pwc-archive/papers-with-abstracts huggingface dataset:
ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly
Authors: Alex Beutel, Amr Ahmed, Alexander J. Smola
Date: 2014-12-31 00:00:00
Tasks: Clustering, Decision Making, Matrix Completion

Matrix completion and approximation are popular tools to capture a user's
preferences for recommendation and to approximate missing data. Instead of
using low-rank factorization we take a drastically different approach, based on
the simple insight that an additive model of co-clusterings allows one to
approximate matrices efficiently. This allows us to build a concise model that,
per bit of model learned, significantly beats all factorization approaches to
matrix approximation. Even more surprisingly, we find that summing over small
co-clusterings is more effective in modeling matrices than classic
co-clustering, which uses just one large partitioning of the matrix.
  Following Occam's razor principle suggests that the simple structure induced
by our model better captures the latent preferences and decision making
processes present in the real world than classic co-clustering or matrix
factorization. We provide an iterative minimization algorithm, a collapsed
Gibbs sampler, theoretical guarantees for matrix approximation, and excellent
empirical evidence for the efficacy of our approach. We achieve
state-of-the-art results on the Netflix problem with a fraction of the model
complexity.
Maximum Margin Clustering for State Decomposition of Metastable Systems
Authors: Hao Wu
Date: 2014-12-31 00:00:00
Tasks: Clustering

When studying a metastable dynamical system, a prime concern is how to
decompose the phase space into a set of metastable states. Unfortunately, the
metastable state decomposition based on simulation or experimental data is
still a challenge. The most popular and simplest approach is geometric
clustering which is developed based on the classical clustering technique.
However, the prerequisites of this approach are: (1) data are obtained from
simulations or experiments which are in global equilibrium and (2) the
coordinate system is appropriately selected. Recently, the kinetic clustering
approach based on phase space discretization and transition probability
estimation has drawn much attention due to its applicability to more general
cases, but the choice of discretization policy is a difficult task. In this
paper, a new decomposition method designated as maximum margin metastable
clustering is proposed, which converts the problem of metastable state
decomposition to a semi-supervised learning problem so that the large margin
technique can be utilized to search for the optimal decomposition without phase
space discretization. Moreover, several simulation examples are given to
illustrate the effectiveness of the proposed method.
HSI based colour image equalization using iterative nth root and nth power
Authors: Gholamreza Anbarjafari
Date: 2014-12-31 00:00:00

In this paper an equalization technique for colour images is introduced. The
method is based on nth root and nth power equalization approach but with
optimization of the mean of the image in different colour channels such as RGB
and HSI. The performance of the proposed method has been measured by the means
of peak signal to noise ratio. The proposed algorithm has been compared with
conventional histogram equalization and the visual and quantitative
experimental results are showing that the proposed method over perform the
histogram equalization.
Face recognition using color local binary pattern from mutually independent color channels
Authors: Gholamreza Anbarjafari
Date: 2014-12-31 00:00:00
Tasks: Face Detection, Face Recognition, Quantization

In this paper, a high performance face recognition system based on local
binary pattern (LBP) using the probability distribution functions (PDF) of
pixels in different mutually independent color channels which are robust to
frontal homogenous illumination and planer rotation is proposed. The
illumination of faces is enhanced by using the state-of-the-art technique which
is using discrete wavelet transform (DWT) and singular value decomposition
(SVD). After equalization, face images are segmented by use of local Successive
Mean Quantization Transform (SMQT) followed by skin color based face detection
system. Kullback-Leibler Distance (KLD) between the concatenated PDFs of a
given face obtained by LBP and the concatenated PDFs of each face in the
database is used as a metric in the recognition process. Various decision
fusion techniques have been used in order to improve the recognition rate. The
proposed system has been tested on the FERET, HP, and Bosphorus face databases.
The proposed system is compared with conventional and thestate-of-the-art
techniques. The recognition rates obtained using FVF approach for FERET
database is 99.78% compared with 79.60% and 68.80% for conventional gray scale
LBP and Principle Component Analysis (PCA) based face recognition techniques
respectively.
Detailed Derivations of Small-Variance Asymptotics for some Hierarchical Bayesian Nonparametric Models
Authors: Jonathan H. Huggins, Ardavan Saeedi, Matthew J. Johnson
Date: 2014-12-31 00:00:00

