rise to these observations. This paper introduces a
Hidden Markov Random Field framework for the purpose of representing,
estimating, and evaluating latent neuronal functional relationships between
different brain regions using fMRI data.
Local Translation Prediction with Global Sentence Representation
Authors: Jiajun Zhang
Date: 2015-02-27 00:00:00
Tasks: Machine Translation, Prediction, Sentence, Translation

Statistical machine translation models have made great progress in improving
the translation quality. However, the existing models predict the target
translation with only the source- and target-side local context information. In
practice, distinguishing good translations from bad ones does not only depend
on the local features, but also rely on the global sentence-level information.
In this paper, we explore the source-side global sentence-level features for
target-side local translation prediction. We propose a novel
bilingually-constrained chunk-based convolutional neural network to learn
sentence semantic representations. With the sentence-level feature
representation, we further design a feed-forward neural network to better
predict translations using both local and global information. The large-scale
experiments show that our method can obtain substantial improvements in
translation quality over the strong baseline: the hierarchical phrase-based
translation model augmented with the neural network joint model.
Unsupervised Domain Adaptation by Backpropagation
Authors: Yaroslav Ganin, Victor Lempitsky
Date: 2014-09-26 00:00:00
Tasks: Domain Adaptation, image-classification, Image Classification, Multi-target Domain Adaptation, Transfer Learning, Unsupervised Domain Adaptation, Unsupervised Image-To-Image Translation

Top-performing deep architectures are trained on massive amounts of labeled
data. In the absence of labeled data for a certain task, domain adaptation
often provides an attractive option given that labeled data of similar nature
but from a different domain (e.g. synthetic images) are available. Here, we
propose a new approach to domain adaptation in deep architectures that can be
trained on large amount of labeled data from the source domain and large amount
of unlabeled data from the target domain (no labeled target-domain data is
necessary).
  As the training progresses, the approach promotes the emergence of "deep"
features that are (i) discriminative for the main learning task on the source
domain and (ii) invariant with respect to the shift between the domains. We
show that this adaptation behaviour can be achieved in almost any feed-forward
model by augmenting it with few standard layers and a simple new gradient
reversal layer. The resulting augmented architecture can be trained using
standard backpropagation.
  Overall, the approach can be implemented with little effort using any of the
deep-learning packages. The method performs very well in a series of image
classification experiments, achieving adaptation effect in the presence of big
domain shifts and outperforming previous state-of-the-art on Office datasets.
Study on Sparse Representation based Classification for Biometric Verification
Authors: Zengxi Huang, Yiguang Liu, Xiaoming Wang, Jinrong Hu
Date: 2015-02-21 00:00:00
Tasks: Classification, General Classification, Sparse Representation-based Classification

In this paper, we propose a multimodal verification system integrating face
and ear based on sparse representation based classification (SRC). The face and
ear query samples are first encoded separately to derive sparsity-based match
scores, and which are then combined with sum-rule fusion for verification.
Apart from validating the encouraging performance of SRC-based multimodal
verification, this paper also dedicates to provide a clear understanding about
the characteristics of SRC-based biometric verification. To this end, two
sparsity-based metrics, i.e. spare coding error (SCE) and sparse contribution
rate (SCR), are involved, together with face and ear unimodal SRC-based
verification. As for the issue that SRC-based biometric verification may suffer
from heavy computational burden and verification accuracy degradation with
increase of enrolled subjects, we argue that it could be properly resolved by
exploiting small random dictionary for sparsity-based score computation, which
consists of training samples from a limited number of randomly selected
subjects. Experimental results demonstrate the superiority of SRC-based
multimodal verification compared to the state-of-the-art multimodal methods
like likelihood ratio (LLR), support vector machine (SVM), and the sum-rule
fusion methods using cosine similarity, meanwhile the idea of using small
random dictionary is feasible in both effectiveness and efficiency.
Hybrid coding of visual content and local image features
Authors: Luca Baroffio, Matteo Cesana, Alessandro Redondi, Marco Tagliasacchi, Stefano Tubaro
Date: 2015-02-27 00:00:00

Distributed visual analysis applications, such as mobile visual search or
Visual Sensor Networks (VSNs) require the transmission of visual content on a
bandwidth-limited network, from a peripheral node to a processing unit.
Traditionally, a Compress-Then-Analyze approach has been pursued, in which
sensing nodes acquire and encode the pixel-level representation of the visual
content, that is subsequently transmitted to a sink node in order to be
processed. This approach might not represent the most effective solution, since
several analysis applications leverage a compact representation of the content,
thus resulting in an inefficient usage of network resources. Furthermore,
coding artifacts might significantly impact the accuracy of the visual task at
hand. To tackle such limitations, an orthogonal approach named
Analyze-Then-Compress has been proposed. According to such a paradigm, sensing
nodes are responsible for the extraction of visual features, that are encoded
and transmitted to a sink node for further processing. In spite of improved
task efficiency, such paradigm implies the central processing node not being
able to reconstruct a pixel-level representation of the visual content. In this
paper we propose an effective compromise between the two paradigms, namely
Hybrid-Analyze-Then-Compress (HATC) that aims at jointly encoding visual
content and local image features. Furthermore, we show how a target tradeoff
between image quality and task accuracy might be achieved by accurately
allocating the bitrate to either visual content or local features.
Language Recognition using Random Indexing
Authors: Aditya Joshi, Johan Halseth, Pentti Kanerva
Date: 2014-12-22 00:00:00

