pwc-archive/papers-with-abstracts huggingface dataset:
A chain rule for the expected suprema of Gaussian processes
Authors: Andreas Maurer
Date: 2014-11-10 00:00:00
Tasks: Gaussian Processes

The expected supremum of a Gaussian process indexed by the image of an index
set under a function class is bounded in terms of separate properties of the
index set and the function class. The bound is relevant to the estimation of
nonlinear transformations or the analysis of learning algorithms whenever
hypotheses are chosen from composite classes, as is the case for multi-layer
models.
Deep Exponential Families
Authors: Rajesh Ranganath, Linpeng Tang, Laurent Charlin, David M. Blei
Date: 2014-11-10 00:00:00
Tasks: Variational Inference

We describe \textit{deep exponential families} (DEFs), a class of latent
variable models that are inspired by the hidden structures used in deep neural
networks. DEFs capture a hierarchy of dependencies between latent variables,
and are easily generalized to many settings through exponential families. We
perform inference using recent "black box" variational inference techniques. We
then evaluate various DEFs on text and combine multiple DEFs into a model for
pairwise recommendation data. In an extensive study, we show that going beyond
one layer improves predictions for DEFs. We demonstrate that DEFs find
interesting exploratory structure in large data sets, and give better
predictive performance than state-of-the-art models.
Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models
Authors: Ryan Kiros, Ruslan Salakhutdinov, Richard S. Zemel
Date: 2014-11-10 00:00:00
Tasks: Decoder, Language Modeling, Language Modelling, Machine Translation, Sentence, Translation

Inspired by recent advances in multimodal learning and machine translation,
we introduce an encoder-decoder pipeline that learns (a): a multimodal joint
embedding space with images and text and (b): a novel language model for
decoding distributed representations from our space. Our pipeline effectively
unifies joint image-text embedding models with multimodal neural language
models. We introduce the structure-content neural language model that
disentangles the structure of a sentence to its content, conditioned on
representations produced by the encoder. The encoder allows one to rank images
and sentences while the decoder can generate novel descriptions from scratch.
Using LSTM to encode sentences, we match the state-of-the-art performance on
Flickr8K and Flickr30K without using object detections. We also set new best
results when using the 19-layer Oxford convolutional network. Furthermore we
show that with linear encoders, the learned embedding space captures multimodal
regularities in terms of vector space arithmetic e.g. *image of a blue car* -
"blue" + "red" is near images of red cars. Sample captions generated for 800
images are made available for comparison.
Learning to Generate Networks
Authors: James Atwood, Don Towsley, Krista Gile, David Jensen
Date: 2014-05-22 00:00:00

We investigate the problem of learning to generate complex networks from
data. Specifically, we consider whether deep belief networks, dependency
networks, and members of the exponential random graph family can learn to
generate networks whose complex behavior is consistent with a set of input
examples. We find that the deep model is able to capture the complex behavior
of small networks, but that no model is able capture this behavior for networks
with more than a handful of nodes.
Comparative Study of View Update Algorithms in Rational Choice Theory
Authors: Radhakrishnan Delhibabu
Date: 2014-11-10 00:00:00

The dynamics of belief and knowledge is one of the major components of any
autonomous system that should be able to incorporate new pieces of information.
We show that knowledge base dynamics has interesting connection with kernel
change via hitting set and abduction. The approach extends and integrates
standard techniques for efficient query answering and integrity checking. The
generation of hitting set is carried out through a hyper tableaux calculus and
magic set that is focused on the goal of minimality. Many different view update
algorithms have been proposed in the literature to address this problem. The
present paper provides a comparative study of view update algorithms in
rational approach.
Distributed k-means algorithm
Authors: Gabriele Oliva, Roberto Setola, Christoforos N. Hadjicostis
Date: 2013-12-15 00:00:00
Tasks: Clustering, Position

