pwc-archive/papers-with-abstracts huggingface dataset:
Novelty Learning via Collaborative Proximity Filtering
Authors: Arun Kumar, Paul Schrater
Date: 2016-10-21 00:00:00
Tasks: Recommendation Systems

The vast majority of recommender systems model preferences as static or
slowly changing due to observable user experience. However, spontaneous changes
in user preferences are ubiquitous in many domains like media consumption and
key factors that drive changes in preferences are not directly observable.
These latent sources of preference change pose new challenges. When systems do
not track and adapt to users' tastes, users lose confidence and trust,
increasing the risk of user churn. We meet these challenges by developing a
model of novelty preferences that learns and tracks latent user tastes. We
combine three innovations: a new measure of item similarity based on patterns
of consumption co-occurrence; model for {\em spontaneous} changes in
preferences; and a learning agent that tracks each user's dynamic preferences
and learns individualized policies for variety. The resulting framework
adaptively provides users with novelty tailored to their preferences for change
per se.
Bit-pragmatic Deep Neural Network Computing
Authors: J. Albericio, P. Judd, A. Delmás, S. Sharify, A. Moshovos
Date: 2016-10-20 00:00:00

We quantify a source of ineffectual computations when processing the
multiplications of the convolutional layers in Deep Neural Networks (DNNs) and
propose Pragmatic (PRA), an architecture that exploits it improving performance
and energy efficiency. The source of these ineffectual computations is best
understood in the context of conventional multipliers which generate internally
multiple terms, that is, products of the multiplicand and powers of two, which
added together produce the final product [1]. At runtime, many of these terms
are zero as they are generated when the multiplicand is combined with the
zero-bits of the multiplicator. While conventional bit-parallel multipliers
calculate all terms in parallel to reduce individual product latency, PRA
calculates only the non-zero terms using a) on-the-fly conversion of the
multiplicator representation into an explicit list of powers of two, and b)
hybrid bit-parallel multplicand/bit-serial multiplicator processing units. PRA
exploits two sources of ineffectual computations: 1) the aforementioned zero
product terms which are the result of the lack of explicitness in the
multiplicator representation, and 2) the excess in the representation precision
used for both multiplicants and multiplicators, e.g., [2]. Measurements
demonstrate that for the convolutional layers, a straightforward variant of PRA
improves performance by 2.6x over the DaDiaNao (DaDN) accelerator [3] and by
1.4x over STR [4]. Similarly, PRA improves energy efficiency by 28% and 10% on
average compared to DaDN and STR. An improved cross lane synchronication scheme
boosts performance improvements to 3.1x over DaDN. Finally, Pragmatic benefits
persist even with an 8-bit quantized representation [5].
Generalization bound for kernel similarity learning
Authors: Michael Rabadi
Date: 2016-10-12 00:00:00
Tasks: regression

Similarity learning has received a large amount of interest and is an
important tool for many scientific and industrial applications. In this
framework, we wish to infer the distance (similarity) between points with
respect to an arbitrary distance function $d$. Here, we formulate the problem
as a regression from a feature space $\mathcal{X}$ to an arbitrary vector space
$\mathcal{Y}$, where the Euclidean distance is proportional to $d$. We then
give Rademacher complexity bounds on the generalization error. We find that
with high probability, the complexity is bounded by the maximum of the radius
of $\mathcal{X}$ and the radius of $\mathcal{Y}$.
Internet of Things Applications: Animal Monitoring with Unmanned Aerial Vehicle
Authors: Jun Xu, Gurkan Solmaz, Rouhollah Rahmatizadeh, Damla Turgut, Ladislau Boloni
Date: 2016-10-17 00:00:00
Tasks: Q-Learning, Traveling Salesman Problem

