pwc-archive/papers-with-abstracts huggingface dataset:
Encoding monotonic multi-set preferences using CI-nets: preliminary report
Authors: Martin Diller, Anthony Hunter
Date: 2016-11-09 00:00:00

CP-nets and their variants constitute one of the main AI approaches for
specifying and reasoning about preferences. CI-nets, in particular, are a
CP-inspired formalism for representing ordinal preferences over sets of goods,
which are typically required to be monotonic.
  Considering also that goods often come in multi-sets rather than sets, a
natural question is whether CI-nets can be used more or less directly to encode
preferences over multi-sets. We here provide some initial ideas on how to
achieve this, in the sense that at least a restricted form of reasoning on our
framework, which we call "confined reasoning", can be efficiently reduced to
reasoning on CI-nets. Our framework nevertheless allows for encoding
preferences over multi-sets with unbounded multiplicities. We also show the
extent to which it can be used to represent preferences where multiplicites of
the goods are not stated explicitly ("purely qualitative preferences") as well
as a potential use of our generalization of CI-nets as a component of a recent
system for evidence aggregation.
Deep fusion of visual signatures for client-server facial analysis
Authors: Binod Bhattarai, Gaurav Sharma, Frederic Jurie
Date: 2016-11-01 00:00:00

Facial analysis is a key technology for enabling human-machine interaction.
In this context, we present a client-server framework, where a client transmits
the signature of a face to be analyzed to the server, and, in return, the
server sends back various information describing the face e.g. is the person
male or female, is she/he bald, does he have a mustache, etc. We assume that a
client can compute one (or a combination) of visual features; from very simple
and efficient features, like Local Binary Patterns, to more complex and
computationally heavy, like Fisher Vectors and CNN based, depending on the
computing resources available. The challenge addressed in this paper is to
design a common universal representation such that a single merged signature is
transmitted to the server, whatever be the type and number of features computed
by the client, ensuring nonetheless an optimal performance. Our solution is
based on learning of a common optimal subspace for aligning the different face
features and merging them into a universal signature. We have validated the
proposed method on the challenging CelebA dataset, on which our method
outperforms existing state-of-the-art methods when rich representation is
available at test time, while giving competitive performance when only simple
signatures (like LBP) are available at test time due to resource constraints on
the client.
Audio Visual Speech Recognition using Deep Recurrent Neural Networks
Authors: Abhinav Thanda, Shankar M Venkatesan
Date: 2016-11-09 00:00:00
Tasks: Audio-Visual Speech Recognition, Automatic Speech Recognition, Automatic Speech Recognition (ASR), Dimensionality Reduction, speech-recognition, Speech Recognition, Visual Speech Recognition

In this work, we propose a training algorithm for an audio-visual automatic
speech recognition (AV-ASR) system using deep recurrent neural network
(RNN).First, we train a deep RNN acoustic model with a Connectionist Temporal
Classification (CTC) objective function. The frame labels obtained from the
acoustic model are then used to perform a non-linear dimensionality reduction
of the visual features using a deep bottleneck network. Audio and visual
features are fused and used to train a fusion RNN. The use of bottleneck
features for visual modality helps the model to converge properly during
training. Our system is evaluated on GRID corpus. Our results show that
presence of visual modality gives significant improvement in character error
rate (CER) at various levels of noise even when the model is trained without
noisy data. We also provide a comparison of two fusion methods: feature fusion
and decision fusion.
Gaussian process regression can turn non-uniform and undersampled diffusion MRI data into diffusion spectrum imaging
Authors: Jens Sjölund, Anders Eklund, Evren Özarslan, Hans Knutsson
Date: 2016-11-09 00:00:00
Tasks: Diffusion  MRI, regression, Unity

