pwc-archive/papers-with-abstracts huggingface dataset:
QUOTUS: The Structure of Political Media Coverage as Revealed by Quoting Patterns
Authors: Vlad Niculae, Caroline Suen, Justine Zhang, Cristian Danescu-Niculescu-Mizil, Jure Leskovec
Date: 2015-04-06 00:00:00
Tasks: Articles

Given the extremely large pool of events and stories available, media outlets
need to focus on a subset of issues and aspects to convey to their audience.
Outlets are often accused of exhibiting a systematic bias in this selection
process, with different outlets portraying different versions of reality.
However, in the absence of objective measures and empirical evidence, the
direction and extent of systematicity remains widely disputed.
  In this paper we propose a framework based on quoting patterns for
quantifying and characterizing the degree to which media outlets exhibit
systematic bias. We apply this framework to a massive dataset of news articles
spanning the six years of Obama's presidency and all of his speeches, and
reveal that a systematic pattern does indeed emerge from the outlet's quoting
behavior. Moreover, we show that this pattern can be successfully exploited in
an unsupervised prediction setting, to determine which new quotes an outlet
will select to broadcast. By encoding bias patterns in a low-rank space we
provide an analysis of the structure of political media coverage. This reveals
a latent media bias space that aligns surprisingly well with political ideology
and outlet type. A linguistic analysis exposes striking differences across
these latent dimensions, showing how the different types of media outlets
portray different realities even when reporting on the same events. For
example, outlets mapped to the mainstream conservative side of the latent space
focus on quotes that portray a presidential persona disproportionately
characterized by negativity.
PASSCoDe: Parallel ASynchronous Stochastic dual Co-ordinate Descent
Authors: Cho-Jui Hsieh, Hsiang-Fu Yu, Inderjit S. Dhillon
Date: 2015-04-06 00:00:00

Stochastic Dual Coordinate Descent (SDCD) has become one of the most
efficient ways to solve the family of $\ell_2$-regularized empirical risk
minimization problems, including linear SVM, logistic regression, and many
others. The vanilla implementation of DCD is quite slow; however, by
maintaining primal variables while updating dual variables, the time complexity
of SDCD can be significantly reduced. Such a strategy forms the core algorithm
in the widely-used LIBLINEAR package. In this paper, we parallelize the SDCD
algorithms in LIBLINEAR. In recent research, several synchronized parallel SDCD
algorithms have been proposed, however, they fail to achieve good speedup in
the shared memory multi-core setting. In this paper, we propose a family of
asynchronous stochastic dual coordinate descent algorithms (ASDCD). Each thread
repeatedly selects a random dual variable and conducts coordinate updates using
the primal variables that are stored in the shared memory. We analyze the
convergence properties when different locking/atomic mechanisms are applied.
For implementation with atomic operations, we show linear convergence under
mild conditions. For implementation without any atomic operations or locking,
we present the first {\it backward error analysis} for ASDCD under the
multi-core environment, showing that the converged solution is the exact
solution for a primal problem with perturbed regularizer. Experimental results
show that our methods are much faster than previous parallel coordinate descent
solvers.
Early Stopping is Nonparametric Variational Inference
Authors: Dougal Maclaurin, David Duvenaud, Ryan P. Adams
Date: 2015-04-06 00:00:00
Tasks: Variational Inference

We show that unconverged stochastic gradient descent can be interpreted as a
procedure that samples from a nonparametric variational approximate posterior
distribution. This distribution is implicitly defined as the transformation of
an initial distribution by a sequence of optimization updates. By tracking the
change in entropy over this sequence of transformations during optimization, we
form a scalable, unbiased estimate of the variational lower bound on the log
marginal likelihood. We can use this bound to optimize hyperparameters instead
of using cross-validation. This Bayesian interpretation of SGD suggests
improved, overfitting-resistant optimization procedures, and gives a
theoretical foundation for popular tricks such as early stopping and
ensembling. We investigate the properties of this marginal likelihood estimator
on neural network models.
Hyperparameter Search in Machine Learning
Authors: Marc Claesen, Bart De Moor
Date: 2015-02-07 00:00:00
Tasks: BIG-bench Machine Learning

