pwc-archive/papers-with-abstracts huggingface dataset:
FrameNet CNL: a Knowledge Representation and Information Extraction Language
Authors: Guntis Barzdins
Date: 2014-06-10 00:00:00
Tasks: Articles

The paper presents a FrameNet-based information extraction and knowledge
representation framework, called FrameNet-CNL. The framework is used on natural
language documents and represents the extracted knowledge in a tailor-made
Frame-ontology from which unambiguous FrameNet-CNL paraphrase text can be
generated automatically in multiple languages. This approach brings together
the fields of information extraction and CNL, because a source text can be
considered belonging to FrameNet-CNL, if information extraction parser produces
the correct knowledge representation as a result. We describe a
state-of-the-art information extraction parser used by a national news agency
and speculate that FrameNet-CNL eventually could shape the natural language
subset used for writing the newswire articles.
Denosing Using Wavelets and Projections onto the L1-Ball
Authors: A. Enis Cetin, Mohammad Tofighi
Date: 2014-06-10 00:00:00
Tasks: Denoising

Both wavelet denoising and denosing methods using the concept of sparsity are
based on soft-thresholding. In sparsity based denoising methods, it is assumed
that the original signal is sparse in some transform domains such as the
wavelet domain and the wavelet subsignals of the noisy signal are projected
onto L1-balls to reduce noise. In this lecture note, it is shown that the size
of the L1-ball or equivalently the soft threshold value can be determined using
linear algebra. The key step is an orthogonal projection onto the epigraph set
of the L1-norm cost function.
Hyperspectral image superresolution: An edge-preserving convex formulation
Authors: Miguel Simões, José Bioucas-Dias, Luis B. Almeida, Jocelyn Chanussot
Date: 2014-03-31 00:00:00

Hyperspectral remote sensing images (HSIs) are characterized by having a low
spatial resolution and a high spectral resolution, whereas multispectral images
(MSIs) are characterized by low spectral and high spatial resolutions. These
complementary characteristics have stimulated active research in the inference
of images with high spatial and spectral resolutions from HSI-MSI pairs.
  In this paper, we formulate this data fusion problem as the minimization of a
convex objective function containing two data-fitting terms and an
edge-preserving regularizer. The data-fitting terms are quadratic and account
for blur, different spatial resolutions, and additive noise; the regularizer, a
form of vector Total Variation, promotes aligned discontinuities across the
reconstructed hyperspectral bands.
  The optimization described above is rather hard, owing to its
non-diagonalizable linear operators, to the non-quadratic and non-smooth nature
of the regularizer, and to the very large size of the image to be inferred. We
tackle these difficulties by tailoring the Split Augmented Lagrangian Shrinkage
Algorithm (SALSA)---an instance of the Alternating Direction Method of
Multipliers (ADMM)---to this optimization problem. By using a convenient
variable splitting and by exploiting the fact that HSIs generally "live" in a
low-dimensional subspace, we obtain an effective algorithm that yields
state-of-the-art results, as illustrated by experiments.
Music and Vocal Separation Using Multi-Band Modulation Based Features
Authors: Sunil Kumar Kopparapu, Meghna Pandharipande, G Sita
Date: 2014-06-10 00:00:00

The potential use of non-linear speech features has not been investigated for
music analysis although other commonly used speech features like Mel Frequency
Ceptral Coefficients (MFCC) and pitch have been used extensively. In this
paper, we assume an audio signal to be a sum of modulated sinusoidal and then
use the energy separation algorithm to decompose the audio into amplitude and
frequency modulation components using the non-linear Teager-Kaiser energy
operator. We first identify the distribution of these non-linear features for
music only and voice only segments in the audio signal in different Mel spaced
frequency bands and show that they have the ability to discriminate. The
proposed method based on Kullback-Leibler divergence measure is evaluated using
a set of Indian classical songs from three different artists. Experimental
results show that the discrimination ability is evident in certain low and mid
frequency bands (200 - 1500 Hz).
Bayesian calibration for forensic evidence reporting
Authors: Niko Brümmer, Albert Swart
Date: 2014-03-24 00:00:00
Tasks: Speaker Recognition

