pwc-archive/papers-with-abstracts huggingface dataset:
Graded Entailment for Compositional Distributional Semantics
Authors: Desislava Bankova, Bob Coecke, Martha Lewis, Daniel Marsden
Date: 2016-01-19 00:00:00
Tasks: Lexical Entailment, Sentence

The categorical compositional distributional model of natural language
provides a conceptually motivated procedure to compute the meaning of
sentences, given grammatical structure and the meanings of its words. This
approach has outperformed other models in mainstream empirical language
processing tasks. However, until recently it has lacked the crucial feature of
lexical entailment -- as do other distributional models of meaning.
  In this paper we solve the problem of entailment for categorical
compositional distributional semantics. Taking advantage of the abstract
categorical framework allows us to vary our choice of model. This enables the
introduction of a notion of entailment, exploiting ideas from the categorical
semantics of partial knowledge in quantum computation.
  The new model of language uses density matrices, on which we introduce a
novel robust graded order capturing the entailment strength between concepts.
This graded measure emerges from a general framework for approximate
entailment, induced by any commutative monoid. Quantum logic embeds in our
graded order.
  Our main theorem shows that entailment strength lifts compositionally to the
sentence level, giving a lower bound on sentence entailment. We describe the
essential properties of graded entailment such as continuity, and provide a
procedure for calculating entailment strength.
Context-Based Prediction of App Usage
Authors: Joseph Keshet, Adam Kariv, Arnon Dagan, Dvir Volk, Joey Simhon
Date: 2015-12-24 00:00:00
Tasks: Navigate, Prediction

There are around a hundred installed apps on an average smartphone. The high
number of apps and the limited number of app icons that can be displayed on the
device's screen requires a new paradigm to address their visibility to the
user. In this paper we propose a new online algorithm for dynamically
predicting a set of apps that the user is likely to use. The algorithm runs on
the user's device and constantly learns the user's habits at a given time,
location, and device state. It is designed to actively help the user to
navigate to the desired app as well as to provide a personalized feeling, and
hence is aimed at maximizing the AUC. We show both theoretically and
empirically that the algorithm maximizes the AUC, and yields good results on a
set of 1,000 devices.
A Label Semantics Approach to Linguistic Hedges
Authors: Martha Lewis, Jonathan Lawry
Date: 2016-01-25 00:00:00

We introduce a model for the linguistic hedges `very' and `quite' within the
label semantics framework, and combined with the prototype and conceptual
spaces theories of concepts. The proposed model emerges naturally from the
representational framework we use and as such, has a clear semantic grounding.
We give generalisations of these hedge models and show that they can be
composed with themselves and with other functions, going on to examine their
behaviour in the limit of composition.
Concept Generation in Language Evolution
Authors: Martha Lewis, Jonathan Lawry
Date: 2016-01-25 00:00:00

This thesis investigates the generation of new concepts from combinations of
existing concepts as a language evolves. We give a method for combining
concepts, and will be investigating the utility of composite concepts in
language evolution and thence the utility of concept generation.
Conditional distribution variability measures for causality detection
Authors: José A. R. Fonollosa
Date: 2016-01-25 00:00:00

In this paper we derive variability measures for the conditional probability
distributions of a pair of random variables, and we study its application in
the inference of causal-effect relationships. We also study the combination of
the proposed measures with standard statistical measures in the the framework
of the ChaLearn cause-effect pair challenge. The developed model obtains an AUC
score of 0.82 on the final test database and ranked second in the challenge.
Pricing Vehicle Sharing with Proximity Information
Authors: Jakub Marecek, Robert Shorten, Jia Yuan Yu
Date: 2016-01-25 00:00:00

For vehicle sharing schemes, where drop-off positions are not fixed, we
propose a pricing scheme, where the price depends in part on the distance
between where a vehicle is being dropped off and where the closest shared
vehicle is parked. Under certain restrictive assumptions, we show that this
pricing leads to a socially optimal spread of the vehicles within a region.
Testing for Causality in Continuous Time Bayesian Network Models of High-Frequency Data
Authors: Jonas Hallgren, Timo Koski
Date: 2016-01-25 00:00:00

