pwc-archive/papers-with-abstracts huggingface dataset:
Towards Learning free Naive Bayes Nearest Neighbor-based Domain Adaptation
Authors: Faraz Saeedan, Barbara Caputo
Date: 2015-03-26 00:00:00
Tasks: Domain Adaptation, Object Categorization

As of today, object categorization algorithms are not able to achieve the
level of robustness and generality necessary to work reliably in the real
world. Even the most powerful convolutional neural network we can train fails
to perform satisfactorily when trained and tested on data from different
databases. This issue, known as domain adaptation and/or dataset bias in the
literature, is due to a distribution mismatch between data collections. Methods
addressing it go from max-margin classifiers to learning how to modify the
features and obtain a more robust representation. Recent work showed that by
casting the problem into the image-to-class recognition framework, the domain
adaptation problem is significantly alleviated \cite{danbnn}. Here we follow
this approach, and show how a very simple, learning free Naive Bayes Nearest
Neighbor (NBNN)-based domain adaptation algorithm can significantly alleviate
the distribution mismatch among source and target data, especially when the
number of classes and the number of sources grow. Experiments on standard
benchmarks used in the literature show that our approach (a) is competitive
with the current state of the art on small scale problems, and (b) achieves the
current state of the art as the number of classes and sources grows, with
minimal computational requirements.
A Unified Perspective on Multi-Domain and Multi-Task Learning
Authors: Yongxin Yang, Timothy M. Hospedales
Date: 2014-12-23 00:00:00
Tasks: Domain Adaptation, Multi-Task Learning, Zero-Shot Learning

In this paper, we provide a new neural-network based perspective on
multi-task learning (MTL) and multi-domain learning (MDL). By introducing the
concept of a semantic descriptor, this framework unifies MDL and MTL as well as
encompassing various classic and recent MTL/MDL algorithms by interpreting them
as different ways of constructing semantic descriptors. Our interpretation
provides an alternative pipeline for zero-shot learning (ZSL), where a model
for a novel class can be constructed without training data. Moreover, it leads
to a new and practically relevant problem setting of zero-shot domain
adaptation (ZSDA), which is the analogous to ZSL but for novel domains: A model
for an unseen domain can be generated by its semantic descriptor. Experiments
across this range of problems demonstrate that our framework outperforms a
variety of alternatives.
On Gridless Sparse Methods for Line Spectral Estimation From Complete and Incomplete Data
Authors: Zai Yang, Lihua Xie
Date: 2014-07-09 00:00:00

This paper is concerned about sparse, continuous frequency estimation in line
spectral estimation, and focused on developing gridless sparse methods which
overcome grid mismatches and correspond to limiting scenarios of existing
grid-based approaches, e.g., $\ell_1$ optimization and SPICE, with an
infinitely dense grid. We generalize AST (atomic-norm soft thresholding) to the
case of nonconsecutively sampled data (incomplete data) inspired by recent
atomic norm based techniques. We present a gridless version of SPICE (gridless
SPICE, or GLS), which is applicable to both complete and incomplete data
without the knowledge of noise level. We further prove the equivalence between
GLS and atomic norm-based techniques under different assumptions of noise.
Moreover, we extend GLS to a systematic framework consisting of model order
selection and robust frequency estimation, and present feasible algorithms for
AST and GLS. Numerical simulations are provided to validate our theoretical
analysis and demonstrate performance of our methods compared to existing ones.
Effective Use of Word Order for Text Categorization with Convolutional Neural Networks
Authors: Rie Johnson, Tong Zhang
Date: 2014-12-01 00:00:00
Proceeding: HLT 2015 5
Tasks: General Classification, Image to text, Sentiment Analysis