In this note we provide detailed derivations of two versions of
small-variance asymptotics for hierarchical Dirichlet process (HDP) mixture
models and the HDP hidden Markov model (HDP-HMM, a.k.a. the infinite HMM). We
include derivations for the probabilities of certain CRP and CRF partitions,
which are of more general interest.
Discriminative Clustering with Relative Constraints
Authors: Yuanli Pei, Xiaoli Z. Fern, Rómer Rosales, Teresa Vania Tjahja
Date: 2014-12-30 00:00:00
Tasks: Clustering

We study the problem of clustering with relative constraints, where each
constraint specifies relative similarities among instances. In particular, each
constraint $(x_i, x_j, x_k)$ is acquired by posing a query: is instance $x_i$
more similar to $x_j$ than to $x_k$? We consider the scenario where answers to
such queries are based on an underlying (but unknown) class concept, which we
aim to discover via clustering. Different from most existing methods that only
consider constraints derived from yes and no answers, we also incorporate don't
know responses. We introduce a Discriminative Clustering method with Relative
Constraints (DCRC) which assumes a natural probabilistic relationship between
instances, their underlying cluster memberships, and the observed constraints.
The objective is to maximize the model likelihood given the constraints, and in
the meantime enforce cluster separation and cluster balance by also making use
of the unlabeled instances. We evaluated the proposed method using constraints
generated from ground-truth class labels, and from (noisy) human judgments from
a user study. Experimental results demonstrate: 1) the usefulness of relative
constraints, in particular when don't know answers are considered; 2) the
improved performance of the proposed method over state-of-the-art methods that
utilize either relative or pairwise constraints; and 3) the robustness of our
method in the presence of noisy constraints, such as those provided by human
judgement.
Quantum Structure in Cognition and the Foundations of Human Reasoning
Authors: Diederik Aerts, Sandro Sozzo, Tomas Veloz
Date: 2014-12-30 00:00:00
Tasks: Decision Making, Logical Reasoning

Traditional cognitive science rests on a foundation of classical logic and
probability theory. This foundation has been seriously challenged by several
findings in experimental psychology on human decision making. Meanwhile, the
formalism of quantum theory has provided an efficient resource for modeling
these classically problematical situations. In this paper, we start from our
successful quantum-theoretic approach to the modeling of concept combinations
to formulate a unifying explanatory hypothesis. In it, human reasoning is the
superposition of two processes -- a conceptual reasoning, whose nature is
emergence of new conceptuality, and a logical reasoning, founded on an
algebraic calculus of the logical type. In most cognitive processes however,
the former reasoning prevails over the latter. In this perspective, the
observed deviations from classical logical reasoning should not be interpreted
as biases but, rather, as natural expressions of emergence in its deepest form.
Large scale canonical correlation analysis with iterative least squares
Authors: Yichao Lu, Dean P. Foster
Date: 2014-07-16 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: BIG-bench Machine Learning

Canonical Correlation Analysis (CCA) is a widely used statistical tool with
both well established theory and favorable performance for a wide range of
machine learning problems. However, computing CCA for huge datasets can be very
slow since it involves implementing QR decomposition or singular value
decomposition of huge matrices. In this paper we introduce L-CCA, a iterative
algorithm which can compute CCA fast on huge sparse datasets. Theory on both
the asymptotic convergence and finite time accuracy of L-CCA are established.
The experiments also show that L-CCA outperform other fast CCA approximation
schemes on two real datasets.
A multistep segmentation algorithm for vessel extraction in medical imaging
Authors: Nasser Aghazadeh, Ladan Sharafyan Cigaroudy
Date: 2014-12-30 00:00:00
Tasks: Denoising