In this paper we provide a fully distributed implementation of the k-means
clustering algorithm, intended for wireless sensor networks where each agent is
endowed with a possibly high-dimensional observation (e.g., position, humidity,
temperature, etc.) The proposed algorithm, by means of one-hop communication,
partitions the agents into measure-dependent groups that have small in-group
and large out-group "distances". Since the partitions may not have a relation
with the topology of the network--members of the same clusters may not be
spatially close--the algorithm is provided with a mechanism to compute the
clusters'centroids even when the clusters are disconnected in several
sub-clusters.The results of the proposed distributed algorithm coincide, in
terms of minimization of the objective function, with the centralized k-means
algorithm. Some numerical examples illustrate the capabilities of the proposed
solution.
Sparse Estimation with Generalized Beta Mixture and the Horseshoe Prior
Authors: Zahra Sabetsarvestani, Hamidreza Amindavar
Date: 2014-11-10 00:00:00
Tasks: Compressive Sensing

In this paper, the use of the Generalized Beta Mixture (GBM) and Horseshoe
distributions as priors in the Bayesian Compressive Sensing framework is
proposed. The distributions are considered in a two-layer hierarchical model,
making the corresponding inference problem amenable to Expectation Maximization
(EM). We present an explicit, algebraic EM-update rule for the models, yielding
two fast and experimentally validated algorithms for signal recovery.
Experimental results show that our algorithms outperform state-of-the-art
methods on a wide range of sparsity levels and amplitudes in terms of
reconstruction accuracy, convergence rate and sparsity. The largest improvement
can be observed for sparse signals with high amplitudes.
Multi-Task Metric Learning on Network Data
Authors: Chen Fang, Daniel N. Rockmore
Date: 2014-11-10 00:00:00
Tasks: Metric Learning, Multi-Task Learning

Multi-task learning (MTL) improves prediction performance in different
contexts by learning models jointly on multiple different, but related tasks.
Network data, which are a priori data with a rich relational structure, provide
an important context for applying MTL. In particular, the explicit relational
structure implies that network data is not i.i.d. data. Network data also often
comes with significant metadata (i.e., attributes) associated with each entity
(node). Moreover, due to the diversity and variation in network data (e.g.,
multi-relational links or multi-category entities), various tasks can be
performed and often a rich correlation exists between them. Learning algorithms
should exploit all of these additional sources of information for better
performance. In this work we take a metric-learning point of view for the MTL
problem in the network context. Our approach builds on structure preserving
metric learning (SPML). In particular SPML learns a Mahalanobis distance metric
for node attributes using network structure as supervision, so that the learned
distance function encodes the structure and can be used to predict link
patterns from attributes. SPML is described for single-task learning on single
network. Herein, we propose a multi-task version of SPML, abbreviated as
MT-SPML, which is able to learn across multiple related tasks on multiple
networks via shared intermediate parametrization. MT-SPML learns a specific
metric for each task and a common metric for all tasks. The task correlation is
carried through the common metric and the individual metrics encode task
specific information. When combined together, they are structure-preserving
with respect to individual tasks. MT-SPML works on general networks, thus is
suitable for a wide variety of problems. In experiments, we challenge MT-SPML
on two real-word problems, where MT-SPML achieves significant improvement.
An Improved Tracking using IMU and Vision Fusion for Mobile Augmented Reality Applications
Authors: Kriti Kumar, Ashley Varghese, Pavan K Reddy, N Narendra, Prashanth Swamy, M Girish Chandra, P Balamuralidhar
Date: 2014-11-10 00:00:00

Mobile Augmented Reality (MAR) is becoming an important cyber-physical system
application given the ubiquitous availability of mobile phones. With the need
to operate in unprepared environments, accurate and robust registration and
tracking has become an important research problem to solve. In fact, when MAR
is used for tele-interactive applications involving large distances, say from
an accident site to insurance office, tracking at both the ends is desirable
and further it is essential to appropriately fuse inertial and vision sensors
data. In this paper, we present results and discuss some insights gained in
marker-less tracking during the development of a prototype pertaining to an
example use case related to breakdown or damage assessment of a vehicle. The
novelty of this paper is in bringing together different components and modules
with appropriate enhancements towards a complete working system.
Conditional Random Field Autoencoders for Unsupervised Structured Prediction
Authors: Waleed Ammar, Chris Dyer, Noah A. Smith
Date: 2014-11-05 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: MULTI-VIEW LEARNING, Prediction, Structured Prediction, Word Alignment