We propose to use Gaussian process regression to accurately estimate the
diffusion MRI signal at arbitrary locations in q-space. By estimating the
signal on a grid, we can do synthetic diffusion spectrum imaging:
reconstructing the ensemble averaged propagator (EAP) by an inverse Fourier
transform. We also propose an alternative reconstruction method guaranteeing a
nonnegative EAP that integrates to unity. The reconstruction is validated on
data simulated from two Gaussians at various crossing angles. Moreover, we
demonstrate on non-uniformly sampled in vivo data that the method is far
superior to linear interpolation, and allows a drastic undersampling of the
data with only a minor loss of accuracy. We envision the method as a potential
replacement for standard diffusion spectrum imaging, in particular when
acquistion time is limited.
Correlated Random Measures
Authors: Rajesh Ranganath, David Blei
Date: 2015-07-02 00:00:00
Tasks: Variational Inference

We develop correlated random measures, random measures where the atom weights
can exhibit a flexible pattern of dependence, and use them to develop powerful
hierarchical Bayesian nonparametric models. Hierarchical Bayesian nonparametric
models are usually built from completely random measures, a Poisson-process
based construction in which the atom weights are independent. Completely random
measures imply strong independence assumptions in the corresponding
hierarchical model, and these assumptions are often misplaced in real-world
settings. Correlated random measures address this limitation. They model
correlation within the measure by using a Gaussian process in concert with the
Poisson process. With correlated random measures, for example, we can develop a
latent feature model for which we can infer both the properties of the latent
features and their dependency pattern. We develop several other examples as
well. We study a correlated random measure model of pairwise count data. We
derive an efficient variational inference algorithm and show improved
predictive performance on large data sets of documents, web clicks, and
electronic health records.
Old Content and Modern Tools - Searching Named Entities in a Finnish OCRed Historical Newspaper Collection 1771-1910
Authors: Kimmo Kettunen, Eetu Mäkelä, Teemu Ruokolainen, Juha Kuokkala, Laura Löfberg
Date: 2016-11-09 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Optical Character Recognition (OCR)

Named Entity Recognition (NER), search, classification and tagging of names
and name like frequent informational elements in texts, has become a standard
information extraction procedure for textual data. NER has been applied to many
types of texts and different types of entities: newspapers, fiction, historical
records, persons, locations, chemical compounds, protein families, animals etc.
In general a NER system's performance is genre and domain dependent and also
used entity categories vary (Nadeau and Sekine, 2007). The most general set of
named entities is usually some version of three partite categorization of
locations, persons and organizations. In this paper we report first large scale
trials and evaluation of NER with data out of a digitized Finnish historical
newspaper collection Digi. Experiments, results and discussion of this research
serve development of the Web collection of historical Finnish newspapers.
  Digi collection contains 1,960,921 pages of newspaper material from years
1771-1910 both in Finnish and Swedish. We use only material of Finnish
documents in our evaluation. The OCRed newspaper collection has lots of OCR
errors; its estimated word level correctness is about 70-75 % (Kettunen and
P\"a\"akk\"onen, 2016). Our principal NER tagger is a rule-based tagger of
Finnish, FiNER, provided by the FIN-CLARIN consortium. We show also results of
limited category semantic tagging with tools of the Semantic Computing Research
Group (SeCo) of the Aalto University. Three other tools are also evaluated
briefly.
  This research reports first published large scale results of NER in a
historical Finnish OCRed newspaper collection. Results of the research
supplement NER results of other languages with similar noisy data.
Normalizing Flows on Riemannian Manifolds
Authors: Mevlana C. Gemici, Danilo Rezende, Shakir Mohamed
Date: 2016-11-07 00:00:00
Tasks: Density Estimation, Protein Folding

We consider the problem of density estimation on Riemannian manifolds.
Density estimation on manifolds has many applications in fluid-mechanics,
optics and plasma physics and it appears often when dealing with angular
variables (such as used in protein folding, robot limbs, gene-expression) and
in general directional statistics. In spite of the multitude of algorithms
available for density estimation in the Euclidean spaces $\mathbf{R}^n$ that
scale to large n (e.g. normalizing flows, kernel methods and variational
approximations), most of these methods are not immediately suitable for density
estimation in more general Riemannian manifolds. We revisit techniques related
to homeomorphisms from differential geometry for projecting densities to
sub-manifolds and use it to generalize the idea of normalizing flows to more
general Riemannian manifolds. The resulting algorithm is scalable, simple to
implement and suitable for use with automatic differentiation. We demonstrate
concrete examples of this method on the n-sphere $\mathbf{S}^n$.
Online Learning for Wireless Distributed Computing
Authors: Yi-Hsuan Kao, Kwame Wright, Bhaskar Krishnamachari, Fan Bai
Date: 2016-11-09 00:00:00
Tasks: Distributed Computing

