pwc-archive/papers-with-abstracts huggingface dataset:
A Tucker decomposition process for probabilistic modeling of diffusion magnetic resonance imaging
Authors: Hernan Dario Vargas Cardona, Mauricio A. Alvarez, Alvaro A. Orozco
Date: 2016-06-25 00:00:00
Tasks: Gaussian Processes, valid

Diffusion magnetic resonance imaging (dMRI) is an emerging medical technique
used for describing water diffusion in an organic tissue. Typically, rank-2
tensors quantify this diffusion. From this quantification, it is possible to
calculate relevant scalar measures (i.e. fractional anisotropy and mean
diffusivity) employed in clinical diagnosis of neurological diseases.
Nonetheless, 2nd-order tensors fail to represent complex tissue structures like
crossing fibers. To overcome this limitation, several researchers proposed a
diffusion representation with higher order tensors (HOT), specifically 4th and
6th orders. However, the current acquisition protocols of dMRI data allow
images with a spatial resolution between 1 $mm^3$ and 2 $mm^3$. This voxel size
is much smaller than tissue structures. Therefore, several clinical procedures
derived from dMRI may be inaccurate. Interpolation has been used to enhance
resolution of dMRI in a tensorial space. Most interpolation methods are valid
only for rank-2 tensors and a generalization for HOT data is missing. In this
work, we propose a novel stochastic process called Tucker decomposition process
(TDP) for performing HOT data interpolation. Our model is based on the Tucker
decomposition and Gaussian processes as parameters of the TDP. We test the TDP
in 2nd, 4th and 6th rank HOT fields. For rank-2 tensors, we compare against
direct interpolation, log-Euclidean approach and Generalized Wishart processes.
For rank-4 and rank-6 tensors we compare against direct interpolation. Results
obtained show that TDP interpolates accurately the HOT fields and generalizes
to any rank.
Generalized Wishart processes for interpolation over diffusion tensor fields
Authors: Hernan Dario Vargas Cardona, Mauricio A. Alvarez, Alvaro A. Orozco
Date: 2016-06-25 00:00:00

Diffusion Magnetic Resonance Imaging (dMRI) is a non-invasive tool for
watching the microstructure of fibrous nerve and muscle tissue. From dMRI, it
is possible to estimate 2-rank diffusion tensors imaging (DTI) fields, that are
widely used in clinical applications: tissue segmentation, fiber tractography,
brain atlas construction, brain conductivity models, among others. Due to
hardware limitations of MRI scanners, DTI has the difficult compromise between
spatial resolution and signal noise ratio (SNR) during acquisition. For this
reason, the data are often acquired with very low resolution. To enhance DTI
data resolution, interpolation provides an interesting software solution. The
aim of this work is to develop a methodology for DTI interpolation that enhance
the spatial resolution of DTI fields. We assume that a DTI field follows a
recently introduced stochastic process known as a generalized Wishart process
(GWP), which we use as a prior over the diffusion tensor field. For posterior
inference, we use Markov Chain Monte Carlo methods. We perform experiments in
toy and real data. Results of GWP outperform other methods in the literature,
when compared in different validation protocols.
Leveraging Semantic Web Search and Browse Sessions for Multi-Turn Spoken Dialog Systems
Authors: Lu Wang, Larry Heck, Dilek Hakkani-Tur
Date: 2016-06-25 00:00:00
Tasks: Entity Extraction using GAN, Relation Extraction

Training statistical dialog models in spoken dialog systems (SDS) requires
large amounts of annotated data. The lack of scalable methods for data mining
and annotation poses a significant hurdle for state-of-the-art statistical
dialog managers. This paper presents an approach that directly leverage
billions of web search and browse sessions to overcome this hurdle. The key
insight is that task completion through web search and browse sessions is (a)
predictable and (b) generalizes to spoken dialog task completion. The new
method automatically mines behavioral search and browse patterns from web logs
and translates them into spoken dialog models. We experiment with naturally
occurring spoken dialogs and large scale web logs. Our session-based models
outperform the state-of-the-art method for entity extraction task in SDS. We
also achieve better performance for both entity and relation extraction on web
search queries when compared with nontrivial baselines.
Summarizing Decisions in Spoken Meetings
Authors: Lu Wang, Claire Cardie
Date: 2016-06-25 00:00:00
Tasks: Topic Models