We introduce a framework for unsupervised learning of structured predictors
with overlapping, global features. Each input's latent representation is
predicted conditional on the observable data using a feature-rich conditional
random field. Then a reconstruction of the input is (re)generated, conditional
on the latent structure, using models for which maximum likelihood estimation
has a closed-form. Our autoencoder formulation enables efficient learning
without making unrealistic independence assumptions or restricting the kinds of
features that can be used. We illustrate insightful connections to traditional
autoencoders, posterior regularization and multi-view learning. We show
competitive results with instantiations of the model for two canonical NLP
tasks: part-of-speech induction and bitext word alignment, and show that
training our model can be substantially more efficient than comparable
feature-rich baselines.
N$^3$LARS: Minimum Redundancy Maximum Relevance Feature Selection for Large and High-dimensional Data
Authors: Makoto Yamada, Avishek Saha, Hua Ouyang, Dawei Yin, Yi Chang
Date: 2014-11-10 00:00:00
Tasks: Distributed Computing, feature selection, regression

We propose a feature selection method that finds non-redundant features from
a large and high-dimensional data in nonlinear way. Specifically, we propose a
nonlinear extension of the non-negative least-angle regression (LARS) called
N${}^3$LARS, where the similarity between input and output is measured through
the normalized version of the Hilbert-Schmidt Independence Criterion (HSIC). An
advantage of N${}^3$LARS is that it can easily incorporate with map-reduce
frameworks such as Hadoop and Spark. Thus, with the help of distributed
computing, a set of features can be efficiently selected from a large and
high-dimensional data. Moreover, N${}^3$LARS is a convex method and can find a
global optimum solution. The effectiveness of the proposed method is first
demonstrated through feature selection experiments for classification and
regression with small and high-dimensional datasets. Finally, we evaluate our
proposed method over a large and high-dimensional biology dataset.
Modeling Word Relatedness in Latent Dirichlet Allocation
Authors: Xun Wang
Date: 2014-11-10 00:00:00
Tasks: Topic Models

Standard LDA model suffers the problem that the topic assignment of each word
is independent and word correlation hence is neglected. To address this
problem, in this paper, we propose a model called Word Related Latent Dirichlet
Allocation (WR-LDA) by incorporating word correlation into LDA topic models.
This leads to new capabilities that standard LDA model does not have such as
estimating infrequently occurring words or multi-language topic modeling.
Experimental results demonstrate the effectiveness of our model compared with
standard LDA.
Model-Parallel Inference for Big Topic Models
Authors: Xun Zheng, Jin Kyu Kim, Qirong Ho, Eric P. Xing
Date: 2014-11-10 00:00:00
Tasks: model, Topic Models

In real world industrial applications of topic modeling, the ability to
capture gigantic conceptual space by learning an ultra-high dimensional topical
representation, i.e., the so-called "big model", is becoming the next
desideratum after enthusiasms on "big data", especially for fine-grained
downstream tasks such as online advertising, where good performances are
usually achieved by regression-based predictors built on millions if not
billions of input features. The conventional data-parallel approach for
training gigantic topic models turns out to be rather inefficient in utilizing
the power of parallelism, due to the heavy dependency on a centralized image of
"model". Big model size also poses another challenge on the storage, where
available model size is bounded by the smallest RAM of nodes. To address these
issues, we explore another type of parallelism, namely model-parallelism, which
enables training of disjoint blocks of a big topic model in parallel. By
integrating data-parallelism with model-parallelism, we show that dependencies
between distributed elements can be handled seamlessly, achieving not only
faster convergence but also an ability to tackle significantly bigger model
size. We describe an architecture for model-parallel inference of LDA, and
present a variant of collapsed Gibbs sampling algorithm tailored for it.
Experimental results demonstrate the ability of this system to handle topic
modeling with unprecedented amount of 200 billion model variables only on a
low-end cluster with very limited computational resources and bandwidth.
Trade-Offs in Exploiting Body Morphology for Control: from Simple Bodies and Model-Based Control to Complex Bodies with Model-Free Distributed Control Schemes
Authors: Matej Hoffmann, Vincent C. Müller
Date: 2014-11-09 00:00:00