There has been a growing interest for Wireless Distributed Computing (WDC),
which leverages collaborative computing over multiple wireless devices. WDC
enables complex applications that a single device cannot support individually.
However, the problem of assigning tasks over multiple devices becomes
challenging in the dynamic environments encountered in real-world settings,
considering that the resource availability and channel conditions change over
time in unpredictable ways due to mobility and other factors. In this paper, we
formulate a task assignment problem as an online learning problem using an
adversarial multi-armed bandit framework. We propose MABSTA, a novel online
learning algorithm that learns the performance of unknown devices and channel
qualities continually through exploratory probing and makes task assignment
decisions by exploiting the gained knowledge. For maximal adaptability, MABSTA
is designed to make no stochastic assumption about the environment. We analyze
it mathematically and provide a worst-case performance guarantee for any
dynamic environment. We also compare it with the optimal offline policy as well
as other baselines via emulations on trace-data obtained from a wireless IoT
testbed, and show that it offers competitive and robust performance in all
cases. To the best of our knowledge, MABSTA is the first online algorithm in
this domain of task assignment problems and provides provable performance
guarantee.
Acquisition of Visual Features Through Probabilistic Spike-Timing-Dependent Plasticity
Authors: Amirhossein Tavanaei, Timothee Masquelier, Anthony S. Maida
Date: 2016-06-03 00:00:00

The final version of this paper has been published in IEEEXplore available at
http://ieeexplore.ieee.org/document/7727213. Please cite this paper as:
Amirhossein Tavanaei, Timothee Masquelier, and Anthony Maida, Acquisition of
visual features through probabilistic spike-timing-dependent plasticity. IEEE
International Joint Conference on Neural Networks. pp. 307-314, IJCNN 2016.
  This paper explores modifications to a feedforward five-layer spiking
convolutional network (SCN) of the ventral visual stream [Masquelier, T.,
Thorpe, S., Unsupervised learning of visual features through spike timing
dependent plasticity. PLoS Computational Biology, 3(2), 247-257]. The original
model showed that a spike-timing-dependent plasticity (STDP) learning algorithm
embedded in an appropriately selected SCN could perform unsupervised feature
discovery. The discovered features where interpretable and could effectively be
used to perform rapid binary decisions in a classifier. In order to study the
robustness of the previous results, the present research examines the effects
of modifying some of the components of the original model. For improved
biological realism, we replace the original non-leaky integrate-and-fire
neurons with Izhikevich-like neurons. We also replace the original STDP rule
with a novel rule that has a probabilistic interpretation. The probabilistic
STDP slightly but significantly improves the performance for both types of
model neurons. Use of the Izhikevich-like neuron was not found to improve
performance although performance was still comparable to the IF neuron. This
shows that the model is robust enough to handle more biologically realistic
neurons. We also conclude that the underlying reasons for stable performance in
the model are preserved despite the overt changes to the explicit components of
the model.
Semi-Supervised Recognition of the Diploglossus Millepunctatus Lizard Species using Artificial Vision Algorithms
Authors: Jhony-Heriberto Giraldo-Zuluaga, Augusto Salazar, Juan M. Daza
Date: 2016-11-09 00:00:00
Tasks: Segmentation