We introduce the hyperparameter search problem in the field of machine
learning and discuss its main challenges from an optimization perspective.
Machine learning methods attempt to build models that capture some element of
interest based on given data. Most common learning algorithms feature a set of
hyperparameters that must be determined before training commences. The choice
of hyperparameters can significantly affect the resulting model's performance,
but determining good values can be complex; hence a disciplined, theoretically
sound search strategy is essential.
Matching-CNN Meets KNN: Quasi-Parametric Human Parsing
Authors: Si Liu, Xiaodan Liang, Luoqi Liu, Xiaohui Shen, Jianchao Yang, Changsheng Xu, Liang Lin, Xiaochun Cao, Shuicheng Yan
Date: 2015-04-06 00:00:00
Proceeding: CVPR 2015 6
Tasks: Human Parsing

Both parametric and non-parametric approaches have demonstrated encouraging
performances in the human parsing task, namely segmenting a human image into
several semantic regions (e.g., hat, bag, left arm, face). In this work, we aim
to develop a new solution with the advantages of both methodologies, namely
supervision from annotated data and the flexibility to use newly annotated
(possibly uncommon) images, and present a quasi-parametric human parsing model.
Under the classic K Nearest Neighbor (KNN)-based nonparametric framework, the
parametric Matching Convolutional Neural Network (M-CNN) is proposed to predict
the matching confidence and displacements of the best matched region in the
testing image for a particular semantic region in one KNN image. Given a
testing image, we first retrieve its KNN images from the
annotated/manually-parsed human image corpus. Then each semantic region in each
KNN image is matched with confidence to the testing image using M-CNN, and the
matched regions from all KNN images are further fused, followed by a superpixel
smoothing procedure to obtain the ultimate human parsing result. The M-CNN
differs from the classic CNN in that the tailored cross image matching filters
are introduced to characterize the matching between the testing image and the
semantic region of a KNN image. The cross image matching filters are defined at
different convolutional layers, each aiming to capture a particular range of
displacements. Comprehensive evaluations over a large dataset with 7,700
annotated human images well demonstrate the significant performance gain from
the quasi-parametric model over the state-of-the-arts, for the human parsing
task.
Bengali to Assamese Statistical Machine Translation using Moses (Corpus Based)
Authors: Nayan Jyoti Kalita, Baharul Islam
Date: 2015-04-06 00:00:00
Tasks: Decoder, Language Modeling, Language Modelling, Machine Translation, Translation

Machine dialect interpretation assumes a real part in encouraging man-machine
correspondence and in addition men-men correspondence in Natural Language
Processing (NLP). Machine Translation (MT) alludes to utilizing machine to
change one dialect to an alternate. Statistical Machine Translation is a type
of MT consisting of Language Model (LM), Translation Model (TM) and decoder. In
this paper, Bengali to Assamese Statistical Machine Translation Model has been
created by utilizing Moses. Other translation tools like IRSTLM for Language
Model and GIZA-PP-V1.0.7 for Translation model are utilized within this
framework which is accessible in Linux situations. The purpose of the LM is to
encourage fluent output and the purpose of TM is to encourage similarity
between input and output, the decoder increases the probability of translated
text in target language. A parallel corpus of 17100 sentences in Bengali and
Assamese has been utilized for preparing within this framework. Measurable MT
procedures have not so far been generally investigated for Indian dialects. It
might be intriguing to discover to what degree these models can help the
immense continuous MT deliberations in the nation.
Dual Decomposition from the Perspective of Relax, Compensate and then Recover
Authors: Arthur Choi, Adnan Darwiche
Date: 2015-04-05 00:00:00