Tailoring the design of robot bodies for control purposes is implicitly
performed by engineers, however, a methodology or set of tools is largely
absent and optimization of morphology (shape, material properties of robot
bodies, etc.) is lagging behind the development of controllers. This has become
even more prominent with the advent of compliant, deformable or "soft" bodies.
These carry substantial potential regarding their exploitation for
control---sometimes referred to as "morphological computation" in the sense of
offloading computation needed for control to the body. Here, we will argue in
favor of a dynamical systems rather than computational perspective on the
problem. Then, we will look at the pros and cons of simple vs. complex bodies,
critically reviewing the attractive notion of "soft" bodies automatically
taking over control tasks. We will address another key dimension of the design
space---whether model-based control should be used and to what extent it is
feasible to develop faithful models for different morphologies.
Applications of sampling Kantorovich operators to thermographic images for seismic engineering
Authors: Danilo Costarelli, Federico Cluni, Anna Maria Minotti, Gianluca Vinti
Date: 2014-11-09 00:00:00

In this paper, we present some applications of the multivariate sampling
Kantorovich operators $S_w$ to seismic engineering. The mathematical theory of
these operators, both in the space of continuous functions and in Orlicz
spaces, show how it is possible to approximate/reconstruct multivariate
signals, such as images. In particular, to obtain applications for
thermographic images a mathematical algorithm is developed using MATLAB and
matrix calculus. The setting of Orlicz spaces is important since allow us to
reconstruct not necessarily continuous signals by means of $S_w$. The
reconstruction of thermographic images of buildings by our sampling Kantorovich
algorithm allow us to obtain models for the simulation of the behavior of
structures under seismic action. We analyze a real world case study in term of
structural analysis and we compare the behavior of the building under seismic
action using various models.
Abnormal Object Recognition: A Comprehensive Study
Authors: Babak Saleh, Ali Farhadi, Ahmed Elgammal
Date: 2014-11-09 00:00:00
Tasks: Anomaly Detection, Object, Object Recognition

When describing images, humans tend not to talk about the obvious, but rather
mention what they find interesting. We argue that abnormalities and deviations
from typicalities are among the most important components that form what is
worth mentioning. In this paper we introduce the abnormality detection as a
recognition problem and show how to model typicalities and, consequently,
meaningful deviations from prototypical properties of categories. Our model can
recognize abnormalities and report the main reasons of any recognized
abnormality. We introduce the abnormality detection dataset and show
interesting results on how to reason about abnormalities.
Estimating Fire Weather Indices via Semantic Reasoning over Wireless Sensor Network Data Streams
Authors: Lianli Gao, Michael Bruenig, Jane Hunter
Date: 2014-11-09 00:00:00
Tasks: Management