This paper addresses the problem of summarizing decisions in spoken meetings:
our goal is to produce a concise {\it decision abstract} for each meeting
decision. We explore and compare token-level and dialogue act-level automatic
summarization methods using both unsupervised and supervised learning
frameworks. In the supervised summarization setting, and given true clusterings
of decision-related utterances, we find that token-level summaries that employ
discourse context can approach an upper bound for decision abstracts derived
directly from dialogue acts. In the unsupervised summarization setting,we find
that summaries based on unsupervised partitioning of decision-related
utterances perform comparably to those based on partitions generated using
supervised techniques (0.22 ROUGE-F1 using LDA-based topic models vs. 0.23
using SVMs).
X575: writing rengas with web services
Authors: Daniel Winterstein, Joseph Corneli
Date: 2016-06-25 00:00:00
Proceeding: WS 2016 9

Our software system simulates the classical collaborative Japanese poetry
form, renga, made of linked haikus. We used NLP methods wrapped up as web
services. Our experiments were only a partial success, since results fail to
satisfy classical constraints. To gather ideas for future work, we examine
related research in semiotics, linguistics, and computing.
Finding the Topic of a Set of Images
Authors: Gonzalo Vaca-Castano
Date: 2016-06-25 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity, TAG

In this paper we introduce the problem of determining the topic that a set of
images is describing, where every topic is represented as a set of words.
Different from other problems like tag assignment or similar, a) we assume
multiple images are used as input instead of single image, b) Input images are
typically not visually related, c) Input images are not necessarily
semantically close, and d) Output word space is unconstrained. In our proposed
solution, visual information of each query image is used to retrieve similar
images with text labels (tags) from an image database. We consider a scenario
where the tags are very noisy and diverse, given that they were obtained by
implicit crowd-sourcing in a database of 1 million images and over seventy
seven thousand tags. The words or tags associated to each query are processed
jointly in a word selection algorithm using random walks that allows to refine
the search topic, rejecting words that are not part of the topic and produce a
set of words that fairly describe the topic. Experiments on a dataset of 300
topics, with up to twenty images per topic, show that our algorithm performs
better than the proposed baseline for any number of query images. We also
present a new Conditional Random Field (CRF) word mapping algorithm that
preserves the semantic similarity of the mapped words, increasing the
performance of the results over the baseline.
Intrinsic Subspace Evaluation of Word Embedding Representations
Authors: Yadollah Yaghoobzadeh, Hinrich Schütze
Date: 2016-06-25 00:00:00
Proceeding: ACL 2016 8

We introduce a new methodology for intrinsic evaluation of word
representations. Specifically, we identify four fundamental criteria based on
the characteristics of natural language that pose difficulties to NLP systems;
and develop tests that directly show whether or not representations contain the
subspaces necessary to satisfy these criteria. Current intrinsic evaluations
are mostly based on the overall similarity or full-space similarity of words
and thus view vector representations as points. We show the limits of these
point-based intrinsic evaluations. We apply our evaluation methodology to the
comparison of a count vector model and several neural network models and
demonstrate important properties of these models.
Corpus-level Fine-grained Entity Typing Using Contextual Information
Authors: Yadollah Yaghoobzadeh, Hinrich Schütze
Date: 2016-06-25 00:00:00
Proceeding: EMNLP 2015 9
Tasks: Entity Typing, Knowledge Base Completion, Open Information Extraction

This paper addresses the problem of corpus-level entity typing, i.e.,
inferring from a large corpus that an entity is a member of a class such as
"food" or "artist". The application of entity typing we are interested in is
knowledge base completion, specifically, to learn which classes an entity is a
member of. We propose FIGMENT to tackle this problem. FIGMENT is
embedding-based and combines (i) a global model that scores based on aggregated
contextual information of an entity and (ii) a context model that first scores
the individual occurrences of an entity and then aggregates the scores. In our
evaluation, FIGMENT strongly outperforms an approach to entity typing that
relies on relations obtained by an open information extraction system.
Large-Scale Kernel Methods for Independence Testing
Authors: Qinyi Zhang, Sarah Filippi, Arthur Gretton, Dino Sejdinovic
Date: 2016-06-25 00:00:00
Tasks: Computational Efficiency