We introduce a Bayesian solution for the problem in forensic speaker
recognition, where there may be very little background material for estimating
score calibration parameters. We work within the Bayesian paradigm of evidence
reporting and develop a principled probabilistic treatment of the problem,
which results in a Bayesian likelihood-ratio as the vehicle for reporting
weight of evidence. We show in contrast, that reporting a likelihood-ratio
distribution does not solve this problem. Our solution is experimentally
exercised on a simulated forensic scenario, using NIST SRE'12 scores, which
demonstrates a clear advantage for the proposed method compared to the
traditional plugin calibration recipe.
Detecting Directionality in Random Fields Using the Monogenic Signal
Authors: Sofia Olhede, David Ramírez, Peter J. Schreier
Date: 2013-04-10 00:00:00

Detecting and analyzing directional structures in images is important in many
applications since one-dimensional patterns often correspond to important
features such as object contours or trajectories. Classifying a structure as
directional or non-directional requires a measure to quantify the degree of
directionality and a threshold, which needs to be chosen based on the
statistics of the image. In order to do this, we model the image as a random
field. So far, little research has been performed on analyzing directionality
in random fields. In this paper, we propose a measure to quantify the degree of
directionality based on the random monogenic signal, which enables a unique
decomposition of a 2D signal into local amplitude, local orientation, and local
phase. We investigate the second-order statistical properties of the monogenic
signal for isotropic, anisotropic, and unidirectional random fields. We analyze
our measure of directionality for finite-size sample images, and determine a
threshold to distinguish between unidirectional and non-unidirectional random
fields, which allows the automatic classification of images.
Online Convex Optimization Against Adversaries with Memory and Application to Statistical Arbitrage
Authors: Oren Anava, Elad Hazan, Shie Mannor
Date: 2013-02-27 00:00:00

The framework of online learning with memory naturally captures learning
problems with temporal constraints, and was previously studied for the experts
setting. In this work we extend the notion of learning with memory to the
general Online Convex Optimization (OCO) framework, and present two algorithms
that attain low regret. The first algorithm applies to Lipschitz continuous
loss functions, obtaining optimal regret bounds for both convex and strongly
convex losses. The second algorithm attains the optimal regret bounds and
applies more broadly to convex losses without requiring Lipschitz continuity,
yet is more complicated to implement. We complement our theoretic results with
an application to statistical arbitrage in finance: we devise algorithms for
constructing mean-reverting portfolios.
Why do linear SVMs trained on HOG features perform so well?
Authors: Hilton Bristow, Simon Lucey
Date: 2014-06-10 00:00:00
Tasks: Pedestrian Detection

Linear Support Vector Machines trained on HOG features are now a de facto
standard across many visual perception tasks. Their popularisation can largely
be attributed to the step-change in performance they brought to pedestrian
detection, and their subsequent successes in deformable parts models. This
paper explores the interactions that make the HOG-SVM symbiosis perform so
well. By connecting the feature extraction and learning processes rather than
treating them as disparate plugins, we show that HOG features can be viewed as
doing two things: (i) inducing capacity in, and (ii) adding prior to a linear
SVM trained on pixels. From this perspective, preserving second-order
statistics and locality of interactions are key to good performance. We
demonstrate surprising accuracy on expression recognition and pedestrian
detection tasks, by assuming only the importance of preserving such local
second-order interactions.
Unsupervised Feature Learning through Divergent Discriminative Feature Accumulation
Authors: Paul A. Szerlip, Gregory Morse, Justin K. Pugh, Kenneth O. Stanley
Date: 2014-06-06 00:00:00
Tasks: General Classification

Unlike unsupervised approaches such as autoencoders that learn to reconstruct
their inputs, this paper introduces an alternative approach to unsupervised
feature learning called divergent discriminative feature accumulation (DDFA)
that instead continually accumulates features that make novel discriminations
among the training set. Thus DDFA features are inherently discriminative from
the start even though they are trained without knowledge of the ultimate
classification problem. Interestingly, DDFA also continues to add new features
indefinitely (so it does not depend on a hidden layer size), is not based on
minimizing error, and is inherently divergent instead of convergent, thereby
providing a unique direction of research for unsupervised feature learning. In
this paper the quality of its learned features is demonstrated on the MNIST
dataset, where its performance confirms that indeed DDFA is a viable technique
for learning useful features.
Optimization Methods for Convolutional Sparse Coding
Authors: Hilton Bristow, Simon Lucey
Date: 2014-06-10 00:00:00

