pwc-archive/papers-with-abstracts huggingface dataset:
Multidimensional Scaling in the Poincare Disk
Authors: Andrej Cvetkovski, Mark Crovella
Date: 2011-05-26 00:00:00

Multidimensional scaling (MDS) is a class of projective algorithms
traditionally used in Euclidean space to produce two- or three-dimensional
visualizations of datasets of multidimensional points or point distances. More
recently however, several authors have pointed out that for certain datasets,
hyperbolic target space may provide a better fit than Euclidean space.
  In this paper we develop PD-MDS, a metric MDS algorithm designed specifically
for the Poincare disk (PD) model of the hyperbolic plane. Emphasizing the
importance of proceeding from first principles in spite of the availability of
various black box optimizers, our construction is based on an elementary
hyperbolic line search and reveals numerous particulars that need to be
carefully addressed when implementing this as well as more sophisticated
iterative optimization methods in a hyperbolic space model.
TabMCQ: A Dataset of General Knowledge Tables and Multiple-choice Questions
Authors: Sujay Kumar Jauhar, Peter Turney, Eduard Hovy
Date: 2016-02-12 00:00:00
Tasks: General Knowledge, Multiple-choice, Question Answering

We describe two new related resources that facilitate modelling of general
knowledge reasoning in 4th grade science exams. The first is a collection of
curated facts in the form of tables, and the second is a large set of
crowd-sourced multiple-choice questions covering the facts in the tables.
Through the setup of the crowd-sourced annotation task we obtain implicit
alignment information between questions and tables. We envisage that the
resources will be useful not only to researchers working on question answering,
but also to people investigating a diverse range of other applications such as
information extraction, question parsing, answer type identification, and
lexical semantic modelling.
The Automatic Statistician: A Relational Perspective
Authors: Yunseong Hwang, Anh Tong, Jaesik Choi
Date: 2015-11-26 00:00:00
Tasks: Gaussian Processes, Time Series, Time Series Analysis

Gaussian Processes (GPs) provide a general and analytically tractable way of
modeling complex time-varying, nonparametric functions. The Automatic Bayesian
Covariance Discovery (ABCD) system constructs natural-language description of
time-series data by treating unknown time-series data nonparametrically using
GP with a composite covariance kernel function. Unfortunately, learning a
composite covariance kernel with a single time-series data set often results in
less informative kernel that may not give qualitative, distinctive descriptions
of data. We address this challenge by proposing two relational kernel learning
methods which can model multiple time-series data sets by finding common,
shared causes of changes. We show that the relational kernel learning methods
find more accurate models for regression problems on several real-world data
sets; US stock data, US house price index data and currency exchange rate data.
General Vector Machine
Authors: Hong Zhao
Date: 2016-02-12 00:00:00

The support vector machine (SVM) is an important class of learning machines
for function approach, pattern recognition, and time-serious prediction, etc.
It maps samples into the feature space by so-called support vectors of selected
samples, and then feature vectors are separated by maximum margin hyperplane.
The present paper presents the general vector machine (GVM) to replace the SVM.
The support vectors are replaced by general project vectors selected from the
usual vector space, and a Monte Carlo (MC) algorithm is developed to find the
general vectors. The general project vectors improves the feature-extraction
ability, and the MC algorithm can control the width of the separation margin of
the hyperplane. By controlling the separation margin, we show that the maximum
margin hyperplane can usually induce the overlearning, and the best learning
machine is achieved with a proper separation margin. Applications in function
approach, pattern recognition, and classification indicate that the developed
method is very successful, particularly for small-set training problems.
Additionally, our algorithm may induce some particular applications, such as
for the transductive inference.
Exploring the Limits of Language Modeling
Authors: Rafal Jozefowicz, Oriol Vinyals, Mike Schuster, Noam Shazeer, Yonghui Wu
Date: 2016-02-07 00:00:00
Tasks: Language Modeling, Language Modelling

