pwc-archive/papers-with-abstracts huggingface dataset:
Enhancing Energy Minimization Framework for Scene Text Recognition with Top-Down Cues
Authors: Anand Mishra, Karteek Alahari, C. V. Jawahar
Date: 2016-01-13 00:00:00
Tasks: Scene Text Recognition

Recognizing scene text is a challenging problem, even more so than the
recognition of scanned documents. This problem has gained significant attention
from the computer vision community in recent years, and several methods based
on energy minimization frameworks and deep learning approaches have been
proposed. In this work, we focus on the energy minimization framework and
propose a model that exploits both bottom-up and top-down cues for recognizing
cropped words extracted from street images. The bottom-up cues are derived from
individual character detections from an image. We build a conditional random
field model on these detections to jointly model the strength of the detections
and the interactions between them. These interactions are top-down cues
obtained from a lexicon-based prior, i.e., language statistics. The optimal
word represented by the text image is obtained by minimizing the energy
function corresponding to the random field model. We evaluate our proposed
algorithm extensively on a number of cropped scene text benchmark datasets,
namely Street View Text, ICDAR 2003, 2011 and 2013 datasets, and IIIT 5K-word,
and show better performance than comparable methods. We perform a rigorous
analysis of all the steps in our approach and analyze the results. We also show
that state-of-the-art convolutional neural network features can be integrated
in our framework to further improve the recognition performance.
Online Prediction of Dyadic Data with Heterogeneous Matrix Factorization
Authors: Guangyong Chen, Fengyuan Zhu, Pheng Ann Heng
Date: 2016-01-13 00:00:00
Tasks: Collaborative Filtering, Variational Inference

Dyadic Data Prediction (DDP) is an important problem in many research areas.
This paper develops a novel fully Bayesian nonparametric framework which
integrates two popular and complementary approaches, discrete mixed membership
modeling and continuous latent factor modeling into a unified Heterogeneous
Matrix Factorization~(HeMF) model, which can predict the unobserved dyadics
accurately. The HeMF can determine the number of communities automatically and
exploit the latent linear structure for each bicluster efficiently. We propose
a Variational Bayesian method to estimate the parameters and missing data. We
further develop a novel online learning approach for Variational inference and
use it for the online learning of HeMF, which can efficiently cope with the
important large-scale DDP problem. We evaluate the performance of our method on
the EachMoive, MovieLens and Netflix Prize collaborative filtering datasets.
The experiment shows that, our model outperforms state-of-the-art methods on
all benchmarks. Compared with Stochastic Gradient Method (SGD), our online
learning approach achieves significant improvement on the estimation accuracy
and robustness.
Blind Image Denoising via Dependent Dirichlet Process Tree
Authors: Fengyuan Zhu, Guangyong Chen, Jianye Hao, Pheng-Ann Heng
Date: 2016-01-13 00:00:00
Tasks: Denoising, Image Denoising, Variational Inference

Most existing image denoising approaches assumed the noise to be homogeneous
white Gaussian distributed with known intensity. However, in real noisy images,
the noise models are usually unknown beforehand and can be much more complex.
This paper addresses this problem and proposes a novel blind image denoising
algorithm to recover the clean image from noisy one with the unknown noise
model. To model the empirical noise of an image, our method introduces the
mixture of Gaussian distribution, which is flexible enough to approximate
different continuous distributions. The problem of blind image denoising is
reformulated as a learning problem. The procedure is to first build a two-layer
structural model for noisy patches and consider the clean ones as latent
variable. To control the complexity of the noisy patch model, this work
proposes a novel Bayesian nonparametric prior called "Dependent Dirichlet
Process Tree" to build the model. Then, this study derives a variational
inference algorithm to estimate model parameters and recover clean patches. We
apply our method on synthesis and real noisy images with different noise
models. Comparing with previous approaches, ours achieves better performance.
The experimental results indicate the efficiency of the proposed algorithm to
cope with practical image denoising tasks.
Provable Tensor Methods for Learning Mixtures of Generalized Linear Models
Authors: Hanie Sedghi, Majid Janzamin, Anima Anandkumar
Date: 2014-12-09 00:00:00
Tasks: General Classification, Tensor Decomposition