In animal monitoring applications, both animal detection and their movement
prediction are major tasks. While a variety of animal monitoring strategies
exist, most of them rely on mounting devices. However, in real world, it is
difficult to find these animals and install mounting devices. In this paper, we
propose an animal monitoring application by utilizing wireless sensor networks
(WSNs) and unmanned aerial vehicle (UAV). The objective of the application is
to detect locations of endangered species in large-scale wildlife areas and
monitor movement of animals without any attached devices. In this application,
sensors deployed throughout the observation area are responsible for gathering
animal information. The UAV flies above the observation area and collects the
information from sensors. To achieve the information efficiently, we propose a
path planning approach for the UAV based on a Markov decision process (MDP)
model. The UAV receives a certain amount of reward from an area if some animals
are detected at that location. We solve the MDP using Q-learning such that the
UAV prefers going to those areas that animals are detected before. Meanwhile,
the UAV explores other areas as well to cover the entire network and detects
changes in the animal positions. We first define the mathematical model
underlying the animal monitoring problem in terms of the value of information
(VoI) and rewards. We propose a network model including clusters of sensor
nodes and a single UAV that acts as a mobile sink and visits the clusters.
Then, one MDP-based path planning approach is designed to maximize the VoI
while reducing message delays. The effectiveness of the proposed approach is
evaluated using two real-world movement datasets of zebras and leopard.
Simulation results show that our approach outperforms greedy, random heuristics
and the path planning based on the traveling salesman problem.
Nonlinear Structural Vector Autoregressive Models for Inferring Effective Brain Network Connectivity
Authors: Yanning Shen, Brian Baingana, Georgios B. Giannakis
Date: 2016-10-20 00:00:00
Tasks: Dimensionality Reduction, Time Series, Time Series Analysis

Structural equation models (SEMs) and vector autoregressive models (VARMs)
are two broad families of approaches that have been shown useful in effective
brain connectivity studies. While VARMs postulate that a given region of
interest in the brain is directionally connected to another one by virtue of
time-lagged influences, SEMs assert that causal dependencies arise due to
contemporaneous effects, and may even be adopted when nodal measurements are
not necessarily multivariate time series. To unify these complementary
perspectives, linear structural vector autoregressive models (SVARMs) that
leverage both contemporaneous and time-lagged nodal data have recently been put
forth. Albeit simple and tractable, linear SVARMs are quite limited since they
are incapable of modeling nonlinear dependencies between neuronal time series.
To this end, the overarching goal of the present paper is to considerably
broaden the span of linear SVARMs by capturing nonlinearities through kernels,
which have recently emerged as a powerful nonlinear modeling framework in
canonical machine learning tasks, e.g., regression, classification, and
dimensionality reduction. The merits of kernel-based methods are extended here
to the task of learning the effective brain connectivity, and an efficient
regularized estimator is put forth to leverage the edge sparsity inherent to
real-world complex networks. Judicious kernel choice from a preselected
dictionary of kernels is also addressed using a data-driven approach. Extensive
numerical tests on ECoG data captured through a study on epileptic seizures
demonstrate that it is possible to unveil previously unknown causal links
between brain regions of interest.
Neural Machine Translation with Characters and Hierarchical Encoding
Authors: Alexander Rosenberg Johansen, Jonas Meinertz Hansen, Elias Khazen Obeid, Casper Kaae Sønderby, Ole Winther
Date: 2016-10-20 00:00:00
Tasks: Decoder, Machine Translation, Translation

Most existing Neural Machine Translation models use groups of characters or
whole words as their unit of input and output. We propose a model with a
hierarchical char2word encoder, that takes individual characters both as input
and output. We first argue that this hierarchical representation of the
character encoder reduces computational complexity, and show that it improves
translation performance. Secondly, by qualitatively studying attention plots
from the decoder we find that the model learns to compress common words into a
single embedding whereas rare words, such as names and places, are represented
character by character.
Lexicons and Minimum Risk Training for Neural Machine Translation: NAIST-CMU at WAT2016
Authors: Graham Neubig
Date: 2016-10-20 00:00:00
Proceeding: WS 2016 12
Tasks: Machine Translation, NMT, Translation

This year, the Nara Institute of Science and Technology (NAIST)/Carnegie
Mellon University (CMU) submission to the Japanese-English translation track of
the 2016 Workshop on Asian Translation was based on attentional neural machine
translation (NMT) models. In addition to the standard NMT model, we make a
number of improvements, most notably the use of discrete translation lexicons
to improve probability estimates, and the use of minimum risk training to
optimize the MT system for BLEU score. As a result, our system achieved the
highest translation evaluation scores for the task.
Jointly Learning to Align and Convert Graphemes to Phonemes with Neural Attention Models
Authors: Shubham Toshniwal, Karen Livescu
Date: 2016-10-20 00:00:00
Tasks: Decoder, Grapheme-to-Phoneme Conversion