Continuous time Bayesian networks are investigated with a special focus on
their ability to express causality. A framework is presented for doing
inference in these networks. The central contributions are a representation of
the intensity matrices for the networks and the introduction of a causality
measure. A new model for high-frequency financial data is presented. It is
calibrated to market data and by the new causality measure it performs better
than older models.
Time-Varying Gaussian Process Bandit Optimization
Authors: Ilija Bogunovic, Jonathan Scarlett, Volkan Cevher
Date: 2016-01-25 00:00:00
Tasks: Bayesian Optimization

We consider the sequential Bayesian optimization problem with bandit
feedback, adopting a formulation that allows for the reward function to vary
with time. We model the reward function using a Gaussian process whose
evolution obeys a simple Markov model. We introduce two natural extensions of
the classical Gaussian process upper confidence bound (GP-UCB) algorithm. The
first, R-GP-UCB, resets GP-UCB at regular intervals. The second, TV-GP-UCB,
instead forgets about old data in a smooth fashion. Our main contribution
comprises of novel regret bounds for these algorithms, providing an explicit
characterization of the trade-off between the time horizon and the rate at
which the function varies. We illustrate the performance of the algorithms on
both synthetic and real data, and we find the gradual forgetting of TV-GP-UCB
to perform favorably compared to the sharp resetting of R-GP-UCB. Moreover,
both algorithms significantly outperform classical GP-UCB, since it treats
stale and fresh data equally.
Bayesian Estimation of Bipartite Matchings for Record Linkage
Authors: Mauricio Sadinle
Date: 2016-01-25 00:00:00

The bipartite record linkage task consists of merging two disparate datafiles
containing information on two overlapping sets of entities. This is non-trivial
in the absence of unique identifiers and it is important for a wide variety of
applications given that it needs to be solved whenever we have to combine
information from different sources. Most statistical techniques currently used
for record linkage are derived from a seminal paper by Fellegi and Sunter
(1969). These techniques usually assume independence in the matching statuses
of record pairs to derive estimation procedures and optimal point estimators.
We argue that this independence assumption is unreasonable and instead target a
bipartite matching between the two datafiles as our parameter of interest.
Bayesian implementations allow us to quantify uncertainty on the matching
decisions and derive a variety of point estimators using different loss
functions. We propose partial Bayes estimates that allow uncertain parts of the
bipartite matching to be left unresolved. We evaluate our approach to record
linkage using a variety of challenging scenarios and show that it outperforms
the traditional methodology. We illustrate the advantages of our methods
merging two datafiles on casualties from the civil war of El Salvador.
Intensity-only optical compressive imaging using a multiply scattering material and a double phase retrieval approach
Authors: Boshra Rajaei, Eric W. Tramel, Sylvain Gigan, Florent Krzakala, Laurent Daudet
Date: 2015-10-05 00:00:00
Tasks: Image Reconstruction, Retrieval

In this paper, the problem of compressive imaging is addressed using natural
randomization by means of a multiply scattering medium. To utilize the medium
in this way, its corresponding transmission matrix must be estimated. To
calibrate the imager, we use a digital micromirror device (DMD) as a simple,
cheap, and high-resolution binary intensity modulator. We propose a phase
retrieval algorithm which is well adapted to intensity-only measurements on the
camera, and to the input binary intensity patterns, both to estimate the
complex transmission matrix as well as image reconstruction. We demonstrate
promising experimental results for the proposed algorithm using the MNIST
dataset of handwritten digits as example images.
A Taxonomy of Deep Convolutional Neural Nets for Computer Vision
Authors: Suraj Srinivas, Ravi Kiran Sarvadevabhatla, Konda Reddy Mopuri, Nikita Prabhu, Srinivas S. S. Kruthiventi, R. Venkatesh Babu
Date: 2016-01-25 00:00:00
Tasks: Deep Learning, Survey