We consider the problem of learning mixtures of generalized linear models
(GLM) which arise in classification and regression problems. Typical learning
approaches such as expectation maximization (EM) or variational Bayes can get
stuck in spurious local optima. In contrast, we present a tensor decomposition
method which is guaranteed to correctly recover the parameters. The key insight
is to employ certain feature transformations of the input, which depend on the
input generative model. Specifically, we employ score function tensors of the
input and compute their cross-correlation with the response variable. We
establish that the decomposition of this tensor consistently recovers the
parameters, under mild non-degeneracy conditions. We demonstrate that the
computational and sample complexity of our method is a low order polynomial of
the input and the latent dimensions.
A note on the sample complexity of the Er-SpUD algorithm by Spielman, Wang and Wright for exact recovery of sparsely used dictionaries
Authors: Radosław Adamczak
Date: 2016-01-08 00:00:00

We consider the problem of recovering an invertible $n \times n$ matrix $A$
and a sparse $n \times p$ random matrix $X$ based on the observation of $Y =
AX$ (up to a scaling and permutation of columns of $A$ and rows of $X$). Using
only elementary tools from the theory of empirical processes we show that a
version of the Er-SpUD algorithm by Spielman, Wang and Wright with high
probability recovers $A$ and $X$ exactly, provided that $p \ge Cn\log n$, which
is optimal up to the constant $C$.
Creativity in Machine Learning
Authors: Martin Thoma
Date: 2016-01-12 00:00:00
Tasks: BIG-bench Machine Learning

Recent machine learning techniques can be modified to produce creative
results. Those results did not exist before; it is not a trivial combination of
the data which was fed into the machine learning system. The obtained results
come in multiple forms: As images, as text and as audio.
  This paper gives a high level overview of how they are created and gives some
examples. It is meant to be a summary of the current work and give people who
are new to machine learning some starting points.
Infomax strategies for an optimal balance between exploration and exploitation
Authors: Gautam Reddy, Antonio Celani, Massimo Vergassola
Date: 2016-01-12 00:00:00

Proper balance between exploitation and exploration is what makes good
decisions, which achieve high rewards like payoff or evolutionary fitness. The
Infomax principle postulates that maximization of information directs the
function of diverse systems, from living systems to artificial neural networks.
While specific applications are successful, the validity of information as a
proxy for reward remains unclear. Here, we consider the multi-armed bandit
decision problem, which features arms (slot-machines) of unknown probabilities
of success and a player trying to maximize cumulative payoff by choosing the
sequence of arms to play. We show that an Infomax strategy (Info-p) which
optimally gathers information on the highest mean reward among the arms
saturates known optimal bounds and compares favorably to existing policies. The
highest mean reward considered by Info-p is not the quantity actually needed
for the choice of the arm to play, yet it allows for optimal tradeoffs between
exploration and exploitation.
Stochastic Gradient Made Stable: A Manifold Propagation Approach for Large-Scale Optimization
Authors: Yadong Mu, Wei Liu, Wei Fan
Date: 2015-06-28 00:00:00

Stochastic gradient descent (SGD) holds as a classical method to build large
scale machine learning models over big data. A stochastic gradient is typically
calculated from a limited number of samples (known as mini-batch), so it
potentially incurs a high variance and causes the estimated parameters bounce
around the optimal solution. To improve the stability of stochastic gradient,
recent years have witnessed the proposal of several semi-stochastic gradient
descent algorithms, which distinguish themselves from standard SGD by
incorporating global information into gradient computation. In this paper we
contribute a novel stratified semi-stochastic gradient descent (S3GD) algorithm
to this nascent research area, accelerating the optimization of a large family
of composite convex functions. Though theoretically converging faster, prior
semi-stochastic algorithms are found to suffer from high iteration complexity,
which makes them even slower than SGD in practice on many datasets. In our
proposed S3GD, the semi-stochastic gradient is calculated based on efficient
manifold propagation, which can be numerically accomplished by sparse matrix
multiplications. This way S3GD is able to generate a highly-accurate estimate
of the exact gradient from each mini-batch with largely-reduced computational
complexity. Theoretic analysis reveals that the proposed S3GD elegantly
balances the geometric algorithmic convergence rate against the space and time
complexities during the optimization. The efficacy of S3GD is also
experimentally corroborated on several large-scale benchmark datasets.
Indicators of Good Student Performance in Moodle Activity Data
Authors: Ewa Młynarska, Derek Greene, Pádraig Cunningham
Date: 2016-01-12 00:00:00