Animal biometrics is an important requirement for monitoring and conservation
tasks. The classical animal biometrics risk the animals' integrity, are
expensive for numerous animals, and depend on expert criterion. The
non-invasive biometrics techniques offer alternatives to manage the
aforementioned problems. In this paper we propose an automatic segmentation and
identification algorithm based on artificial vision algorithms to recognize
Diploglossus millepunctatus. Diploglossus millepunctatus is an endangered
lizard species. The algorithm is based on two stages: automatic segmentation to
remove the subjective evaluation, and one identification stage to reduce the
analysis time. A 82.87% of correct segmentation in average is reached.
Meanwhile the identification algorithm is achieved with euclidean distance
point algorithms such as Iterative Closest Point and Procrustes Analysis. A
performance of 92.99% on the top 1, and a 96.82% on the top 5 is reached. The
developed software, and the database used in this paper are publicly available
for download from the web page of the project.
Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data
Authors: Xinghua Lou, Ken Kansky, Wolfgang Lehrach, CC Laan, Bhaskara Marthi, D. Scott Phoenix, Dileep George
Date: 2016-11-09 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Instance Segmentation, Scene Text Recognition, Semantic Segmentation

We demonstrate that a generative model for object shapes can achieve state of
the art results on challenging scene text recognition tasks, and with orders of
magnitude fewer training images than required for competing discriminative
methods. In addition to transcribing text from challenging images, our method
performs fine-grained instance segmentation of characters. We show that our
model is more robust to both affine transformations and non-affine deformations
compared to previous approaches.
Deep Convolutional Neural Network for 6-DOF Image Localization
Authors: Daoyuan Jia, Yongchi Su, Chunping Li
Date: 2016-11-08 00:00:00
Tasks: Pose Estimation, regression

We present an accurate and robust method for six degree of freedom image
localization. There are two key-points of our method, 1. automatic immense
photo synthesis and labeling from point cloud model and, 2. pose estimation
with deep convolutional neural networks regression. Our model can directly
regresses 6-DOF camera poses from images, accurately describing where and how
it was captured. We achieved an accuracy within 1 meters and 1 degree on our
out-door dataset, which covers about 2 acres on our school campus.
A backward pass through a CNN using a generative model of its activations
Authors: Huayan Wang, Anna Chen, Yi Liu, Dileep George, D. Scott Phoenix
Date: 2016-11-08 00:00:00
Tasks: Object

Neural networks have shown to be a practical way of building a very complex
mapping between a pre-specified input space and output space. For example, a
convolutional neural network (CNN) mapping an image into one of a thousand
object labels is approaching human performance in this particular task. However
the mapping (neural network) does not automatically lend itself to other forms
of queries, for example, to detect/reconstruct object instances, to enforce
top-down signal on ambiguous inputs, or to recover object instances from
occlusion. One way to address these queries is a backward pass through the
network that fuses top-down and bottom-up information. In this paper, we show a
way of building such a backward pass by defining a generative model of the
neural network's activations. Approximate inference of the model would
naturally take the form of a backward pass through the CNN layers, and it
addresses the aforementioned queries in a unified framework.
Inferring low-dimensional microstructure representations using convolutional neural networks
Authors: Nicholas Lubbers, Turab Lookman, Kipton Barros
Date: 2016-11-08 00:00:00
Tasks: BIG-bench Machine Learning

We apply recent advances in machine learning and computer vision to a central
problem in materials informatics: The statistical representation of
microstructural images. We use activations in a pre-trained convolutional
neural network to provide a high-dimensional characterization of a set of
synthetic microstructural images. Next, we use manifold learning to obtain a
low-dimensional embedding of this statistical characterization. We show that
the low-dimensional embedding extracts the parameters used to generate the
images. According to a variety of metrics, the convolutional neural network
method yields dramatically better embeddings than the analogous method derived
from two-point correlations alone.
Recursive Decomposition for Nonconvex Optimization
Authors: Abram L. Friesen, Pedro Domingos
Date: 2016-11-08 00:00:00
Tasks: Combinatorial Optimization, graph partitioning, Problem Decomposition, Protein Folding