We propose an attention-enabled encoder-decoder model for the problem of
grapheme-to-phoneme conversion. Most previous work has tackled the problem via
joint sequence models that require explicit alignments for training. In
contrast, the attention-enabled encoder-decoder model allows for jointly
learning to align and convert characters to phonemes. We explore different
types of attention models, including global and local attention, and our best
models achieve state-of-the-art results on three standard data sets (CMUDict,
Pronlex, and NetTalk).
Authorship Attribution Based on Life-Like Network Automata
Authors: Jeaneth Machicao, Edilson A. Corrêa Jr., Gisele H. B. Miranda, Diego R. Amancio, Odemir M. Bruno
Date: 2016-10-20 00:00:00
Tasks: Authorship Attribution, Lemmatization

The authorship attribution is a problem of considerable practical and
technical interest. Several methods have been designed to infer the authorship
of disputed documents in multiple contexts. While traditional statistical
methods based solely on word counts and related measurements have provided a
simple, yet effective solution in particular cases; they are prone to
manipulation. Recently, texts have been successfully modeled as networks, where
words are represented by nodes linked according to textual similarity
measurements. Such models are useful to identify informative topological
patterns for the authorship recognition task. However, there is no consensus on
which measurements should be used. Thus, we proposed a novel method to
characterize text networks, by considering both topological and dynamical
aspects of networks. Using concepts and methods from cellular automata theory,
we devised a strategy to grasp informative spatio-temporal patterns from this
model. Our experiments revealed an outperformance over traditional analysis
relying only on topological measurements. Remarkably, we have found a
dependence of pre-processing steps (such as the lemmatization) on the obtained
results, a feature that has mostly been disregarded in related works. The
optimized results obtained here pave the way for a better characterization of
textual networks.
An Image Dataset of Text Patches in Everyday Scenes
Authors: Ahmed Ibrahim, A. Lynn Abbott, Mohamed E. Hussein
Date: 2016-10-20 00:00:00
Tasks: Text Detection

This paper describes a dataset containing small images of text from everyday
scenes. The purpose of the dataset is to support the development of new
automated systems that can detect and analyze text. Although much research has
been devoted to text detection and recognition in scanned documents, relatively
little attention has been given to text detection in other types of images,
such as photographs that are posted on social-media sites. This new dataset,
known as COCO-Text-Patch, contains approximately 354,000 small images that are
each labeled as "text" or "non-text". This dataset particularly addresses the
problem of text verification, which is an essential stage in the end-to-end
text detection and recognition pipeline. In order to evaluate the utility of
this dataset, it has been used to train two deep convolution neural networks to
distinguish text from non-text. One network is inspired by the GoogLeNet
architecture, and the second one is based on CaffeNet. Accuracy levels of 90.2%
and 90.9% were obtained using the two networks, respectively. All of the
images, source code, and deep-learning trained models described in this paper
will be publicly available
Utilization of Deep Reinforcement Learning for saccadic-based object visual search
Authors: Tomasz Kornuta, Kamil Rocki
Date: 2016-10-20 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The paper focuses on the problem of learning saccades enabling visual object
search. The developed system combines reinforcement learning with a neural
network for learning to predict the possible outcomes of its actions. We
validated the solution in three types of environment consisting of
(pseudo)-randomly generated matrices of digits. The experimental verification
is followed by the discussion regarding elements required by systems mimicking
the fovea movement and possible further research directions.
An Ensemble of Adaptive Neuro-Fuzzy Kohonen Networks for Online Data Stream Fuzzy Clustering
Authors: Zhengbing Hu, Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko, Olena O. Boiko
Date: 2016-10-20 00:00:00
Tasks: Clustering