Sparse and convolutional constraints form a natural prior for many
optimization problems that arise from physical processes. Detecting motifs in
speech and musical passages, super-resolving images, compressing videos, and
reconstructing harmonic motions can all leverage redundancies introduced by
convolution. Solving problems involving sparse and convolutional constraints
remains a difficult computational problem, however. In this paper we present an
overview of convolutional sparse coding in a consistent framework. The
objective involves iteratively optimizing a convolutional least-squares term
for the basis functions, followed by an L1-regularized least squares term for
the sparse coefficients. We discuss a range of optimization methods for solving
the convolutional sparse coding objective, and the properties that make each
method suitable for different applications. In particular, we concentrate on
computational complexity, speed to {\epsilon} convergence, memory usage, and
the effect of implied boundary conditions. We present a broad suite of examples
covering different signal and application domains to illustrate the general
applicability of convolutional sparse coding, and the efficacy of the available
optimization methods.
Controlled Natural Language Generation from a Multilingual FrameNet-based Grammar
Authors: Dana Dannélls, Normunds Grūzītis
Date: 2014-06-10 00:00:00
Tasks: Text Generation

This paper presents a currently bilingual but potentially multilingual
FrameNet-based grammar library implemented in Grammatical Framework. The
contribution of this paper is two-fold. First, it offers a methodological
approach to automatically generate the grammar based on semantico-syntactic
valence patterns extracted from FrameNet-annotated corpora. Second, it provides
a proof of concept for two use cases illustrating how the acquired multilingual
grammar can be exploited in different CNL applications in the domains of arts
and tourism.
ExpertBayes: Automatically refining manually built Bayesian networks
Authors: Ezilda Almeida, Pedro Ferreira, Tiago Vinhoza, Inês Dutra, Jingwei Li, Yirong Wu, Elizabeth Burnside
Date: 2014-06-10 00:00:00

Bayesian network structures are usually built using only the data and
starting from an empty network or from a naive Bayes structure. Very often, in
some domains, like medicine, a prior structure knowledge is already known. This
structure can be automatically or manually refined in search for better
performance models. In this work, we take Bayesian networks built by
specialists and show that minor perturbations to this original network can
yield better classifiers with a very small computational cost, while
maintaining most of the intended meaning of the original model.
Automatic Labeling for Entity Extraction in Cyber Security
Authors: Robert A. Bridges, Corinne L. Jones, Michael D. Iannacone, Kelly M. Testa, John R. Goodall
Date: 2013-08-22 00:00:00
Tasks: Entity Extraction using GAN

Timely analysis of cyber-security information necessitates automated
information extraction from unstructured text. While state-of-the-art
extraction methods produce extremely accurate results, they require ample
training data, which is generally unavailable for specialized applications,
such as detecting security related entities; moreover, manual annotation of
corpora is very costly and often not a viable solution. In response, we develop
a very precise method to automatically label text from several data sources by
leveraging related, domain-specific, structured data and provide public access
to a corpus annotated with cyber-security entities. Next, we implement a
Maximum Entropy Model trained with the average perceptron on a portion of our
corpus ($\sim$750,000 words) and achieve near perfect precision, recall, and
accuracy, with training times under 17 seconds.
Explaining Violation Traces with Finite State Natural Language Generation Models
Authors: Gordon J. Pace, Michael Rosner
Date: 2014-06-09 00:00:00
Tasks: Text Generation