In this work we explore recent advances in Recurrent Neural Networks for
large scale Language Modeling, a task central to language understanding. We
extend current models to deal with two key challenges present in this task:
corpora and vocabulary sizes, and complex, long term structure of language. We
perform an exhaustive study on techniques such as character Convolutional
Neural Networks or Long-Short Term Memory, on the One Billion Word Benchmark.
Our best single model significantly improves state-of-the-art perplexity from
51.3 down to 30.0 (whilst reducing the number of parameters by a factor of 20),
while an ensemble of models sets a new record by improving perplexity from 41.0
down to 23.7. We also release these models for the NLP and ML community to
study and improve upon.
Defining Concepts of Emotion: From Philosophy to Science
Authors: Changqing Liu
Date: 2016-02-11 00:00:00
Tasks: Philosophy

This paper is motivated by a series of (related) questions as to whether a
computer can have pleasure and pain, what pleasure (and intensity of pleasure)
is, and, ultimately, what concepts of emotion are.
  To determine what an emotion is, is a matter of conceptualization, namely,
understanding and explicitly encoding the concept of emotion as people use it
in everyday life. This is a notoriously difficult problem (Frijda, 1986, Fehr
\& Russell, 1984). This paper firstly shows why this is a difficult problem by
aligning it with the conceptualization of a few other so called semantic
primitives such as "EXIST", "FORCE", "BIG" (plus "LIMIT"). The definitions of
these thought-to-be-indefinable concepts, given in this paper, show what formal
definitions of concepts look like and how concepts are constructed. As a
by-product, owing to the explicit account of the meaning of "exist", the famous
dispute between Einstein and Bohr is naturally resolved from linguistic point
of view. Secondly, defending Frijda's view that emotion is action tendency (or
Ryle's behavioral disposition (propensity)), we give a list of emotions defined
in terms of action tendency. In particular, the definitions of pleasure and the
feeling of beauty are presented.
  Further, we give a formal definition of "action tendency", from which the
concept of "intensity" of emotions (including pleasure) is naturally derived in
a formal fashion. The meanings of "wish", "wait", "good", "hot" are analyzed.
Modeling Human Ad Hoc Coordination
Authors: Peter M. Krafft, Chris L. Baker, Alex Pentland, Joshua B. Tenenbaum
Date: 2016-02-11 00:00:00
Tasks: Human Agent Collaboration, valid

Whether in groups of humans or groups of computer agents, collaboration is
most effective between individuals who have the ability to coordinate on a
joint strategy for collective action. However, in general a rational actor will
only intend to coordinate if that actor believes the other group members have
the same intention. This circular dependence makes rational coordination
difficult in uncertain environments if communication between actors is
unreliable and no prior agreements have been made. An important normative
question with regard to coordination in these ad hoc settings is therefore how
one can come to believe that other actors will coordinate, and with regard to
systems involving humans, an important empirical question is how humans arrive
at these expectations. We introduce an exact algorithm for computing the
infinitely recursive hierarchy of graded beliefs required for rational
coordination in uncertain environments, and we introduce a novel mechanism for
multiagent coordination that uses it. Our algorithm is valid in any environment
with a finite state space, and extensions to certain countably infinite state
spaces are likely possible. We test our mechanism for multiagent coordination
as a model for human decisions in a simple coordination game using existing
experimental data. We then explore via simulations whether modeling humans in
this way may improve human-agent collaboration.
Solving Transition-Independent Multi-agent MDPs with Sparse Interactions (Extended version)
Authors: Joris Scharpff, Diederik M. Roijers, Frans A. Oliehoek, Matthijs T. J. Spaan, Mathijs M. de Weerdt
Date: 2015-11-29 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty, Sequential Decision Making

In cooperative multi-agent sequential decision making under uncertainty,
agents must coordinate to find an optimal joint policy that maximises joint
value. Typical algorithms exploit additive structure in the value function, but
in the fully-observable multi-agent MDP setting (MMDP) such structure is not
present. We propose a new optimal solver for transition-independent MMDPs, in
which agents can only affect their own state but their reward depends on joint
transitions. We represent these dependencies compactly in conditional return
graphs (CRGs). Using CRGs the value of a joint policy and the bounds on
partially specified joint policies can be efficiently computed. We propose
CoRe, a novel branch-and-bound policy search algorithm building on CRGs. CoRe
typically requires less runtime than the available alternatives and finds
solutions to problems previously unsolvable.
Real-Time Hand Tracking Using a Sum of Anisotropic Gaussians Model
Authors: Srinath Sridhar, Helge Rhodin, Hans-Peter Seidel, Antti Oulasvirta, Christian Theobalt
Date: 2016-02-11 00:00:00