Wildfires are frequent, devastating events in Australia that regularly cause
significant loss of life and widespread property damage. Fire weather indices
are a widely-adopted method for measuring fire danger and they play a
significant role in issuing bushfire warnings and in anticipating demand for
bushfire management resources. Existing systems that calculate fire weather
indices are limited due to low spatial and temporal resolution. Localized
wireless sensor networks, on the other hand, gather continuous sensor data
measuring variables such as air temperature, relative humidity, rainfall and
wind speed at high resolutions. However, using wireless sensor networks to
estimate fire weather indices is a challenge due to data quality issues, lack
of standard data formats and lack of agreement on thresholds and methods for
calculating fire weather indices. Within the scope of this paper, we propose a
standardized approach to calculating Fire Weather Indices (a.k.a. fire danger
ratings) and overcome a number of the challenges by applying Semantic Web
Technologies to the processing of data streams from a wireless sensor network
deployed in the Springbrook region of South East Queensland. This paper
describes the underlying ontologies, the semantic reasoning and the Semantic
Fire Weather Index (SFWI) system that we have developed to enable domain
experts to specify and adapt rules for calculating Fire Weather Indices. We
also describe the Web-based mapping interface that we have developed, that
enables users to improve their understanding of how fire weather indices vary
over time within a particular region.Finally, we discuss our evaluation results
that indicate that the proposed system outperforms state-of-the-art techniques
in terms of accuracy, precision and query performance.
Stacked Quantizers for Compositional Vector Compression
Authors: Julieta Martinez, Holger H. Hoos, James J. Little
Date: 2014-11-08 00:00:00
Tasks: Quantization

Recently, Babenko and Lempitsky introduced Additive Quantization (AQ), a
generalization of Product Quantization (PQ) where a non-independent set of
codebooks is used to compress vectors into small binary codes. Unfortunately,
under this scheme encoding cannot be done independently in each codebook, and
optimal encoding is an NP-hard problem. In this paper, we observe that PQ and
AQ are both compositional quantizers that lie on the extremes of the codebook
dependence-independence assumption, and explore an intermediate approach that
exploits a hierarchical structure in the codebooks. This results in a method
that achieves quantization error on par with or lower than AQ, while being
several orders of magnitude faster. We perform a complexity analysis of PQ, AQ
and our method, and evaluate our approach on standard benchmarks of SIFT and
GIST descriptors, as well as on new datasets of features obtained from
state-of-the-art convolutional neural networks.
Evolving intraday foreign exchange trading strategies utilizing multiple instruments price series
Authors: Simone Cirillo, Stefan Lloyd, Peter Nordin
Date: 2014-11-08 00:00:00

We propose a Genetic Programming architecture for the generation of foreign
exchange trading strategies. The system's principal features are the evolution
of free-form strategies which do not rely on any prior models and the
utilization of price series from multiple instruments as input data. This
latter feature constitutes an innovation with respect to previous works
documented in literature. In this article we utilize Open, High, Low, Close bar
data at a 5 minutes frequency for the AUD.USD, EUR.USD, GBP.USD and USD.JPY
currency pairs. We will test the implementation analyzing the in-sample and
out-of-sample performance of strategies for trading the USD.JPY obtained across
multiple algorithm runs. We will also evaluate the differences between
strategies selected according to two different criteria: one relies on the
fitness obtained on the training set only, the second one makes use of an
additional validation dataset. Strategy activity and trade accuracy are
remarkably stable between in and out of sample results. From a profitability
aspect, the two criteria both result in strategies successful on out-of-sample
data but exhibiting different characteristics. The overall best performing
out-of-sample strategy achieves a yearly return of 19%.
Fast Mesh-Based Medical Image Registration
Authors: Ahmadreza Baghaie, Zeyun Yu, Roshan M. D'souza
Date: 2014-11-08 00:00:00
Tasks: Image Registration, Medical Image Registration

In this paper a fast triangular mesh based registration method is proposed.
Having Template and Reference images as inputs, the template image is
triangulated using a content adaptive mesh generation algorithm. Considering
the pixel values at mesh nodes, interpolated using spline interpolation method
for both of the images, the energy functional needed for image registration is
minimized. The minimization process was achieved using a mesh based
discretization of the distance measure and regularization term which resulted
in a sparse system of linear equations, which due to the smaller size in
comparison to the pixel-wise registration method, can be solved directly. Mean
Squared Difference (MSD) is used as a metric for evaluating the results. Using
the mesh based technique, higher speed was achieved compared to pixel-based
curvature registration technique with fast DCT solver. The implementation was
done in MATLAB without any specific optimization. Higher speeds can be achieved
using C/C++ implementations.