The main contribution of this paper is to propose an iterative procedure for tubular structure segmentation of 2D images, which combines tight frame of Curvelet transforms with a SURE technique thresholding which is based on principle obtained by minimizing Stein Unbiased Risk Estimate for denoising. This proposed algorithm is mainly based on the TFA proposal presented in [1, 9], which we use eigenvectors of Hessian matrix of image for improving this iterative part in segmenting unclear and narrow vessels and filling the gap between separate pieces of detected vessels. The experimental results are presented to demonstrate the effectiveness of the proposed model.
Holistic random encoding for imaging through multimode fibers
Authors: Hwanchol Jang, Changhyeong Yoon, Euiheon Chung, Wonshik Choi, Heung-No Lee
Date: 2014-12-30 00:00:00
Tasks: Image Reconstruction

The input numerical aperture (NA) of multimode fiber (MMF) can be effectively
increased by placing turbid media at the input end of the MMF. This provides
the potential for high-resolution imaging through the MMF. While the input NA
is increased, the number of propagation modes in the MMF and hence the output
NA remains the same. This makes the image reconstruction process
underdetermined and may limit the quality of the image reconstruction. In this
paper, we aim to improve the signal to noise ratio (SNR) of the image
reconstruction in imaging through MMF. We notice that turbid media placed in
the input of the MMF transforms the incoming waves into a better format for
information transmission and information extraction. We call this
transformation as holistic random (HR) encoding of turbid media. By exploiting
the HR encoding, we make a considerable improvement on the SNR of the image
reconstruction. For efficient utilization of the HR encoding, we employ sparse
representation (SR), a relatively new signal reconstruction framework when it
is provided with a HR encoded signal. This study shows for the first time to
our knowledge the benefit of utilizing the HR encoding of turbid media for
recovery in the optically underdetermined systems where the output NA of it is
smaller than the input NA for imaging through MMF.
Consistency of spectral clustering in stochastic block models
Authors: Jing Lei, Alessandro Rinaldo
Date: 2013-12-07 00:00:00
Tasks: Clustering

We analyze the performance of spectral clustering for community extraction in
stochastic block models. We show that, under mild conditions, spectral
clustering applied to the adjacency matrix of the network can consistently
recover hidden communities even when the order of the maximum expected degree
is as small as $\log n$, with $n$ the number of nodes. This result applies to
some popular polynomial time spectral clustering algorithms and is further
extended to degree corrected stochastic block models using a spherical
$k$-median spectral clustering method. A key component of our analysis is a
combinatorial bound on the spectrum of binary random matrices, which is sharper
than the conventional matrix Bernstein inequality and may be of independent
interest.
A Bayesian encourages dropout
Authors: Shin-ichi Maeda
Date: 2014-12-22 00:00:00
Tasks: L2 Regularization

Dropout is one of the key techniques to prevent the learning from
overfitting. It is explained that dropout works as a kind of modified L2
regularization. Here, we shed light on the dropout from Bayesian standpoint.
Bayesian interpretation enables us to optimize the dropout rate, which is
beneficial for learning of weight parameters and prediction after learning. The
experiment result also encourages the optimization of the dropout.
Accurate and Conservative Estimates of MRF Log-likelihood using Reverse Annealing
Authors: Yuri Burda, Roger B. Grosse, Ruslan Salakhutdinov
Date: 2014-12-30 00:00:00

Markov random fields (MRFs) are difficult to evaluate as generative models
because computing the test log-probabilities requires the intractable partition
function. Annealed importance sampling (AIS) is widely used to estimate MRF
partition functions, and often yields quite accurate results. However, AIS is
prone to overestimate the log-likelihood with little indication that anything
is wrong. We present the Reverse AIS Estimator (RAISE), a stochastic lower
bound on the log-likelihood of an approximation to the original MRF model.
RAISE requires only the same MCMC transition operators as standard AIS.
Experimental results indicate that RAISE agrees closely with AIS
log-probability estimates for RBMs, DBMs, and DBNs, but typically errs on the
side of underestimating, rather than overestimating, the log-likelihood.
A Study of Entanglement in a Categorical Framework of Natural Language
Authors: Dimitri Kartsaklis, Mehrnoosh Sadrzadeh
Date: 2014-05-12 00:00:00