Convolutional neural network (CNN) is a neural network that can make use of
the internal structure of data such as the 2D structure of image data. This
paper studies CNN on text categorization to exploit the 1D structure (namely,
word order) of text data for accurate prediction. Instead of using
low-dimensional word vectors as input as is often done, we directly apply CNN
to high-dimensional text data, which leads to directly learning embedding of
small text regions for use in classification. In addition to a straightforward
adaptation of CNN from image to text, a simple but new variation which employs
bag-of-word conversion in the convolution layer is proposed. An extension to
combine multiple convolution layers is also explored for higher accuracy. The
experiments demonstrate the effectiveness of our approach in comparison with
state-of-the-art methods.
Pain Intensity Estimation by a Self--Taught Selection of Histograms of Topographical Features
Authors: Corneliu Florea, Laura Florea, Raluca Boia, Alessandra Bandrabur, Constantin Vertan
Date: 2015-03-26 00:00:00
Tasks: Clustering

Pain assessment through observational pain scales is necessary for special
categories of patients such as neonates, patients with dementia, critically ill
patients, etc. The recently introduced Prkachin-Solomon score allows pain
assessment directly from facial images opening the path for multiple assistive
applications. In this paper, we introduce the Histograms of Topographical (HoT)
features, which are a generalization of the topographical primal sketch, for
the description of the face parts contributing to the mentioned score. We
propose a semi-supervised, clustering oriented self--taught learning procedure
developed on the emotion oriented Cohn-Kanade database. We use this procedure
to improve the discrimination between different pain intensity levels and the
generalization with respect to the monitored persons, while testing on the UNBC
McMaster Shoulder Pain database.
Robust Eye Centers Localization with Zero--Crossing Encoded Image Projections
Authors: Laura Florea, Corneliu Florea, Constantin Vertan
Date: 2015-03-26 00:00:00

This paper proposes a new framework for the eye centers localization by the
joint use of encoding of normalized image projections and a Multi Layer
Perceptron (MLP) classifier. The encoding is novel and it consists in
identifying the zero-crossings and extracting the relevant parameters from the
resulting modes. The compressed normalized projections produce feature
descriptors that are inputs to a properly-trained MLP, for discriminating among
various categories of image regions. The proposed framework forms a fast and
reliable system for the eye centers localization, especially in the context of
face expression analysis in unconstrained environments. We successfully test
the proposed method on a wide variety of databases including BioID,
Cohn-Kanade, Extended Yale B and Labelled Faces in the Wild (LFW) databases.
Solving Graph Coloring Problems with Abstraction and Symmetry
Authors: Michael Codish, Michael Frank, Avraham Itzhakov, Alice Miller
Date: 2014-09-18 00:00:00

This paper introduces a general methodology, based on abstraction and
symmetry, that applies to solve hard graph edge-coloring problems and
demonstrates its use to provide further evidence that the Ramsey number
$R(4,3,3)=30$. The number $R(4,3,3)$ is often presented as the unknown Ramsey
number with the best chances of being found "soon". Yet, its precise value has
remained unknown for more than 50 years. We illustrate our approach by showing
that: (1) there are precisely 78{,}892 $(3,3,3;13)$ Ramsey colorings; and (2)
if there exists a $(4,3,3;30)$ Ramsey coloring then it is (13,8,8) regular.
Specifically each node has 13 edges in the first color, 8 in the second, and 8
in the third. We conjecture that these two results will help provide a proof
that no $(4,3,3;30)$ Ramsey coloring exists implying that $R(4,3,3)=30$.
Bayesian Reconstruction of Missing Observations
Authors: Shun Kataoka, Muneki Yasuda, Kazuyuki Tanaka
Date: 2014-04-23 00:00:00

We focus on an interpolation method referred to Bayesian reconstruction in
this paper. Whereas in standard interpolation methods missing data are
interpolated deterministically, in Bayesian reconstruction, missing data are
interpolated probabilistically using a Bayesian treatment. In this paper, we
address the framework of Bayesian reconstruction and its application to the
traffic data reconstruction problem in the field of traffic engineering. In the
latter part of this paper, we describe the evaluation of the statistical
performance of our Bayesian traffic reconstruction model using a statistical
mechanical approach and clarify its statistical behavior.
Unsupervised authorship attribution
Authors: David Fifield, Torbjørn Follan, Emil Lunde
Date: 2015-03-26 00:00:00
Tasks: Authorship Attribution