Real-time marker-less hand tracking is of increasing importance in
human-computer interaction. Robust and accurate tracking of arbitrary hand
motion is a challenging problem due to the many degrees of freedom, frequent
self-occlusions, fast motions, and uniform skin color. In this paper, we
propose a new approach that tracks the full skeleton motion of the hand from
multiple RGB cameras in real-time. The main contributions include a new
generative tracking method which employs an implicit hand shape representation
based on Sum of Anisotropic Gaussians (SAG), and a pose fitting energy that is
smooth and analytically differentiable making fast gradient based pose
optimization possible. This shape representation, together with a full
perspective projection model, enables more accurate hand modeling than a
related baseline method from literature. Our method achieves better accuracy
than previous methods and runs at 25 fps. We show these improvements both
qualitatively and quantitatively on publicly available datasets.
Validity of time reversal for testing Granger causality
Authors: Irene Winkler, Danny Panknin, Daniel Bartz, Klaus-Robert Müller, Stefan Haufe
Date: 2015-09-25 00:00:00
Tasks: valid

Inferring causal interactions from observed data is a challenging problem,
especially in the presence of measurement noise. To alleviate the problem of
spurious causality, Haufe et al. (2013) proposed to contrast measures of
information flow obtained on the original data against the same measures
obtained on time-reversed data. They show that this procedure, time-reversed
Granger causality (TRGC), robustly rejects causal interpretations on mixtures
of independent signals. While promising results have been achieved in
simulations, it was so far unknown whether time reversal leads to valid
measures of information flow in the presence of true interaction. Here we prove
that, for linear finite-order autoregressive processes with unidirectional
information flow, the application of time reversal for testing Granger
causality indeed leads to correct estimates of information flow and its
directionality. Using simulations, we further show that TRGC is able to infer
correct directionality with similar statistical power as the net Granger
causality between two variables, while being much more robust to the presence
of measurement noise.
Enabling Basic Normative HRI in a Cognitive Robotic Architecture
Authors: Vasanth Sarathy, Jason R. Wilson, Thomas Arnold, Matthias Scheutz
Date: 2016-02-11 00:00:00
Tasks: Decision Making, Position

Collaborative human activities are grounded in social and moral norms, which
humans consciously and subconsciously use to guide and constrain their
decision-making and behavior, thereby strengthening their interactions and
preventing emotional and physical harm. This type of norm-based processing is
also critical for robots in many human-robot interaction scenarios (e.g., when
helping elderly and disabled persons in assisted living facilities, or
assisting humans in assembly tasks in factories or even the space station). In
this position paper, we will briefly describe how several components in an
integrated cognitive architecture can be used to implement processes that are
required for normative human-robot interactions, especially in collaborative
tasks where actions and situations could potentially be perceived as
threatening and thus need a change in course of action to mitigate the
perceived threats.
Semi-supervised Learning with Explicit Relationship Regularization
Authors: Kwang In Kim, James Tompkin, Hanspeter Pfister, Christian Theobalt
Date: 2016-02-11 00:00:00
Proceeding: CVPR 2015 6
Tasks: Clustering, Constrained Clustering, Dimensionality Reduction, General Classification

In many learning tasks, the structure of the target space of a function holds
rich information about the relationships between evaluations of functions on
different data points. Existing approaches attempt to exploit this relationship
information implicitly by enforcing smoothness on function evaluations only.
However, what happens if we explicitly regularize the relationships between
function evaluations? Inspired by homophily, we regularize based on a smooth
relationship function, either defined from the data or with labels. In
experiments, we demonstrate that this significantly improves the performance of
state-of-the-art algorithms in semi-supervised classification and in spectral
data embedding for constrained clustering and dimensionality reduction.
Local High-order Regularization on Data Manifolds
Authors: Kwang In Kim, James Tompkin, Hanspeter Pfister, Christian Theobalt
Date: 2016-02-11 00:00:00
Proceeding: CVPR 2015 6
Tasks: Dimensionality Reduction, Vocal Bursts Intensity Prediction