Continuous optimization is an important problem in many areas of AI,
including vision, robotics, probabilistic inference, and machine learning.
Unfortunately, most real-world optimization problems are nonconvex, causing
standard convex techniques to find only local optima, even with extensions like
random restarts and simulated annealing. We observe that, in many cases, the
local modes of the objective function have combinatorial structure, and thus
ideas from combinatorial optimization can be brought to bear. Based on this, we
propose a problem-decomposition approach to nonconvex optimization. Similarly
to DPLL-style SAT solvers and recursive conditioning in probabilistic
inference, our algorithm, RDIS, recursively sets variables so as to simplify
and decompose the objective function into approximately independent
sub-functions, until the remaining functions are simple enough to be optimized
by standard techniques like gradient descent. The variables to set are chosen
by graph partitioning, ensuring decomposition whenever possible. We show
analytically that RDIS can solve a broad class of nonconvex optimization
problems exponentially faster than gradient descent with random restarts.
Experimentally, RDIS outperforms standard techniques on problems like structure
from motion and protein folding.
Group Regularized Estimation under Structural Hierarchy
Authors: Yiyuan She, Zhifeng Wang, He Jiang
Date: 2014-11-17 00:00:00
Tasks: Variable Selection

Variable selection for models including interactions between explanatory
variables often needs to obey certain hierarchical constraints. The weak or
strong structural hierarchy requires that the existence of an interaction term
implies at least one or both associated main effects to be present in the
model. Lately, this problem has attracted a lot of attention, but existing
computational algorithms converge slow even with a moderate number of
predictors. Moreover, in contrast to the rich literature on ordinary variable
selection, there is a lack of statistical theory to show reasonably low error
rates of hierarchical variable selection.
  This work investigates a new class of estimators that make use of multiple
group penalties to capture structural parsimony. We give the minimax lower
bounds for strong and weak hierarchical variable selection and show that the
proposed estimators enjoy sharp rate oracle inequalities. A general-purpose
algorithm is developed with guaranteed convergence and global optimality.
Simulations and real data experiments demonstrate the efficiency and efficacy
of the proposed approach.
Accelerating the BSM interpretation of LHC data with machine learning
Authors: Gianfranco Bertone, Marc Peter Deisenroth, Jong Soo Kim, Sebastian Liem, Roberto Ruiz de Austri, Max Welling
Date: 2016-11-08 00:00:00
Tasks: BIG-bench Machine Learning

The interpretation of Large Hadron Collider (LHC) data in the framework of
Beyond the Standard Model (BSM) theories is hampered by the need to run
computationally expensive event generators and detector simulators. Performing
statistically convergent scans of high-dimensional BSM theories is consequently
challenging, and in practice unfeasible for very high-dimensional BSM theories.
We present here a new machine learning method that accelerates the
interpretation of LHC data, by learning the relationship between BSM theory
parameters and data. As a proof-of-concept, we demonstrate that this technique
accurately predicts natural SUSY signal events in two signal regions at the
High Luminosity LHC, up to four orders of magnitude faster than standard
techniques. The new approach makes it possible to rapidly and accurately
reconstruct the theory parameters of complex BSM theories, should an excess in
the data be discovered at the LHC.
Deep Action Sequence Learning for Causal Shape Transformation
Authors: Kin Gwn Lore, Daniel Stoecklein, Michael Davies, Baskar Ganapathysubramanian, Soumik Sarkar
Date: 2016-05-17 00:00:00
Tasks: Decision Making, Sequential Decision Making

Deep learning became the method of choice in recent year for solving a wide
variety of predictive analytics tasks. For sequence prediction, recurrent
neural networks (RNN) are often the go-to architecture for exploiting
sequential information where the output is dependent on previous computation.
However, the dependencies of the computation lie in the latent domain which may
not be suitable for certain applications involving the prediction of a
step-wise transformation sequence that is dependent on the previous computation
only in the visible domain. We propose that a hybrid architecture of
convolution neural networks (CNN) and stacked autoencoders (SAE) is sufficient
to learn a sequence of actions that nonlinearly transforms an input shape or
distribution into a target shape or distribution with the same support. While
such a framework can be useful in a variety of problems such as robotic path
planning, sequential decision-making in games, and identifying material
processing pathways to achieve desired microstructures, the application of the
framework is exemplified by the control of fluid deformations in a microfluidic
channel by deliberately placing a sequence of pillars. Learning of a multistep
topological transform has significant implications for rapid advances in
material science and biomedical applications.