We describe a technique for attributing parts of a written text to a set of
unknown authors. Nothing is assumed to be known a priori about the writing
styles of potential authors. We use multiple independent clusterings of an
input text to identify parts that are similar and dissimilar to one another. We
describe algorithms necessary to combine the multiple clusterings into a
meaningful output. We show results of the application of the technique on texts
having multiple writing styles.
An Evolutionary Algorithm for Error-Driven Learning via Reinforcement
Authors: Yanping Liu, Erik D. Reichle
Date: 2015-03-26 00:00:00

Although different learning systems are coordinated to afford complex
behavior, little is known about how this occurs. This article describes a
theoretical framework that specifies how complex behaviors that might be
thought to require error-driven learning might instead be acquired through
simple reinforcement. This framework includes specific assumptions about the
mechanisms that contribute to the evolution of (artificial) neural networks to
generate topologies that allow the networks to learn large-scale complex
problems using only information about the quality of their performance. The
practical and theoretical implications of the framework are discussed, as are
possible biological analogs of the approach.
Universal Psychometrics Tasks: difficulty, composition and decomposition
Authors: Jose Hernandez-Orallo
Date: 2015-03-26 00:00:00
Tasks: Reinforcement Learning

This note revisits the concepts of task and difficulty. The notion of
cognitive task and its use for the evaluation of intelligent systems is still
replete with issues. The view of tasks as MDP in the context of reinforcement
learning has been especially useful for the formalisation of learning tasks.
However, this alternate interaction does not accommodate well for some other
tasks that are usual in artificial intelligence and, most especially, in animal
and human evaluation. In particular, we want to have a more general account of
episodes, rewards and responses, and, most especially, the computational
complexity of the algorithm behind an agent solving a task. This is crucial for
the determination of the difficulty of a task as the (logarithm of the) number
of computational steps required to acquire an acceptable policy for the task,
which includes the exploration of policies and their verification. We introduce
a notion of asynchronous-time stochastic tasks. Based on this interpretation,
we can see what task difficulty is, what instance difficulty is (relative to a
task) and also what task compositions and decompositions are.
A note about the generalisation of the C-tests
Authors: Jose Hernandez-Orallo
Date: 2014-12-30 00:00:00

In this exploratory note we ask the question of what a measure of performance
for all tasks is like if we use a weighting of tasks based on a difficulty
function. This difficulty function depends on the complexity of the
(acceptable) solution for the task (instead of a universal distribution over
tasks or an adaptive test). The resulting aggregations and decompositions are
(now retrospectively) seen as the natural (and trivial) interactive
generalisation of the C-tests.
On learning optimized reaction diffusion processes for effective image restoration
Authors: Yunjin Chen, Wei Yu, Thomas Pock
Date: 2015-03-19 00:00:00
Proceeding: CVPR 2015 6
Tasks: Computational Efficiency, Image Restoration

For several decades, image restoration remains an active research topic in
low-level computer vision and hence new approaches are constantly emerging.
However, many recently proposed algorithms achieve state-of-the-art performance
only at the expense of very high computation time, which clearly limits their
practical relevance. In this work, we propose a simple but effective approach
with both high computational efficiency and high restoration quality. We extend
conventional nonlinear reaction diffusion models by several parametrized linear
filters as well as several parametrized influence functions. We propose to
train the parameters of the filters and the influence functions through a loss
based approach. Experiments show that our trained nonlinear reaction diffusion
models largely benefit from the training of the parameters and finally lead to
the best reported performance on common test datasets for image restoration.
Due to their structural simplicity, our trained models are highly efficient and
are also well-suited for parallel computation on GPUs.
Stable Feature Selection from Brain sMRI
Authors: Bo Xin, Lingjing Hu, Yizhou Wang, Wen Gao
Date: 2015-03-25 00:00:00
Tasks: feature selection