The common graph Laplacian regularizer is well-established in semi-supervised
learning and spectral dimensionality reduction. However, as a first-order
regularizer, it can lead to degenerate functions in high-dimensional manifolds.
The iterated graph Laplacian enables high-order regularization, but it has a
high computational complexity and so cannot be applied to large problems. We
introduce a new regularizer which is globally high order and so does not suffer
from the degeneracy of the graph Laplacian regularizer, but is also sparse for
efficient computation in semi-supervised learning applications. We reduce
computational complexity by building a local first-order approximation of the
manifold as a surrogate geometry, and construct our high-order regularizer
based on local derivative evaluations therein. Experiments on human body shape
and pose analysis demonstrate the effectiveness and efficiency of our method.
Column-Oriented Datalog Materialization for Large Knowledge Graphs (Extended Technical Report)
Authors: Jacopo Urbani, Ceriel Jacobs, Markus Krötzsch
Date: 2015-11-28 00:00:00
Tasks: Knowledge Graphs

The evaluation of Datalog rules over large Knowledge Graphs (KGs) is
essential for many applications. In this paper, we present a new method of
materializing Datalog inferences, which combines a column-based memory layout
with novel optimization methods that avoid redundant inferences at runtime. The
pro-active caching of certain subqueries further increases efficiency. Our
empirical evaluation shows that this approach can often match or even surpass
the performance of state-of-the-art systems, especially under restricted
resources.
HMM and DTW for evaluation of therapeutical gestures using kinect
Authors: Carlos Palma, Augusto Salazar, Francisco Vargas
Date: 2016-02-11 00:00:00
Tasks: Dynamic Time Warping

Automatic recognition of the quality of movement in human beings is a
challenging task, given the difficulty both in defining the constraints that
make a movement correct, and the difficulty in using noisy data to determine if
these constraints were satisfied. This paper presents a method for the
detection of deviations from the correct form in movements from physical
therapy routines based on Hidden Markov Models, which is compared to Dynamic
Time Warping. The activities studied include upper an lower limbs movements,
the data used comes from a Kinect sensor. Correct repetitions of the activities
of interest were recorded, as well as deviations from these correct forms. The
ability of the proposed approach to detect these deviations was studied.
Results show that a system based on HMM is much more likely to determine if a
certain movement has deviated from the specification.
A Versatile Scene Model with Differentiable Visibility Applied to Generative Pose Estimation
Authors: Helge Rhodin, Nadia Robertini, Christian Richardt, Hans-Peter Seidel, Christian Theobalt
Date: 2016-02-11 00:00:00
Proceeding: ICCV 2015 12
Tasks: 3D geometry, Occlusion Handling, Pose Estimation

Generative reconstruction methods compute the 3D configuration (such as pose
and/or geometry) of a shape by optimizing the overlap of the projected 3D shape
model with images. Proper handling of occlusions is a big challenge, since the
visibility function that indicates if a surface point is seen from a camera can
often not be formulated in closed form, and is in general discrete and
non-differentiable at occlusion boundaries. We present a new scene
representation that enables an analytically differentiable closed-form
formulation of surface visibility. In contrast to previous methods, this yields
smooth, analytically differentiable, and efficient to optimize pose similarity
energies with rigorous occlusion handling, fewer local minima, and
experimentally verified improved convergence of numerical optimization. The
underlying idea is a new image formation model that represents opaque objects
by a translucent medium with a smooth Gaussian density distribution which turns
visibility into a smooth phenomenon. We demonstrate the advantages of our
versatile scene model in several generative pose estimation problems, namely
marker-less multi-object pose estimation, marker-less human motion capture with
few cameras, and image-based 3D geometry estimation.
An End-to-End Neural Network for Polyphonic Piano Music Transcription
Authors: Siddharth Sigtia, Emmanouil Benetos, Simon Dixon
Date: 2015-08-07 00:00:00
Tasks: Language Modeling, Language Modelling, Music Transcription, speech-recognition, Speech Recognition