Representations of probability measures in reproducing kernel Hilbert spaces
provide a flexible framework for fully nonparametric hypothesis tests of
independence, which can capture any type of departure from independence,
including nonlinear associations and multivariate interactions. However, these
approaches come with an at least quadratic computational cost in the number of
observations, which can be prohibitive in many applications. Arguably, it is
exactly in such large-scale datasets that capturing any type of dependence is
of interest, so striking a favourable tradeoff between computational efficiency
and test performance for kernel independence tests would have a direct impact
on their applicability in practice. In this contribution, we provide an
extensive study of the use of large-scale kernel approximations in the context
of independence testing, contrasting block-based, Nystrom and random Fourier
feature approaches. Through a variety of synthetic data experiments, it is
demonstrated that our novel large scale methods give comparable performance
with existing methods whilst using significantly less computation time and
memory.
Scale-aware Fast R-CNN for Pedestrian Detection
Authors: Jianan Li, Xiaodan Liang, ShengMei Shen, Tingfa Xu, Jiashi Feng, Shuicheng Yan
Date: 2015-10-28 00:00:00
Tasks: Pedestrian Detection, Philosophy

In this work, we consider the problem of pedestrian detection in natural
scenes. Intuitively, instances of pedestrians with different spatial scales may
exhibit dramatically different features. Thus, large variance in instance
scales, which results in undesirable large intra-category variance in features,
may severely hurt the performance of modern object instance detection methods.
We argue that this issue can be substantially alleviated by the
divide-and-conquer philosophy. Taking pedestrian detection as an example, we
illustrate how we can leverage this philosophy to develop a Scale-Aware Fast
R-CNN (SAF R-CNN) framework. The model introduces multiple built-in
sub-networks which detect pedestrians with scales from disjoint ranges. Outputs
from all the sub-networks are then adaptively combined to generate the final
detection results that are shown to be robust to large variance in instance
scales, via a gate function defined over the sizes of object proposals.
Extensive evaluations on several challenging pedestrian detection datasets well
demonstrate the effectiveness of the proposed SAF R-CNN. Particularly, our
method achieves state-of-the-art performance on Caltech, INRIA, and ETH, and
obtains competitive results on KITTI.
Functional archetype and archetypoid analysis
Authors: Irene Epifanio
Date: 2016-01-26 00:00:00
Tasks: valid

Archetype and archetypoid analysis can be extended to functional data. Each
function is represented as a mixture of actual observations (functional
archetypoids) or functional archetypes, which are a mixture of observations in
the data set. Well-known Canadian temperature data are used to illustrate the
analysis developed. Computational methods are proposed for performing these
analyses, based on the coefficients of a basis. Unlike a previous attempt to
compute functional archetypes, which was only valid for an orthogonal basis,
the proposed methodology can be used for any basis. It is computationally less
demanding than the simple approach of discretizing the functions. Multivariate
functional archetype and archetypoid analysis are also introduced and applied
in an interesting problem about the study of human development around the world
over the last 50 years. These tools can contribute to the understanding of a
functional data set, as in the multivariate case.
An Uncertain Future: Forecasting from Static Images using Variational Autoencoders
Authors: Jacob Walker, Carl Doersch, Abhinav Gupta, Martial Hebert
Date: 2016-06-25 00:00:00