A new approach to data stream clustering with the help of an ensemble of
adaptive neuro-fuzzy systems is proposed. The proposed ensemble is formed with
adaptive neuro-fuzzy self-organizing Kohonen maps in a parallel processing
mode. A final result is chosen by the best neuro-fuzzy self-organizing Kohonen
map.
An Evolving Neuro-Fuzzy System with Online Learning/Self-learning
Authors: Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko, Anastasiia O. Deineko
Date: 2016-10-20 00:00:00
Tasks: Self-Learning

An architecture of a new neuro-fuzzy system is proposed. The basic idea of
this approach is to tune both synaptic weights and membership functions with
the help of the supervised learning and self-learning paradigms. The approach
to solving the problem has to do with evolving online neuro-fuzzy systems that
can process data under uncertainty conditions. The results prove the
effectiveness of the developed architecture and the learning procedure.
Adaptive Forecasting of Non-Stationary Nonlinear Time Series Based on the Evolving Weighted Neuro-Neo-Fuzzy-ANARX-Model
Authors: Zhengbing Hu, Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko, Olena O. Boiko
Date: 2016-10-20 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Forecasting

An evolving weighted neuro-neo-fuzzy-ANARX model and its learning procedures
are introduced in the article. This system is basically used for time series
forecasting. This system may be considered as a pool of elements that process
data in a parallel manner. The proposed evolving system may provide online
processing data streams.
A Multidimensional Cascade Neuro-Fuzzy System with Neuron Pool Optimization in Each Cascade
Authors: Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko, Daria S. Kopaliani
Date: 2016-10-20 00:00:00
Tasks: Time Series, Time Series Analysis

A new architecture and learning algorithms for the multidimensional hybrid
cascade neural network with neuron pool optimization in each cascade are
proposed in this paper. The proposed system differs from the well-known cascade
systems in its capability to process multidimensional time series in an online
mode, which makes it possible to process non-stationary stochastic and chaotic
signals with the required accuracy. Compared to conventional analogs, the
proposed system provides computational simplicity and possesses both tracking
and filtering capabilities.
An Evolving Cascade System Based on A Set Of Neo Fuzzy Nodes
Authors: Zhengbing Hu, Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko, Olena O. Boiko
Date: 2016-10-20 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Forecasting

Neo-fuzzy elements are used as nodes for an evolving cascade system. The
proposed system can tune both its parameters and architecture in an online
mode. It can be used for solving a wide range of Data Mining tasks (namely time
series forecasting). The evolving cascade system with neo-fuzzy nodes can
process rather large data sets with high speed and effectiveness.
An Extended Neo-Fuzzy Neuron and its Adaptive Learning Algorithm
Authors: Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko, Daria S. Kopaliani
Date: 2016-10-20 00:00:00

A modification of the neo-fuzzy neuron is proposed (an extended neo-fuzzy
neuron (ENFN)) that is characterized by improved approximating properties. An
adaptive learning algorithm is proposed that has both tracking and smoothing
properties. An ENFN distinctive feature is its computational simplicity
compared to other artificial neural networks and neuro-fuzzy systems.
Generalized Interval-valued OWA Operators with Interval Weights Derived from Interval-valued Overlap Functions
Authors: Benjamin Bedregal, Humberto Bustince, Eduardo Palmeira, Graçaliz Pereira Dimuro, Javier Fernandez
Date: 2016-10-20 00:00:00

In this work we extend to the interval-valued setting the notion of an
overlap functions and we discuss a method which makes use of interval-valued
overlap functions for constructing OWA operators with interval-valued weights.
. Some properties of interval-valued overlap functions and the derived
interval-valued OWA operators are analysed. We specially focus on the
homogeneity and migrativity properties.
Efficient Estimation of Compressible State-Space Models with Application to Calcium Signal Deconvolution
Authors: Abbas Kazemipour, Ji Liu, Patrick Kanold, Min Wu, Behtash Babadi
Date: 2016-10-20 00:00:00
Tasks: compressed sensing, State Estimation, State Space Models

In this paper, we consider linear state-space models with compressible
innovations and convergent transition matrices in order to model
spatiotemporally sparse transient events. We perform parameter and state
estimation using a dynamic compressed sensing framework and develop an
efficient solution consisting of two nested Expectation-Maximization (EM)
algorithms. Under suitable sparsity assumptions on the innovations, we prove
recovery guarantees and derive confidence bounds for the state estimates. We
provide simulation studies as well as application to spike deconvolution from
calcium imaging data which verify our theoretical results and show significant
improvement over existing algorithms.
Change-point Detection Methods for Body-Worn Video
Authors: Stephanie Allen, David Madras, Ye Ye, Greg Zanotti
Date: 2016-10-20 00:00:00
Tasks: Change Point Detection, Scene Classification