In both quantum mechanics and corpus linguistics based on vector spaces, the
notion of entanglement provides a means for the various subsystems to
communicate with each other. In this paper we examine a number of
implementations of the categorical framework of Coecke, Sadrzadeh and Clark
(2010) for natural language, from an entanglement perspective. Specifically,
our goal is to better understand in what way the level of entanglement of the
relational tensors (or the lack of it) affects the compositional structures in
practical situations. Our findings reveal that a number of proposals for verb
construction lead to almost separable tensors, a fact that considerably
simplifies the interactions between the words. We examine the ramifications of
this fact, and we show that the use of Frobenius algebras mitigates the
potential problems to a great extent. Finally, we briefly examine a machine
learning method that creates verb tensors exhibiting a sufficient level of
entanglement.
Complexity of Grammar Induction for Quantum Types
Authors: Antonin Delpeuch
Date: 2014-04-13 00:00:00
Tasks: valid

Most categorical models of meaning use a functor from the syntactic category
to the semantic category. When semantic information is available, the problem
of grammar induction can therefore be defined as finding preimages of the
semantic types under this forgetful functor, lifting the information flow from
the semantic level to a valid reduction at the syntactic level. We study the
complexity of grammar induction, and show that for a variety of type systems,
including pivotal and compact closed categories, the grammar induction problem
is NP-complete. Our approach could be extended to linguistic type systems such
as autonomous or bi-closed categories.
Disjunctive Normal Networks
Authors: Mehdi Sajjadi, Mojtaba Seyedhosseini, Tolga Tasdizen
Date: 2014-12-30 00:00:00
Tasks: General Classification, GPU

Artificial neural networks are powerful pattern classifiers; however, they
have been surpassed in accuracy by methods such as support vector machines and
random forests that are also easier to use and faster to train.
Backpropagation, which is used to train artificial neural networks, suffers
from the herd effect problem which leads to long training times and limit
classification accuracy. We use the disjunctive normal form and approximate the
boolean conjunction operations with products to construct a novel network
architecture. The proposed model can be trained by minimizing an error function
and it allows an effective and intuitive initialization which solves the
herd-effect problem associated with backpropagation. This leads to state-of-the
art classification accuracy and fast training times. In addition, our model can
be jointly optimized with convolutional features in an unified structure
leading to state-of-the-art results on computer vision problems with fast
convergence rates. A GPU implementation of LDNN with optional convolutional
features is also available
Workshop Notes of the 6th International Workshop on Acquisition, Representation and Reasoning about Context with Logic (ARCOE-Logic 2014)
Authors: Michael Fink, Martin Homola, Alessandra Mileo
Date: 2014-12-30 00:00:00
Tasks: Management

ARCOE-Logic 2014, the 6th International Workshop on Acquisition,
Representation and Reasoning about Context with Logic, was held in co-location
with the 19th International Conference on Knowledge Engineering and Knowledge
Management (EKAW 2014) on November 25, 2014 in Link\"oping, Sweden. These notes
contain the five papers which were accepted and presented at the workshop.
From Logical to Distributional Models
Authors: Anne Preller
Date: 2014-12-30 00:00:00

The paper relates two variants of semantic models for natural language,
logical functional models and compositional distributional vector space models,
by transferring the logic and reasoning from the logical to the distributional
models.
  The geometrical operations of quantum logic are reformulated as algebraic
operations on vectors. A map from functional models to vector space models
makes it possible to compare the meaning of sentences word by word.
Verifying the Steane code with Quantomatic
Authors: Ross Duncan, Maxime Lucas
Date: 2013-06-19 00:00:00