Traditional architectures for solving computer vision problems and the degree
of success they enjoyed have been heavily reliant on hand-crafted features.
However, of late, deep learning techniques have offered a compelling
alternative -- that of automatically learning problem-specific features. With
this new paradigm, every problem in computer vision is now being re-examined
from a deep learning perspective. Therefore, it has become important to
understand what kind of deep networks are suitable for a given problem.
Although general surveys of this fast-moving paradigm (i.e. deep-networks)
exist, a survey specific to computer vision is missing. We specifically
consider one form of deep networks widely used in computer vision -
convolutional neural networks (CNNs). We start with "AlexNet" as our base CNN
and then examine the broad variations proposed over time to suit different
applications. We hope that our recipe-style survey will serve as a guide,
particularly for novice practitioners intending to use deep-learning techniques
for computer vision.
Generalizing Prototype Theory: A Formal Quantum Framework
Authors: Diederik Aerts, Jan Broekaert, Liane Gabora, Sandro Sozzo
Date: 2016-01-25 00:00:00
Tasks: Decision Making

Theories of natural language and concepts have been unable to model the
flexibility, creativity, context-dependence, and emergence, exhibited by words,
concepts and their combinations. The mathematical formalism of quantum theory
has instead been successful in capturing these phenomena such as graded
membership, situational meaning, composition of categories, and also more
complex decision making situations, which cannot be modeled in traditional
probabilistic approaches. We show how a formal quantum approach to concepts and
their combinations can provide a powerful extension of prototype theory. We
explain how prototypes can interfere in conceptual combinations as a
consequence of their contextual interactions, and provide an illustration of
this using an intuitive wave-like diagram. This quantum-conceptual approach
gives new life to original prototype theory, without however making it a
privileged concept theory, as we explain at the end of our paper.
An Unsupervised Method for Detection and Validation of The Optic Disc and The Fovea
Authors: Mrinal Haloi, Samarendra Dandapat, Rohit Sinha
Date: 2016-01-25 00:00:00

In this work, we have presented a novel method for detection of retinal image
features, the optic disc and the fovea, from colour fundus photographs of
dilated eyes for Computer-aided Diagnosis(CAD) system. A saliency map based
method was used to detect the optic disc followed by an unsupervised
probabilistic Latent Semantic Analysis for detection validation. The validation
concept is based on distinct vessels structures in the optic disc. By using the
clinical information of standard location of the fovea with respect to the
optic disc, the macula region is estimated. Accuracy of 100\% detection is
achieved for the optic disc and the macula on MESSIDOR and DIARETDB1 and 98.8\%
detection accuracy on STARE dataset.
Egocentric Activity Recognition with Multimodal Fisher Vector
Authors: Sibo Song, Ngai-Man Cheung, Vijay Chandrasekhar, Bappaditya Mandal, Jie Lin
Date: 2016-01-25 00:00:00
Tasks: Activity Recognition, Egocentric Activity Recognition

With the increasing availability of wearable devices, research on egocentric
activity recognition has received much attention recently. In this paper, we
build a Multimodal Egocentric Activity dataset which includes egocentric videos
and sensor data of 20 fine-grained and diverse activity categories. We present
a novel strategy to extract temporal trajectory-like features from sensor data.
We propose to apply the Fisher Kernel framework to fuse video and temporal
enhanced sensor features. Experiment results show that with careful design of
feature extraction and fusion algorithm, sensor data can enhance
information-rich video data. We make publicly available the Multimodal
Egocentric Activity dataset to facilitate future research.
Is swarm intelligence able to create mazes?
Authors: Dawid Polap, Marcin Wozniak, Christian Napoli, Emiliano Tramontana
Date: 2016-01-25 00:00:00
Tasks: Board Games