In this paper we conduct an analysis of Moodle activity data focused on
identifying early predictors of good student performance. The analysis shows
that three relevant hypotheses are largely supported by the data. These
hypotheses are: early submission is a good sign, a high level of activity is
predictive of good results and evening activity is even better than daytime
activity. We highlight some pathological examples where high levels of activity
correlates with bad results.
Deep Neural Networks predict Hierarchical Spatio-temporal Cortical Dynamics of Human Visual Object Recognition
Authors: Radoslaw M. Cichy, Aditya Khosla, Dimitrios Pantazis, Antonio Torralba, Aude Oliva
Date: 2016-01-12 00:00:00
Tasks: Object Recognition

The complex multi-stage architecture of cortical visual pathways provides the
neural basis for efficient visual object recognition in humans. However, the
stage-wise computations therein remain poorly understood. Here, we compared
temporal (magnetoencephalography) and spatial (functional MRI) visual brain
representations with representations in an artificial deep neural network (DNN)
tuned to the statistics of real-world visual recognition. We showed that the
DNN captured the stages of human visual processing in both time and space from
early visual areas towards the dorsal and ventral streams. Further
investigation of crucial DNN parameters revealed that while model architecture
was important, training on real-world categorization was necessary to enforce
spatio-temporal hierarchical relationships with the brain. Together our results
provide an algorithmically informed view on the spatio-temporal dynamics of
visual object recognition in the human visual brain.
Learning Subclass Representations for Visually-varied Image Classification
Authors: Xinchao Li, Peng Xu, Yue Shi, Martha Larson, Alan Hanjalic
Date: 2016-01-12 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, TAG

In this paper, we present a subclass-representation approach that predicts
the probability of a social image belonging to one particular class. We explore
the co-occurrence of user-contributed tags to find subclasses with a strong
connection to the top level class. We then project each image on to the
resulting subclass space to generate a subclass representation for the image.
The novelty of the approach is that subclass representations make use of not
only the content of the photos themselves, but also information on the
co-occurrence of their tags, which determines membership in both subclasses and
top-level classes. The novelty is also that the images are classified into
smaller classes, which have a chance of being more visually stable and easier
to model. These subclasses are used as a latent space and images are
represented in this space by their probability of relatedness to all of the
subclasses. In contrast to approaches directly modeling each top-level class
based on the image content, the proposed method can exploit more information
for visually diverse classes. The approach is evaluated on a set of $2$ million
photos with 10 classes, released by the Multimedia 2013 Yahoo! Large-scale
Flickr-tag Image Classification Grand Challenge. Experiments show that the
proposed system delivers sound performance for visually diverse classes
compared with methods that directly model top classes.
Mapping-equivalence and oid-equivalence of single-function object-creating conjunctive queries
Authors: Angela Bonifati, Werner Nutt, Riccardo Torlone, Jan Van den Bussche
Date: 2015-03-05 00:00:00
Tasks: Data Integration, Object