In a given scene, humans can often easily predict a set of immediate future
events that might happen. However, generalized pixel-level anticipation in
computer vision systems is difficult because machine learning struggles with
the ambiguity inherent in predicting the future. In this paper, we focus on
predicting the dense trajectory of pixels in a scene, specifically what will
move in the scene, where it will travel, and how it will deform over the course
of one second. We propose a conditional variational autoencoder as a solution
to this problem. In this framework, direct inference from the image shapes the
distribution of possible trajectories, while latent variables encode any
necessary information that is not available in the image. We show that our
method is able to successfully predict events in a wide variety of scenes and
can produce multiple different predictions when the future is ambiguous. Our
algorithm is trained on thousands of diverse, realistic videos and requires
absolutely no human labeling. In addition to non-semantic action prediction, we
find that our method learns a representation that is applicable to semantic
vision tasks.
Adaptive Image Denoising by Mixture Adaptation
Authors: Enming Luo, Stanley H. Chan, Truong Q. Nguyen
Date: 2016-01-19 00:00:00
Tasks: Denoising, Image Denoising

We propose an adaptive learning procedure to learn patch-based image priors
for image denoising. The new algorithm, called the Expectation-Maximization
(EM) adaptation, takes a generic prior learned from a generic external database
and adapts it to the noisy image to generate a specific prior. Different from
existing methods that combine internal and external statistics in ad-hoc ways,
the proposed algorithm is rigorously derived from a Bayesian hyper-prior
perspective. There are two contributions of this paper: First, we provide full
derivation of the EM adaptation algorithm and demonstrate methods to improve
the computational complexity. Second, in the absence of the latent clean image,
we show how EM adaptation can be modified based on pre-filtering. Experimental
results show that the proposed adaptation algorithm yields consistently better
denoising results than the one without adaptation and is superior to several
state-of-the-art algorithms.
Global Convergence of a Grassmannian Gradient Descent Algorithm for Subspace Estimation
Authors: Dejiao Zhang, Laura Balzano
Date: 2015-06-24 00:00:00

It has been observed in a variety of contexts that gradient descent methods
have great success in solving low-rank matrix factorization problems, despite
the relevant problem formulation being non-convex. We tackle a particular
instance of this scenario, where we seek the $d$-dimensional subspace spanned
by a streaming data matrix. We apply the natural first order incremental
gradient descent method, constraining the gradient method to the Grassmannian.
In this paper, we propose an adaptive step size scheme that is greedy for the
noiseless case, that maximizes the improvement of our metric of convergence at
each data index $t$, and yields an expected improvement for the noisy case. We
show that, with noise-free data, this method converges from any random
initialization to the global minimum of the problem. For noisy data, we provide
the expected convergence rate of the proposed algorithm per iteration.
Advances in Very Deep Convolutional Neural Networks for LVCSR
Authors: Tom Sercu, Vaibhava Goel
Date: 2016-04-06 00:00:00
Tasks: set matching, speech-recognition, Speech Recognition

Very deep CNNs with small 3x3 kernels have recently been shown to achieve
very strong performance as acoustic models in hybrid NN-HMM speech recognition
systems. In this paper we investigate how to efficiently scale these models to
larger datasets. Specifically, we address the design choice of pooling and
padding along the time dimension which renders convolutional evaluation of
sequences highly inefficient. We propose a new CNN design without timepadding
and without timepooling, which is slightly suboptimal for accuracy, but has two
significant advantages: it enables sequence training and deployment by allowing
efficient convolutional evaluation of full utterances, and, it allows for batch
normalization to be straightforwardly adopted to CNNs on sequence data. Through
batch normalization, we recover the lost peformance from removing the
time-pooling, while keeping the benefit of efficient convolutional evaluation.
We demonstrate the performance of our models both on larger scale data than
before, and after sequence training. Our very deep CNN model sequence trained
on the 2000h switchboard dataset obtains 9.4 word error rate on the Hub5
test-set, matching with a single model the performance of the 2015 IBM system
combination, which was the previous best published result.
Probabilistic Forecasting and Simulation of Electricity Markets via Online Dictionary Learning
Authors: Weisi Deng, Yuting Ji, Lang Tong
Date: 2016-06-25 00:00:00
Tasks: Dictionary Learning