Relax, Compensate and then Recover (RCR) is a paradigm for approximate
inference in probabilistic graphical models that has previously provided
theoretical and practical insights on iterative belief propagation and some of
its generalizations. In this paper, we characterize the technique of dual
decomposition in the terms of RCR, viewing it as a specific way to compensate
for relaxed equivalence constraints. Among other insights gathered from this
perspective, we propose novel heuristics for recovering relaxed equivalence
constraints with the goal of incrementally tightening dual decomposition
approximations, all the way to reaching exact solutions. We also show
empirically that recovering equivalence constraints can sometimes tighten the
corresponding approximation (and obtaining exact results), without increasing
much the complexity of inference.
Efficient Dictionary Learning via Very Sparse Random Projections
Authors: Farhad Pourkamali-Anaraki, Stephen Becker, Shannon M. Hughes
Date: 2015-04-05 00:00:00
Tasks: Clustering, Dictionary Learning

Performing signal processing tasks on compressive measurements of data has
received great attention in recent years. In this paper, we extend previous
work on compressive dictionary learning by showing that more general random
projections may be used, including sparse ones. More precisely, we examine
compressive K-means clustering as a special case of compressive dictionary
learning and give theoretical guarantees for its performance for a very general
class of random projections. We then propose a memory and computation efficient
dictionary learning algorithm, specifically designed for analyzing large
volumes of high-dimensional data, which learns the dictionary from very sparse
random projections. Experimental results demonstrate that our approach allows
for reduction of computational complexity and memory/data access, with
controllable loss in accuracy.
Heuristic algorithms for obtaining Polynomial Threshold Functions with low densities
Authors: Can Eren Sezener, Erhan Oztop
Date: 2015-04-05 00:00:00

In this paper we present several heuristic algorithms, including a Genetic
Algorithm (GA), for obtaining polynomial threshold function (PTF)
representations of Boolean functions (BFs) with small number of monomials. We
compare these among each other and against the algorithm of Oztop via
computational experiments. The results indicate that our heuristic algorithms
find more parsimonious representations compared to the those of non-heuristic
and GA-based algorithms.
Ultra-large alignments using Phylogeny-aware Profiles
Authors: Nam-phuong Nguyen, Siavash Mirarab, Keerthana Kumar, Tandy Warnow
Date: 2015-04-05 00:00:00
Tasks: BIG-bench Machine Learning, Multiple Sequence Alignment

Many biological questions, including the estimation of deep evolutionary
histories and the detection of remote homology between protein sequences, rely
upon multiple sequence alignments (MSAs) and phylogenetic trees of large
datasets. However, accurate large-scale multiple sequence alignment is very
difficult, especially when the dataset contains fragmentary sequences. We
present UPP, an MSA method that uses a new machine learning technique - the
Ensemble of Hidden Markov Models - that we propose here. UPP produces highly
accurate alignments for both nucleotide and amino acid sequences, even on
ultra-large datasets or datasets containing fragmentary sequences. UPP is
available at https://github.com/smirarab/sepp.
EM-Based Channel Estimation from Crowd-Sourced RSSI Samples Corrupted by Noise and Interference
Authors: Silvija Kokalj-Filipovic, Larry Greenstein
Date: 2015-04-05 00:00:00

We propose a method for estimating channel parameters from RSSI measurements
and the lost packet count, which can work in the presence of losses due to both
interference and signal attenuation below the noise floor. This is especially
important in the wireless networks, such as vehicular, where propagation model
changes with the density of nodes. The method is based on Stochastic
Expectation Maximization, where the received data is modeled as a mixture of
distributions (no/low interference and strong interference), incomplete
(censored) due to packet losses. The PDFs in the mixture are Gamma, according
to the commonly accepted model for wireless signal and interference power. This
approach leverages the loss count as additional information, hence
outperforming maximum likelihood estimation, which does not use this
information (ML-), for a small number of received RSSI samples. Hence, it
allows inexpensive on-line channel estimation from ad-hoc collected data. The
method also outperforms ML- on uncensored data mixtures, as ML- assumes that
samples are from a single-mode PDF.
Sync-Rank: Robust Ranking, Constrained Ranking and Rank Aggregation via Eigenvector and Semidefinite Programming Synchronization
Authors: Mihai Cucuringu
Date: 2015-04-05 00:00:00