Conjunctive database queries have been extended with a mechanism for object
creation to capture important applications such as data exchange, data
integration, and ontology-based data access. Object creation generates new
object identifiers in the result, that do not belong to the set of constants in
the source database. The new object identifiers can be also seen as Skolem
terms. Hence, object-creating conjunctive queries can also be regarded as
restricted second-order tuple-generating dependencies (SO tgds), considered in
the data exchange literature.
  In this paper, we focus on the class of single-function object-creating
conjunctive queries, or sifo CQs for short. We give a new characterization for
oid-equivalence of sifo CQs that is simpler than the one given by Hull and
Yoshikawa and places the problem in the complexity class NP. Our
characterization is based on Cohen's equivalence notions for conjunctive
queries with multiplicities. We also solve the logical entailment problem for
sifo CQs, showing that also this problem belongs to NP. Results by Pichler et
al. have shown that logical equivalence for more general classes of SO tgds is
either undecidable or decidable with as yet unknown complexity upper bounds.
Essence' Description
Authors: Peter Nightingale, Andrea Rendl
Date: 2016-01-12 00:00:00

A description of the Essence' language as used by the tool Savile Row.
Human Attention Estimation for Natural Images: An Automatic Gaze Refinement Approach
Authors: Jinsoo Choi, Tae-Hyun Oh, In So Kweon
Date: 2016-01-12 00:00:00
Tasks: Gaze Estimation

Photo collections and its applications today attempt to reflect user
interactions in various forms. Moreover, photo collections aim to capture the
users' intention with minimum effort through applications capturing user
intentions. Human interest regions in an image carry powerful information about
the user's behavior and can be used in many photo applications. Research on
human visual attention has been conducted in the form of gaze tracking and
computational saliency models in the computer vision community, and has shown
considerable progress. This paper presents an integration between implicit gaze
estimation and computational saliency model to effectively estimate human
attention regions in images on the fly. Furthermore, our method estimates human
attention via implicit calibration and incremental model updating without any
active participation from the user. We also present extensive analysis and
possible applications for personal photo collections.
Weightless neural network parameters and architecture selection in a quantum computer
Authors: Adenilton J. da Silva, Wilson R. de Oliveira, Teresa B. Ludermir
Date: 2016-01-12 00:00:00

Training artificial neural networks requires a tedious empirical evaluation
to determine a suitable neural network architecture. To avoid this empirical
process several techniques have been proposed to automatise the architecture
selection process. In this paper, we propose a method to perform parameter and
architecture selection for a quantum weightless neural network (qWNN). The
architecture selection is performed through the learning procedure of a qWNN
with a learning algorithm that uses the principle of quantum superposition and
a non-linear quantum operator. The main advantage of the proposed method is
that it performs a global search in the space of qWNN architecture and
parameters rather than a local search.
Learning the Correction for Multi-Path Deviations in Time-of-Flight Cameras
Authors: Mojmir Mutny, Rahul Nair, Jens-Malte Gottfried
Date: 2015-12-13 00:00:00

The Multipath effect in Time-of-Flight(ToF) cameras still remains to be a
challenging problem that hinders further processing of 3D data information.
Based on the evidence from previous literature, we explored the possibility of
using machine learning techniques to correct this effect. Firstly, we created
two new datasets of of ToF images rendered via ToF simulator of LuxRender.
These two datasets contain corners in multiple orientations and with different
material properties. We chose scenes with corners as multipath effects are most
pronounced in corners. Secondly, we used this dataset to construct a learning
model to predict real valued corrections to the ToF data using Random Forests.
We found out that in our smaller dataset we were able to predict real valued
correction and improve the quality of depth images significantly by removing
multipath bias. With our algorithm, we improved relative per-pixel error from
average value of 19% to 3%. Additionally, variance of the error was lowered by
an order of magnitude.
Comparison and Adaptation of Automatic Evaluation Metrics for Quality Assessment of Re-Speaking
Authors: Krzysztof Wołk, Danijel Koržinek
Date: 2016-01-12 00:00:00
Tasks: Machine Translation, NER, Translation