Random Indexing is a simple implementation of Random Projections with a wide
range of applications. It can solve a variety of problems with good accuracy
without introducing much complexity. Here we use it for identifying the
language of text samples. We present a novel method of generating language
representation vectors using letter blocks. Further, we show that the method is
easily implemented and requires little computational power and space.
Experiments on a number of model parameters illustrate certain properties about
high dimensional sparse vector representations of data. Proof of statistically
relevant language vectors are shown through the extremely high success of
various language recognition tasks. On a difficult data set of 21,000 short
sentences from 21 different languages, our model performs a language
recognition task and achieves 97.8% accuracy, comparable to state-of-the-art
methods.
Minimum message length estimation of mixtures of multivariate Gaussian and von Mises-Fisher distributions
Authors: Parthan Kasarapu, Lloyd Allison
Date: 2015-02-27 00:00:00

Mixture modelling involves explaining some observed evidence using a
combination of probability distributions. The crux of the problem is the
inference of an optimal number of mixture components and their corresponding
parameters. This paper discusses unsupervised learning of mixture models using
the Bayesian Minimum Message Length (MML) criterion. To demonstrate the
effectiveness of search and inference of mixture parameters using the proposed
approach, we select two key probability distributions, each handling
fundamentally different types of data: the multivariate Gaussian distribution
to address mixture modelling of data distributed in Euclidean space, and the
multivariate von Mises-Fisher (vMF) distribution to address mixture modelling
of directional data distributed on a unit hypersphere. The key contributions of
this paper, in addition to the general search and inference methodology,
include the derivation of MML expressions for encoding the data using
multivariate Gaussian and von Mises-Fisher distributions, and the analytical
derivation of the MML estimates of the parameters of the two distributions. Our
approach is tested on simulated and real world data sets. For instance, we
infer vMF mixtures that concisely explain experimentally determined
three-dimensional protein conformations, providing an effective null model
description of protein structures that is central to many inference problems in
structural bioinformatics. The experimental results demonstrate that the
performance of our proposed search and inference method along with the encoding
schemes improve on the state of the art mixture modelling techniques.
Modelling Local Deep Convolutional Neural Network Features to Improve Fine-Grained Image Classification
Authors: ZongYuan Ge, Chris McCool, Conrad Sanderson, Peter Corke
Date: 2015-02-27 00:00:00
Tasks: Fine-Grained Image Classification, General Classification, image-classification, Image Classification, Object Recognition

We propose a local modelling approach using deep convolutional neural
networks (CNNs) for fine-grained image classification. Recently, deep CNNs
trained from large datasets have considerably improved the performance of
object recognition. However, to date there has been limited work using these
deep CNNs as local feature extractors. This partly stems from CNNs having
internal representations which are high dimensional, thereby making such
representations difficult to model using stochastic models. To overcome this
issue, we propose to reduce the dimensionality of one of the internal fully
connected layers, in conjunction with layer-restricted retraining to avoid
retraining the entire network. The distribution of low-dimensional features
obtained from the modified layer is then modelled using a Gaussian mixture
model. Comparative experiments show that considerable performance improvements
can be achieved on the challenging Fish and UEC FOOD-100 datasets.
The conjugated null space method of blind PSF estimation and deconvolution optimization
Authors: Yuriy A. Bunyak, Roman N. Kvetnyy, Olga Yu. Sofina
Date: 2015-02-26 00:00:00
Tasks: Denoising, image smoothing

We have shown that the vector of the point spread function (PSF)
lexicographical presentation belongs to the left side conjugated null space
(NS) of the autoregression (AR) matrix operator on condition the AR parameters
are common for original and blurred images. The method of the PSF and inverse
PSF (IPSF) evaluation in the basis of the NS eigenfunctions is offered. The
optimization of the PSF and IPSF shape with the aim of fluctuation elimination
is considered in NS spectral domain and image space domain. The function of
surface area was used as the regularization functional. Two methods of original
image estimate optimization were designed basing on maximum entropy
generalization of sought and blurred images conditional probability density and
regularization. The first method uses balanced variations of convolutions with
the PSF and IPSF to obtaining iterative schema of image optimization. The
variations balance is providing by dynamic regularization basing on condition
of the iteration process convergence. The regularization has dynamic character
because depends on current and previous image estimate variations. The second
method implements the regularization of the deconvolution optimization in
curved space with metric defined on image estimate surface. The given iterative
schemas have fast convergence and therefore can be used for reconstruction of
high resolution images series in real time. The NS can be used for design of
denoising bilateral linear filter which does not introduce image smoothing.
Efficient Geometric-based Computation of the String Subsequence Kernel
Authors: Slimane Bellaouar, Hadda Cherroun, Djelloul Ziadi
Date: 2015-02-26 00:00:00

Kernel methods are powerful tools in machine learning. They have to be
computationally efficient. In this paper, we present a novel Geometric-based
approach to compute efficiently the string subsequence kernel (SSK). Our main
idea is that the SSK computation reduces to range query problem. We started by
the construction of a match list $L(s,t)=\{(i,j):s_{i}=t_{j}\}$ where $s$ and
$t$ are the strings to be compared; such match list contains only the required
data that contribute to the result. To compute efficiently the SSK, we extended
the layered range tree data structure to a layered range sum tree, a
range-aggregation data structure. The whole process takes $ O(p|L|\log|L|)$
time and $O(|L|\log|L|)$ space, where $|L|$ is the size of the match list and
$p$ is the length of the SSK. We present empiric evaluations of our approach
against the dynamic and the sparse programming approaches both on synthetically
generated data and on newswire article data. Such experiments show the
efficiency of our approach for large alphabet size except for very short
strings. Moreover, compared to the sparse dynamic approach, the proposed
approach outperforms absolutely for long strings.