An essential element of any verification technique is that of identifying and
communicating to the user, system behaviour which leads to a deviation from the
expected behaviour. Such behaviours are typically made available as long traces
of system actions which would benefit from a natural language explanation of
the trace and especially in the context of business logic level specifications.
In this paper we present a natural language generation model which can be used
to explain such traces. A key idea is that the explanation language is a CNL
that is, formally speaking, regular language susceptible transformations that
can be expressed with finite state machinery. At the same time it admits
various forms of abstraction and simplification which contribute to the
naturalness of explanations that are communicated to the user.
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
Authors: David Eigen, Christian Puhrsch, Rob Fergus
Date: 2014-06-09 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: 3D geometry, Monocular Depth Estimation

Predicting depth is an essential component in understanding the 3D geometry
of a scene. While for stereo images local correspondence suffices for
estimation, finding depth relations from a single image is less
straightforward, requiring integration of both global and local information
from various cues. Moreover, the task is inherently ambiguous, with a large
source of uncertainty coming from the overall scale. In this paper, we present
a new method that addresses this task by employing two deep network stacks: one
that makes a coarse global prediction based on the entire image, and another
that refines this prediction locally. We also apply a scale-invariant error to
help measure depth relations rather than scale. By leveraging the raw datasets
as large sources of training data, our method achieves state-of-the-art results
on both NYU Depth and KITTI, and matches detailed depth boundaries without the
need for superpixelation.
Robust Estimation of 3D Human Poses from a Single Image
Authors: Chunyu Wang, Yizhou Wang, Zhouchen Lin, Alan L. Yuille, Wen Gao
Date: 2014-06-09 00:00:00
Proceeding: CVPR 2014 6
Tasks: 3D Human Pose Estimation, 3D Pose Estimation, Action Recognition, Pose Estimation, Temporal Action Localization

Human pose estimation is a key step to action recognition. We propose a
method of estimating 3D human poses from a single image, which works in
conjunction with an existing 2D pose/joint detector. 3D pose estimation is
challenging because multiple 3D poses may correspond to the same 2D pose after
projection due to the lack of depth information. Moreover, current 2D pose
estimators are usually inaccurate which may cause errors in the 3D estimation.
We address the challenges in three ways: (i) We represent a 3D pose as a linear
combination of a sparse set of bases learned from 3D human skeletons. (ii) We
enforce limb length constraints to eliminate anthropomorphically implausible
skeletons. (iii) We estimate a 3D pose by minimizing the $L_1$-norm error
between the projection of the 3D pose and the corresponding 2D detection. The
$L_1$-norm loss term is robust to inaccurate 2D joint estimations. We use the
alternating direction method (ADM) to solve the optimization problem
efficiently. Our approach outperforms the state-of-the-arts on three benchmark
datasets.
Feature Selection For High-Dimensional Clustering
Authors: Larry Wasserman, Martin Azizyan, Aarti Singh
Date: 2014-06-09 00:00:00
Tasks: Clustering, Density Estimation, feature selection, Vocal Bursts Intensity Prediction

We present a nonparametric method for selecting informative features in
high-dimensional clustering problems. We start with a screening step that uses
a test for multimodality. Then we apply kernel density estimation and mode
clustering to the selected features. The output of the method consists of a
list of relevant features, and cluster assignments. We provide explicit bounds
on the error rate of the resulting clustering. In addition, we provide the
first error bounds on mode based clustering.
A Hybrid Latent Variable Neural Network Model for Item Recommendation
Authors: Michael R. Smith, Tony Martinez, Michael Gashler
Date: 2014-06-09 00:00:00
Tasks: Collaborative Filtering

Collaborative filtering is used to recommend items to a user without
requiring a knowledge of the item itself and tends to outperform other
techniques. However, collaborative filtering suffers from the cold-start
problem, which occurs when an item has not yet been rated or a user has not
rated any items. Incorporating additional information, such as item or user
descriptions, into collaborative filtering can address the cold-start problem.
In this paper, we present a neural network model with latent input variables
(latent neural network or LNN) as a hybrid collaborative filtering technique
that addresses the cold-start problem. LNN outperforms a broad selection of
content-based filters (which make recommendations based on item descriptions)
and other hybrid approaches while maintaining the accuracy of state-of-the-art
collaborative filtering techniques.
Fault-Tolerant, but Paradoxical Path-Finding in Physical and Conceptual Systems
Authors: Bryan Knowles, Mustafa Atici
Date: 2014-06-09 00:00:00