Re-speaking is a mechanism for obtaining high quality subtitles for use in
live broadcast and other public events. Because it relies on humans performing
the actual re-speaking, the task of estimating the quality of the results is
non-trivial. Most organisations rely on humans to perform the actual quality
assessment, but purely automatic methods have been developed for other similar
problems, like Machine Translation. This paper will try to compare several of
these methods: BLEU, EBLEU, NIST, METEOR, METEOR-PL, TER and RIBES. These will
then be matched to the human-derived NER metric, commonly used in re-speaking.
Lock in Feedback in Sequential Experiments
Authors: Maurits Kaptein, Davide Iannuzzi
Date: 2015-02-02 00:00:00

We often encounter situations in which an experimenter wants to find, by
sequential experimentation, $x_{max} = \arg\max_{x} f(x)$, where $f(x)$ is a
(possibly unknown) function of a well controllable variable $x$. Taking
inspiration from physics and engineering, we have designed a new method to
address this problem. In this paper, we first introduce the method in
continuous time, and then present two algorithms for use in sequential
experiments. Through a series of simulation studies, we show that the method is
effective for finding maxima of unknown functions by experimentation, even when
the maximum of the functions drifts or when the signal to noise ratio is low.
Robust Visual Tracking via Inverse Nonnegative Matrix Factorization
Authors: Fanghui Liu, Tao Zhou, Keren Fu, Irene Y. H. Gu, Jie Yang
Date: 2015-09-20 00:00:00
Tasks: Visual Tracking

The establishment of robust target appearance model over time is an
overriding concern in visual tracking. In this paper, we propose an inverse
nonnegative matrix factorization (NMF) method for robust appearance modeling.
Rather than using a linear combination of nonnegative basis matrices for each
target image patch in the conventional NMF, the proposed method is a reverse
thought to conventional NMF tracker. It utilizes both the foreground and
background information, and imposes a local coordinate constraint, where the
basis matrix is sparse matrix from the linear combination of candidates with
corresponding nonnegative coefficient vectors. Inverse NMF is used as a feature
encoder, where the resulting coefficient vectors are fed into a SVM classifier
for separating the target from the background. The proposed method is tested on
several videos and compared with seven state-of-the-art methods. Our results
have provided further support to the effectiveness and robustness of the
proposed method.
Robust Lineage Reconstruction from High-Dimensional Single-Cell Data
Authors: Gregory Giecold, Eugenio Marco, Lorenzo Trippa, Guo-Cheng Yuan
Date: 2016-01-12 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Single-cell gene expression data provide invaluable resources for systematic
characterization of cellular hierarchy in multi-cellular organisms. However,
cell lineage reconstruction is still often associated with significant
uncertainty due to technological constraints. Such uncertainties have not been
taken into account in current methods. We present ECLAIR, a novel computational
method for the statistical inference of cell lineage relationships from
single-cell gene expression data. ECLAIR uses an ensemble approach to improve
the robustness of lineage predictions, and provides a quantitative estimate of
the uncertainty of lineage branchings. We show that the application of ECLAIR
to published datasets successfully reconstructs known lineage relationships and
significantly improves the robustness of predictions. In conclusion, ECLAIR is
a powerful bioinformatics tool for single-cell data analysis. It can be used
for robust lineage reconstruction with quantitative estimate of prediction
accuracy.
Basic Reasoning with Tensor Product Representations
Authors: Paul Smolensky, Moontae Lee, Xiaodong He, Wen-tau Yih, Jianfeng Gao, Li Deng
Date: 2016-01-12 00:00:00
Tasks: Question Answering

In this paper we present the initial development of a general theory for
mapping inference in predicate logic to computation over Tensor Product
Representations (TPRs; Smolensky (1990), Smolensky & Legendre (2006)). After an
initial brief synopsis of TPRs (Section 0), we begin with particular examples
of inference with TPRs in the 'bAbI' question-answering task of Weston et al.
(2015) (Section 1). We then present a simplification of the general analysis
that suffices for the bAbI task (Section 2). Finally, we lay out the general
treatment of inference over TPRs (Section 3). We also show the simplification
in Section 2 derives the inference methods described in Lee et al. (2016); this
shows how the simple methods of Lee et al. (2016) can be formally extended to
more general reasoning tasks.