Body-worn video (BWV) cameras are increasingly utilized by police departments
to provide a record of police-public interactions. However, large-scale BWV
deployment produces terabytes of data per week, necessitating the development
of effective computational methods to identify salient changes in video. In
work carried out at the 2016 RIPS program at IPAM, UCLA, we present a novel
two-stage framework for video change-point detection. First, we employ
state-of-the-art machine learning methods including convolutional neural
networks and support vector machines for scene classification. We then develop
and compare change-point detection algorithms utilizing mean squared-error
minimization, forecasting methods, hidden Markov models, and maximum likelihood
estimation to identify noteworthy changes. We test our framework on detection
of vehicle exits and entrances in a BWV data set provided by the Los Angeles
Police Department and achieve over 90% recall and nearly 70% precision --
demonstrating robustness to rapid scene changes, extreme luminance differences,
and frequent camera occlusions.
Hardware-oriented Approximation of Convolutional Neural Networks
Authors: Philipp Gysel, Mohammad Motamedi, Soheil Ghiasi
Date: 2016-04-11 00:00:00

High computational complexity hinders the widespread usage of Convolutional
Neural Networks (CNNs), especially in mobile devices. Hardware accelerators are
arguably the most promising approach for reducing both execution time and power
consumption. One of the most important steps in accelerator development is
hardware-oriented model approximation. In this paper we present Ristretto, a
model approximation framework that analyzes a given CNN with respect to
numerical resolution used in representing weights and outputs of convolutional
and fully connected layers. Ristretto can condense models by using fixed point
arithmetic and representation instead of floating point. Moreover, Ristretto
fine-tunes the resulting fixed point network. Given a maximum error tolerance
of 1%, Ristretto can successfully condense CaffeNet and SqueezeNet to 8-bit.
The code for Ristretto is available.
Exploiting inter-image similarity and ensemble of extreme learners for fixation prediction using deep features
Authors: Hamed R. -Tavakoli, Ali Borji, Jorma Laaksonen, Esa Rahtu
Date: 2016-10-20 00:00:00

This paper presents a novel fixation prediction and saliency modeling
framework based on inter-image similarities and ensemble of Extreme Learning
Machines (ELM). The proposed framework is inspired by two observations, 1) the
contextual information of a scene along with low-level visual cues modulates
attention, 2) the influence of scene memorability on eye movement patterns
caused by the resemblance of a scene to a former visual experience. Motivated
by such observations, we develop a framework that estimates the saliency of a
given image using an ensemble of extreme learners, each trained on an image
similar to the input image. That is, after retrieving a set of similar images
for a given image, a saliency predictor is learnt from each of the images in
the retrieved image set using an ELM, resulting in an ensemble. The saliency of
the given image is then measured in terms of the mean of predicted saliency
value by the ensemble's members.
DOLDA - a regularized supervised topic model for high-dimensional multi-class regression
Authors: Måns Magnusson, Leif Jonsson, Mattias Villani
Date: 2016-01-31 00:00:00
Tasks: General Classification, Multi-class Classification, regression, Variable Selection

Generating user interpretable multi-class predictions in data rich
environments with many classes and explanatory covariates is a daunting task.
We introduce Diagonal Orthant Latent Dirichlet Allocation (DOLDA), a supervised
topic model for multi-class classification that can handle both many classes as
well as many covariates. To handle many classes we use the recently proposed
Diagonal Orthant (DO) probit model (Johndrow et al., 2013) together with an
efficient Horseshoe prior for variable selection/shrinkage (Carvalho et al.,
2010). We propose a computationally efficient parallel Gibbs sampler for the
new model. An important advantage of DOLDA is that learned topics are directly
connected to individual classes without the need for a reference class. We
evaluate the model's predictive accuracy on two datasets and demonstrate
DOLDA's advantage in interpreting the generated predictions.