In this paper we give a partially mechanized proof of the correctness of
Steane's 7-qubit error correcting code, using the tool Quantomatic. To the best
of our knowledge, this represents the largest and most complicated verification
task yet carried out using Quantomatic.
Understanding and Designing Complex Systems: Response to "A framework for optimal high-level descriptions in science and engineering---preliminary report"
Authors: James P. Crutchfield, Ryan G. James, Sarah Marzen, Dowman P. Varn
Date: 2014-12-30 00:00:00

We recount recent history behind building compact models of nonlinear,
complex processes and identifying their relevant macroscopic patterns or
"macrostates". We give a synopsis of computational mechanics, predictive
rate-distortion theory, and the role of information measures in monitoring
model complexity and predictive performance. Computational mechanics provides a
method to extract the optimal minimal predictive model for a given process.
Rate-distortion theory provides methods for systematically approximating such
models. We end by commenting on future prospects for developing a general
framework that automatically discovers optimal compact models. As a response to
the manuscript cited in the title above, this brief commentary corrects
potentially misleading claims about its state space compression method and
places it in a broader historical setting.
Probing the topological properties of complex networks modeling short written texts
Authors: Diego R. Amancio
Date: 2014-12-29 00:00:00

In recent years, graph theory has been widely employed to probe several
language properties. More specifically, the so-called word adjacency model has
been proven useful for tackling several practical problems, especially those
relying on textual stylistic analysis. The most common approach to treat texts
as networks has simply considered either large pieces of texts or entire books.
This approach has certainly worked well -- many informative discoveries have
been made this way -- but it raises an uncomfortable question: could there be
important topological patterns in small pieces of texts? To address this
problem, the topological properties of subtexts sampled from entire books was
probed. Statistical analyzes performed on a dataset comprising 50 novels
revealed that most of the traditional topological measurements are stable for
short subtexts. When the performance of the authorship recognition task was
analyzed, it was found that a proper sampling yields a discriminability similar
to the one found with full texts. Surprisingly, the support vector machine
classification based on the characterization of short texts outperformed the
one performed with entire books. These findings suggest that a local
topological analysis of large documents might improve its global
characterization. Most importantly, it was verified, as a proof of principle,
that short texts can be analyzed with the methods and concepts of complex
networks. As a consequence, the techniques described here can be extended in a
straightforward fashion to analyze texts as time-varying complex networks.
Accurate Localization in Dense Urban Area Using Google Street View Image
Authors: Mahdi Salarian
Date: 2014-12-29 00:00:00
Tasks: Image Retrieval, Position, Retrieval

Accurate information about the location and orientation of a camera in mobile
devices is central to the utilization of location-based services (LBS). Most of
such mobile devices rely on GPS data but this data is subject to inaccuracy due
to imperfections in the quality of the signal provided by satellites. This
shortcoming has spurred the research into improving the accuracy of
localization. Since mobile devices have camera, a major thrust of this research
has been seeks to acquire the local scene and apply image retrieval techniques
by querying a GPS-tagged image database to find the best match for the acquired
scene.. The techniques are however computationally demanding and unsuitable for
real-time applications such as assistive technology for navigation by the blind
and visually impaired which motivated out work. To overcome the high complexity
of those techniques, we investigated the use of inertial sensors as an aid in
image-retrieval-based approach. Armed with information of media other than
images, such as data from the GPS module along with orientation sensors such as
accelerometer and gyro, we sought to limit the size of the image set to c
search for the best match. Specifically, data from the orientation sensors
along with Dilution of precision (DOP) from GPS are used to find the angle of
view and estimation of position. We present analysis of the reduction in the
image set size for the search as well as simulations to demonstrate the
effectiveness in a fast implementation with 98% Estimated Position Error.
An ADMM algorithm for solving a proximal bound-constrained quadratic program
Authors: Miguel Á. Carreira-Perpiñán
Date: 2014-12-29 00:00:00

We consider a proximal operator given by a quadratic function subject to
bound constraints and give an optimization algorithm using the alternating
direction method of multipliers (ADMM). The algorithm is particularly efficient
to solve a collection of proximal operators that share the same quadratic form,
or if the quadratic program is the relaxation of a binary quadratic problem.