We consider the classic problem of establishing a statistical ranking of a
set of n items given a set of inconsistent and incomplete pairwise comparisons
between such items. Instantiations of this problem occur in numerous
applications in data analysis (e.g., ranking teams in sports data), computer
vision, and machine learning. We formulate the above problem of ranking with
incomplete noisy information as an instance of the group synchronization
problem over the group SO(2) of planar rotations, whose usefulness has been
demonstrated in numerous applications in recent years. Its least squares
solution can be approximated by either a spectral or a semidefinite programming
(SDP) relaxation, followed by a rounding procedure. We perform extensive
numerical simulations on both synthetic and real-world data sets, showing that
our proposed method compares favorably to other algorithms from the recent
literature. Existing theoretical guarantees on the group synchronization
problem imply lower bounds on the largest amount of noise permissible in the
ranking data while still achieving exact recovery. We propose a similar
synchronization-based algorithm for the rank-aggregation problem, which
integrates in a globally consistent ranking pairwise comparisons given by
different rating systems on the same set of items. We also discuss the problem
of semi-supervised ranking when there is available information on the ground
truth rank of a subset of players, and propose an algorithm based on SDP which
recovers the ranks of the remaining players. Finally, synchronization-based
ranking, combined with a spectral technique for the densest subgraph problem,
allows one to extract locally-consistent partial rankings, in other words, to
identify the rank of a small subset of players whose pairwise comparisons are
less noisy than the rest of the data, which other methods are not able to
identify.
Fast algorithms for morphological operations using run-length encoded binary images
Authors: Gregor Ehrensperger, Alexander Ostermann, Felix Schwitzer
Date: 2015-04-04 00:00:00

This paper presents innovative algorithms to efficiently compute erosions and
dilations of run-length encoded (RLE) binary images with arbitrary shaped
structuring elements. An RLE image is given by a set of runs, where a run is a
horizontal concatenation of foreground pixels. The proposed algorithms extract
the skeleton of the structuring element and build distance tables of the input
image, which are storing the distance to the next background pixel on the left
and right hand sides. This information is then used to speed up the
calculations of the erosion and dilation operator by enabling the use of
techniques which allow to skip the analysis of certain pixels whenever a hit or
miss occurs. Additionally the input image gets trimmed during the preprocessing
steps on the base of two primitive criteria. Experimental results show the
advantages over other algorithms. The source code of our algorithms is
available in C++.
An Online Approach to Dynamic Channel Access and Transmission Scheduling
Authors: Yang Liu, Mingyan Liu
Date: 2015-04-04 00:00:00
Tasks: Diversity, Scheduling

Making judicious channel access and transmission scheduling decisions is
essential for improving performance as well as energy and spectral efficiency
in multichannel wireless systems. This problem has been a subject of extensive
study in the past decade, and the resulting dynamic and opportunistic channel
access schemes can bring potentially significant improvement over traditional
schemes. However, a common and severe limitation of these dynamic schemes is
that they almost always require some form of a priori knowledge of the channel
statistics. A natural remedy is a learning framework, which has also been
extensively studied in the same context, but a typical learning algorithm in
this literature seeks only the best static policy, with performance measured by
weak regret, rather than learning a good dynamic channel access policy. There
is thus a clear disconnect between what an optimal channel access policy can
achieve with known channel statistics that actively exploits temporal, spatial
and spectral diversity, and what a typical existing learning algorithm aims
for, which is the static use of a single channel devoid of diversity gain. In
this paper we bridge this gap by designing learning algorithms that track known
optimal or sub-optimal dynamic channel access and transmission scheduling
policies, thereby yielding performance measured by a form of strong regret, the
accumulated difference between the reward returned by an optimal solution when
a priori information is available and that by our online algorithm. We do so in
the context of two specific algorithms that appeared in [1] and [2],
respectively, the former for a multiuser single-channel setting and the latter
for a single-user multichannel setting. In both cases we show that our
algorithms achieve sub-linear regret uniform in time and outperforms the
standard weak-regret learning algorithms.
Microsoft COCO Captions: Data Collection and Evaluation Server
Authors: Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick
Date: 2015-04-01 00:00:00
Tasks: Caption Generation