The problem of probabilistic forecasting and online simulation of real-time
electricity market with stochastic generation and demand is considered. By
exploiting the parametric structure of the direct current optimal power flow, a
new technique based on online dictionary learning (ODL) is proposed. The ODL
approach incorporates real-time measurements and historical traces to produce
forecasts of joint and marginal probability distributions of future locational
marginal prices, power flows, and dispatch levels, conditional on the system
state at the time of forecasting. Compared with standard Monte Carlo simulation
techniques, the ODL approach offers several orders of magnitude improvement in
computation time, making it feasible for online forecasting of market
operations. Numerical simulations on large and moderate size power systems
illustrate its performance and complexity features and its potential as a tool
for system operators.
Learning overcomplete, low coherence dictionaries with linear inference
Authors: Jesse A. Livezey, Alejandro F. Bujan, Friedrich T. Sommer
Date: 2016-06-10 00:00:00
Tasks: compressed sensing

Finding overcomplete latent representations of data has applications in data
analysis, signal processing, machine learning, theoretical neuroscience and
many other fields. In an overcomplete representation, the number of latent
features exceeds the data dimensionality, which is useful when the data is
undersampled by the measurements (compressed sensing, information bottlenecks
in neural systems) or composed from multiple complete sets of linear features,
each spanning the data space. Independent Components Analysis (ICA) is a linear
technique for learning sparse latent representations, which typically has a
lower computational cost than sparse coding, its nonlinear, recurrent
counterpart. While well suited for finding complete representations, we show
that overcompleteness poses a challenge to existing ICA algorithms.
Specifically, the coherence control in existing ICA algorithms, necessary to
prevent the formation of duplicate dictionary features, is ill-suited in the
overcomplete case. We show that in this case several existing ICA algorithms
have undesirable global minima that maximize coherence. Further, by comparing
ICA algorithms on synthetic data and natural images to the computationally more
expensive sparse coding solution, we show that the coherence control biases the
exploration of the data manifold, sometimes yielding suboptimal solutions. We
provide a theoretical explanation of these failures and, based on the theory,
propose improved overcomplete ICA algorithms. All told, this study contributes
new insights into and methods for coherence control for linear ICA, some of
which are applicable to many other, potentially nonlinear, unsupervised
learning methods.
Focused Meeting Summarization via Unsupervised Relation Extraction
Authors: Lu Wang, Claire Cardie
Date: 2016-06-24 00:00:00
Proceeding: WS 2012 7
Tasks: Extractive Summarization, Meeting Summarization, Relation, Relation Extraction

We present a novel unsupervised framework for focused meeting summarization
that views the problem as an instance of relation extraction. We adapt an
existing in-domain relation learner (Chen et al., 2011) by exploiting a set of
task-specific constraints and features. We evaluate the approach on a decision
summarization task and show that it outperforms unsupervised utterance-level
extractive summarization baselines as well as an existing generic
relation-extraction-based summarization method. Moreover, our approach produces
summaries competitive with those generated by supervised methods in terms of
the standard ROUGE score.
Robust and scalable Bayesian analysis of spatial neural tuning function data
Authors: Kamiar Rahnama Rad, Timothy A. Machado, Liam Paninski
Date: 2016-06-24 00:00:00

A common analytical problem in neuroscience is the interpretation of neural
activity with respect to sensory input or behavioral output. This is typically
achieved by regressing measured neural activity against known stimuli or
behavioral variables to produce a "tuning function" for each neuron.
Unfortunately, because this approach handles neurons individually, it cannot
take advantage of simultaneous measurements from spatially adjacent neurons
that often have similar tuning properties. On the other hand, sharing
information between adjacent neurons can errantly degrade estimates of tuning
functions across space if there are sharp discontinuities in tuning between
nearby neurons. In this paper, we develop a computationally efficient block
Gibbs sampler that effectively pools information between neurons to de-noise
tuning function estimates while simultaneously preserving sharp discontinuities
that might exist in the organization of tuning across space. This method is
fully Bayesian and its computational cost per iteration scales
sub-quadratically with total parameter dimensionality. We demonstrate the
robustness and scalability of this approach by applying it to both real and
synthetic datasets. In particular, an application to data from the spinal cord
illustrates that the proposed methods can dramatically decrease the
experimental time required to accurately estimate tuning functions.