Neuroimage analysis usually involves learning thousands or even millions of
variables using only a limited number of samples. In this regard, sparse
models, e.g. the lasso, are applied to select the optimal features and achieve
high diagnosis accuracy. The lasso, however, usually results in independent
unstable features. Stability, a manifest of reproducibility of statistical
results subject to reasonable perturbations to data and the model, is an
important focus in statistics, especially in the analysis of high dimensional
data. In this paper, we explore a nonnegative generalized fused lasso model for
stable feature selection in the diagnosis of Alzheimer's disease. In addition
to sparsity, our model incorporates two important pathological priors: the
spatial cohesion of lesion voxels and the positive correlation between the
features and the disease labels. To optimize the model, we propose an efficient
algorithm by proving a novel link between total variation and fast network flow
algorithms via conic duality. Experiments show that the proposed nonnegative
model performs much better in exploring the intrinsic structure of data via
selecting stable features compared with other state-of-the-arts.
DJ-MC: A Reinforcement-Learning Agent for Music Playlist Recommendation
Authors: Elad Liebman, Maytal Saar-Tsechansky, Peter Stone
Date: 2014-01-09 00:00:00
Tasks: Music Recommendation, Recommendation Systems, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In recent years, there has been growing focus on the study of automated
recommender systems. Music recommendation systems serve as a prominent domain
for such works, both from an academic and a commercial perspective. A
fundamental aspect of music perception is that music is experienced in temporal
context and in sequence. In this work we present DJ-MC, a novel
reinforcement-learning framework for music recommendation that does not
recommend songs individually but rather song sequences, or playlists, based on
a model of preferences for both songs and song transitions. The model is
learned online and is uniquely adapted for each listener. To reduce exploration
time, DJ-MC exploits user feedback to initialize a model, which it subsequently
updates by reinforcement. We evaluate our framework with human participants
using both real song and playlist data. Our results indicate that DJ-MC's
ability to recommend sequences of songs provides a significant improvement over
more straightforward approaches, which do not take transitions into account.
Poisson Matrix Completion
Authors: Yang Cao, Yao Xie
Date: 2015-01-26 00:00:00
Tasks: Matrix Completion

We extend the theory of matrix completion to the case where we make Poisson
observations for a subset of entries of a low-rank matrix. We consider the
(now) usual matrix recovery formulation through maximum likelihood with proper
constraints on the matrix $M$, and establish theoretical upper and lower bounds
on the recovery error. Our bounds are nearly optimal up to a factor on the
order of $\mathcal{O}(\log(d_1 d_2))$. These bounds are obtained by adapting
the arguments used for one-bit matrix completion \cite{davenport20121}
(although these two problems are different in nature) and the adaptation
requires new techniques exploiting properties of the Poisson likelihood
function and tackling the difficulties posed by the locally sub-Gaussian
characteristic of the Poisson distribution. Our results highlight a few
important distinctions of Poisson matrix completion compared to the prior work
in matrix completion including having to impose a minimum signal-to-noise
requirement on each observed entry. We also develop an efficient iterative
algorithm and demonstrate its good performance in recovering solar flare
images.
A Survey of Classification Techniques in the Area of Big Data
Authors: Praful Koturwar, Sheetal Girase, Debajyoti Mukhopadhyay
Date: 2015-03-25 00:00:00
Tasks: Classification, General Classification

Big Data concern large-volume, growing data sets that are complex and have
multiple autonomous sources. Earlier technologies were not able to handle
storage and processing of huge data thus Big Data concept comes into existence.
This is a tedious job for users unstructured data. So, there should be some
mechanism which classify unstructured data into organized form which helps user
to easily access required data. Classification techniques over big
transactional database provide required data to the users from large datasets
more simple way. There are two main classification techniques, supervised and
unsupervised. In this paper we focused on to study of different supervised
classification techniques. Further this paper shows a advantages and
limitations.
Properties of Sparse Distributed Representations and their Application to Hierarchical Temporal Memory
Authors: Subutai Ahmad, Jeff Hawkins
Date: 2015-03-25 00:00:00

Empirical evidence demonstrates that every region of the neocortex represents
information using sparse activity patterns. This paper examines Sparse
Distributed Representations (SDRs), the primary information representation
strategy in Hierarchical Temporal Memory (HTM) systems and the neocortex. We
derive a number of properties that are core to scaling, robustness, and
generalization. We use the theory to provide practical guidelines and
illustrate the power of SDRs as the basis of HTM. Our goal is to help create a
unified mathematical and practical framework for SDRs as it relates to cortical
function.
Compressed sensing MRI using masked DCT and DFT measurements
Authors: Elma Hot, Petar Sekulić
Date: 2015-03-25 00:00:00
Tasks: compressed sensing, Compressive Sensing