We report our initial investigations into reliability and path-finding based
models and propose future areas of interest. Inspired by broken sidewalks
during on-campus construction projects, we develop two models for navigating
this "unreliable network." These are based on a concept of "accumulating risk"
backward from the destination, and both operate on directed acyclic graphs with
a probability of failure associated with each edge. The first serves to
introduce and has faults addressed by the second, more conservative model.
Next, we show a paradox when these models are used to construct polynomials on
conceptual networks, such as design processes and software development life
cycles. When the risk of a network increases uniformly, the most reliable path
changes from wider and longer to shorter and narrower. If we let professional
inexperience--such as with entry level cooks and software developers--represent
probability of edge failure, does this change in path imply that the novice
should follow instructions with fewer "back-up" plans, yet those with
alternative routes should be followed by the expert?
Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation
Authors: Emily Denton, Wojciech Zaremba, Joan Bruna, Yann Lecun, Rob Fergus
Date: 2014-04-02 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: CPU, GPU, Object Recognition

We present techniques for speeding up the test-time evaluation of large convolutional networks, designed for object recognition tasks. These models deliver impressive accuracy but each image evaluation requires millions of floating point operations, making their deployment on smartphones and Internet-scale clusters problematic. The computation is dominated by the convolution operations in the lower layers of the model. We exploit the linear structure present within the convolutional filters to derive approximations that significantly reduce the required computation. Using large state-of-the-art models, we demonstrate we demonstrate speedups of convolutional layers on both CPU and GPU by a factor of 2x, while keeping the accuracy within 1% of the original model.
Efficient Sparse Clustering of High-Dimensional Non-spherical Gaussian Mixtures
Authors: Martin Azizyan, Aarti Singh, Larry Wasserman
Date: 2014-06-09 00:00:00
Tasks: Clustering, Vocal Bursts Intensity Prediction

We consider the problem of clustering data points in high dimensions, i.e.
when the number of data points may be much smaller than the number of
dimensions. Specifically, we consider a Gaussian mixture model (GMM) with
non-spherical Gaussian components, where the clusters are distinguished by only
a few relevant dimensions. The method we propose is a combination of a recent
approach for learning parameters of a Gaussian mixture model and sparse linear
discriminant analysis (LDA). In addition to cluster assignments, the method
returns an estimate of the set of features relevant for clustering. Our results
indicate that the sample complexity of clustering depends on the sparsity of
the relevant feature set, while only scaling logarithmically with the ambient
dimension. Additionally, we require much milder assumptions than existing work
on clustering in high dimensions. In particular, we do not require spherical
clusters nor necessitate mean separation along relevant dimensions.
How Easy is it to Learn a Controlled Natural Language for Building a Knowledge Base?
Authors: Sandra Williams, Richard Power, Allan Third
Date: 2014-06-09 00:00:00

Recent developments in controlled natural language editors for knowledge
engineering (KE) have given rise to expectations that they will make KE tasks
more accessible and perhaps even enable non-engineers to build knowledge bases.
This exploratory research focussed on novices and experts in knowledge
engineering during their attempts to learn a controlled natural language (CNL)
known as OWL Simplified English and use it to build a small knowledge base.
Participants' behaviours during the task were observed through eye-tracking and
screen recordings. This was an attempt at a more ambitious user study than in
previous research because we used a naturally occurring text as the source of
domain knowledge, and left them without guidance on which information to
select, or how to encode it. We have identified a number of skills
(competencies) required for this difficult task and key problems that authors
face.
Tableaux for Dynamic Logic of Propositional Assignments
Authors: Tiago de Lima, Andreas Herzig
Date: 2014-06-09 00:00:00

The Dynamic Logic for Propositional Assignments (DL-PA) has recently been
studied as an alternative to Propositional Dynamic Logic (PDL). In DL-PA, the
abstract atomic programs of PDL are replaced by assignments of propositional
variables to truth values. This makes DL-PA enjoy some interesting meta-logical
properties that PDL does not, such as eliminability of the Kleene star,
compactness and interpolation. We define and analytic tableaux calculus for
DL-PA and show that it matches the known complexity results.