In this paper we describe the Microsoft COCO Caption dataset and evaluation
server. When completed, the dataset will contain over one and a half million
captions describing over 330,000 images. For the training and validation
images, five independent human generated captions will be provided. To ensure
consistency in evaluation of automatic caption generation algorithms, an
evaluation server is used. The evaluation server receives candidate captions
and scores them using several popular metrics, including BLEU, METEOR, ROUGE
and CIDEr. Instructions for using the evaluation server are provided.
A Unified Deep Neural Network for Speaker and Language Recognition
Authors: Fred Richardson, Douglas Reynolds, Najim Dehak
Date: 2015-04-03 00:00:00
Tasks: Domain Adaptation, Speaker Recognition

Learned feature representations and sub-phoneme posteriors from Deep Neural
Networks (DNNs) have been used separately to produce significant performance
gains for speaker and language recognition tasks. In this work we show how
these gains are possible using a single DNN for both speaker and language
recognition. The unified DNN approach is shown to yield substantial performance
improvements on the the 2013 Domain Adaptation Challenge speaker recognition
task (55% reduction in EER for the out-of-domain condition) and on the NIST
2011 Language Recognition Evaluation (48% reduction in EER for the 30s test
condition).
On model misspecification and KL separation for Gaussian graphical models
Authors: Varun Jog, Po-Ling Loh
Date: 2015-01-10 00:00:00
Tasks: Model Selection

We establish bounds on the KL divergence between two multivariate Gaussian
distributions in terms of the Hamming distance between the edge sets of the
corresponding graphical models. We show that the KL divergence is bounded below
by a constant when the graphs differ by at least one edge; this is essentially
the tightest possible bound, since classes of graphs exist for which the edge
discrepancy increases but the KL divergence remains bounded above by a
constant. As a natural corollary to our KL lower bound, we also establish a
sample size requirement for correct model selection via maximum likelihood
estimation. Our results rigorize the notion that it is essential to estimate
the edge structure of a Gaussian graphical model accurately in order to
approximate the true distribution to close precision.
Embedding Word Similarity with Neural Machine Translation
Authors: Felix Hill, Kyunghyun Cho, Sebastien Jean, Coline Devin, Yoshua Bengio
Date: 2014-12-19 00:00:00
Tasks: Language Modeling, Language Modelling, Machine Translation, Translation, Word Similarity

Neural language models learn word representations, or embeddings, that
capture rich linguistic and conceptual information. Here we investigate the
embeddings learned by neural machine translation models, a recently-developed
class of neural language model. We show that embeddings from translation models
outperform those learned by monolingual models at tasks that require knowledge
of both conceptual similarity and lexical-syntactic role. We further show that
these effects hold when translating from both English to French and English to
German, and argue that the desirable properties of translation embeddings
should emerge largely independently of the source and target languages.
Finally, we apply a new method for training neural translation models with very
large vocabularies, and show that this vocabulary expansion algorithm results
in minimal degradation of embedding quality. Our embedding spaces can be
queried in an online demo and downloaded from our web page. Overall, our
analyses indicate that translation-based embeddings should be used in
applications that require concepts to be organised according to similarity
and/or lexical function, while monolingual embeddings are better suited to
modelling (nonspecific) inter-word relatedness.
Beta diffusion trees and hierarchical feature allocations
Authors: Creighton Heaukulani, David A. Knowles, Zoubin Ghahramani
Date: 2014-08-14 00:00:00

We define the beta diffusion tree, a random tree structure with a set of
leaves that defines a collection of overlapping subsets of objects, known as a
feature allocation. A generative process for the tree structure is defined in
terms of particles (representing the objects) diffusing in some continuous
space, analogously to the Dirichlet diffusion tree (Neal, 2003), which defines
a tree structure over partitions (i.e., non-overlapping subsets) of the
objects. Unlike in the Dirichlet diffusion tree, multiple copies of a particle
may exist and diffuse along multiple branches in the beta diffusion tree, and
an object may therefore belong to multiple subsets of particles. We demonstrate
how to build a hierarchically-clustered factor analysis model with the beta
diffusion tree and how to perform inference over the random tree structures
with a Markov chain Monte Carlo algorithm. We conclude with several numerical
experiments on missing data problems with data sets of gene expression
microarrays, international development statistics, and intranational
socioeconomic measurements.