This paper presents modification of the TwIST algorithm for Compressive
Sensing MRI images reconstruction. Compressive Sensing is new approach in
signal processing whose basic idea is recovering signal form small set of
available samples. The application of the Compressive Sensing in biomedical
imaging has found great importance. It allows significant lowering of the
acquisition time, and therefore, save the patient from the negative impact of
the MR apparatus. TwIST is commonly used algorithm for 2D signals
reconstruction using Compressive Sensing principle. It is based on the Total
Variation minimization. Standard version of the TwIST uses masked 2D Discrete
Fourier Transform coefficients as Compressive Sensing measurements. In this
paper, different masks and different transformation domains for coefficients
selection are tested. Certain percent of the measurements is used from the
mask, as well as small number of coefficients outside the mask. Comparative
analysis using 2D DFT and 2D DCT coefficients, with different mask shapes is
performed. The theory is proved with experimental results.
Adaptive Metric Dimensionality Reduction
Authors: Lee-Ad Gottlieb, Aryeh Kontorovich, Robert Krauthgamer
Date: 2013-02-12 00:00:00
Tasks: Dimensionality Reduction, Generalization Bounds

We study adaptive data-dependent dimensionality reduction in the context of
supervised learning in general metric spaces. Our main statistical contribution
is a generalization bound for Lipschitz functions in metric spaces that are
doubling, or nearly doubling. On the algorithmic front, we describe an analogue
of PCA for metric spaces: namely an efficient procedure that approximates the
data's intrinsic dimension, which is often much lower than the ambient
dimension. Our approach thus leverages the dual benefits of low dimensionality:
(1) more efficient algorithms, e.g., for proximity search, and (2) more
optimistic generalization bounds.
An Experiment on Using Bayesian Networks for Process Mining
Authors: Catarina Moreira
Date: 2015-03-25 00:00:00
Tasks: Clustering

Process mining is a technique that performs an automatic analysis of business
processes from a log of events with the promise of understanding how processes
are executed in an organisation.
  Several models have been proposed to address this problem, however, here we
propose a different approach to deal with uncertainty. By uncertainty, we mean
estimating the probability of some sequence of tasks occurring in a business
process, given that only a subset of tasks may be observable.
  In this sense, this work proposes a new approach to perform process mining
using Bayesian Networks. These structures can take into account the probability
of a task being present or absent in the business process. Moreover, Bayesian
Networks are able to automatically learn these probabilities through mechanisms
such as the maximum likelihood estimate and EM clustering.
  Experiments made over a Loan Application Case study suggest that Bayesian
Networks are adequate structures for process mining and enable a deep analysis
of the business process model that can be used to answer queries about that
process.
Fast and Robust Fixed-Rank Matrix Recovery
Authors: German Ros, Julio Guerrero
Date: 2015-03-10 00:00:00
Tasks: Clustering

We address the problem of efficient sparse fixed-rank (S-FR) matrix
decomposition, i.e., splitting a corrupted matrix $M$ into an uncorrupted
matrix $L$ of rank $r$ and a sparse matrix of outliers $S$. Fixed-rank
constraints are usually imposed by the physical restrictions of the system
under study. Here we propose a method to perform accurate and very efficient
S-FR decomposition that is more suitable for large-scale problems than existing
approaches. Our method is a grateful combination of geometrical and algebraical
techniques, which avoids the bottleneck caused by the Truncated SVD (TSVD).
Instead, a polar factorization is used to exploit the manifold structure of
fixed-rank problems as the product of two Stiefel and an SPD manifold, leading
to a better convergence and stability. Then, closed-form projectors help to
speed up each iteration of the method. We introduce a novel and fast projector
for the $\text{SPD}$ manifold and a proof of its validity. Further acceleration
is achieved using a Nystrom scheme. Extensive experiments with synthetic and
real data in the context of robust photometric stereo and spectral clustering
show that our proposals outperform the state of the art.