We present a supervised neural network model for polyphonic piano music
transcription. The architecture of the proposed model is analogous to speech
recognition systems and comprises an acoustic model and a music language model.
The acoustic model is a neural network used for estimating the probabilities of
pitches in a frame of audio. The language model is a recurrent neural network
that models the correlations between pitch combinations over time. The proposed
model is general and can be used to transcribe polyphonic music without
imposing any constraints on the polyphony. The acoustic and language model
predictions are combined using a probabilistic graphical model. Inference over
the output variables is performed using the beam search algorithm. We perform
two sets of experiments. We investigate various neural network architectures
for the acoustic models and also investigate the effect of combining acoustic
and music language model predictions using the proposed architecture. We
compare performance of the neural network based acoustic models with two
popular unsupervised acoustic models. Results show that convolutional neural
network acoustic models yields the best performance across all evaluation
metrics. We also observe improved performance with the application of the music
language models. Finally, we present an efficient variant of beam search that
improves performance and reduces run-times by an order of magnitude, making the
model suitable for real-time applications.
A Universal Approximation Theorem for Mixture of Experts Models
Authors: Hien D. Nguyen, Luke R Lloyd-Jones, Geoffrey J. McLachlan
Date: 2016-02-11 00:00:00
Tasks: General Classification, Mixture-of-Experts, regression

The mixture of experts (MoE) model is a popular neural network architecture
for nonlinear regression and classification. The class of MoE mean functions is
known to be uniformly convergent to any unknown target function, assuming that
the target function is from Sobolev space that is sufficiently differentiable
and that the domain of estimation is a compact unit hypercube. We provide an
alternative result, which shows that the class of MoE mean functions is dense
in the class of all continuous functions over arbitrary compact domains of
estimation. Our result can be viewed as a universal approximation theorem for
MoE models.
Package equivalence in complex software network
Authors: Tomislav Slijepčević
Date: 2016-02-11 00:00:00
Tasks: Recommendation Systems

The public package registry npm is one of the biggest software registry. With
its 216 911 software packages, it forms a big network of software dependencies.
In this paper we evaluate various methods for finding similar packages in the
npm network, using only the structure of the graph. Namely, we want to find a
way of categorizing similar packages, which would be useful for recommendation
systems. This size enables us to compute meaningful results, as it softened the
particularities of the graph. Npm is also quite famous as it is the default
package repository of Node.js. We believe that it will make our results
interesting for more people than a less used package repository. This makes it
a good subject of analysis of software networks.
On the emergence of syntactic structures: quantifying and modelling duality of patterning
Authors: Vittorio Loreto, Pietro Gravino, Vito D. P. Servedio, Francesca Tria
Date: 2016-02-11 00:00:00

The complex organization of syntax in hierarchical structures is one of the
core design features of human language. Duality of patterning refers for
instance to the organization of the meaningful elements in a language at two
distinct levels: a combinatorial level where meaningless forms are combined
into meaningful forms and a compositional level where meaningful forms are
composed into larger lexical units. The question remains wide open regarding
how such a structure could have emerged. Furthermore a clear mathematical
framework to quantify this phenomenon is still lacking. The aim of this paper
is that of addressing these two aspects in a self-consistent way. First, we
introduce suitable measures to quantify the level of combinatoriality and
compositionality in a language, and present a framework to estimate these
observables in human natural languages. Second, we show that the theoretical
predictions of a multi-agents modeling scheme, namely the Blending Game, are in
surprisingly good agreement with empirical data. In the Blending Game a
population of individuals plays language games aiming at success in
communication. It is remarkable that the two sides of duality of patterning
emerge simultaneously as a consequence of a pure cultural dynamics in a
simulated environment that contains meaningful relations, provided a simple
constraint on message transmission fidelity is also considered.