In this paper, the idea of applying Computational Intelligence in the process
of creation board games, in particular mazes, is presented. For two different
algorithms the proposed idea has been examined. The results of the experiments
are shown and discussed to present advantages and disadvantages.
Visualizations Relevant to The User By Multi-View Latent Variable Factorization
Authors: Seppo Virtanen, Homayun Afrabandpey, Samuel Kaski
Date: 2015-12-24 00:00:00
Tasks: Data Visualization

A main goal of data visualization is to find, from among all the available
alternatives, mappings to the 2D/3D display which are relevant to the user.
Assuming user interaction data, or other auxiliary data about the items or
their relationships, the goal is to identify which aspects in the primary data
support the user\'s input and, equally importantly, which aspects of the
user\'s potentially noisy input have support in the primary data. For solving
the problem, we introduce a multi-view embedding in which a latent
factorization identifies which aspects in the two data views (primary data and
user data) are related and which are specific to only one of them. The
factorization is a generative model in which the display is parameterized as a
part of the factorization and the other factors explain away the aspects not
expressible in a two-dimensional display. Functioning of the model is
demonstrated on several data sets.
Towards Resolving Unidentifiability in Inverse Reinforcement Learning
Authors: Kareem Amin, Satinder Singh
Date: 2016-01-25 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider a setting for Inverse Reinforcement Learning (IRL) where the
learner is extended with the ability to actively select multiple environments,
observing an agent's behavior on each environment. We first demonstrate that if
the learner can experiment with any transition dynamics on some fixed set of
states and actions, then there exists an algorithm that reconstructs the
agent's reward function to the fullest extent theoretically possible, and that
requires only a small (logarithmic) number of experiments. We contrast this
result to what is known about IRL in single fixed environments, namely that the
true reward function is fundamentally unidentifiable. We then extend this
setting to the more realistic case where the learner may not select any
transition dynamic, but rather is restricted to some fixed set of environments
that it may try. We connect the problem of maximizing the information derived
from experiments to submodular function maximization and demonstrate that a
greedy algorithm is near optimal (up to logarithmic factors). Finally, we
empirically validate our algorithm on an environment inspired by behavioral
psychology.
EM Algorithms for Weighted-Data Clustering with Application to Audio-Visual Scene Analysis
Authors: Israel D. Gebru, Xavier Alameda-Pineda, Florence Forbes, Radu Horaud
Date: 2015-09-04 00:00:00
Tasks: Clustering, Model Selection

Data clustering has received a lot of attention and numerous methods,
algorithms and software packages are available. Among these techniques,
parametric finite-mixture models play a central role due to their interesting
mathematical properties and to the existence of maximum-likelihood estimators
based on expectation-maximization (EM). In this paper we propose a new mixture
model that associates a weight with each observed point. We introduce the
weighted-data Gaussian mixture and we derive two EM algorithms. The first one
considers a fixed weight for each observation. The second one treats each
weight as a random variable following a gamma distribution. We propose a model
selection method based on a minimum message length criterion, provide a weight
initialization strategy, and validate the proposed algorithms by comparing them
with several state of the art parametric and non-parametric clustering
techniques. We also demonstrate the effectiveness and robustness of the
proposed clustering technique in the presence of heterogeneous data, namely
audio-visual scene analysis.
Smooth PARAFAC Decomposition for Tensor Completion
Authors: Tatsuya Yokota, Qibin Zhao, Andrzej Cichocki
Date: 2015-05-25 00:00:00
Tasks: Matrix Completion

In recent years, low-rank based tensor completion, which is a higher-order
extension of matrix completion, has received considerable attention. However,
the low-rank assumption is not sufficient for the recovery of visual data, such
as color and 3D images, where the ratio of missing data is extremely high. In
this paper, we consider "smoothness" constraints as well as low-rank
approximations, and propose an efficient algorithm for performing tensor
completion that is particularly powerful regarding visual data. The proposed
method admits significant advantages, owing to the integration of smooth
PARAFAC decomposition for incomplete tensors and the efficient selection of
models in order to minimize the tensor rank. Thus, our proposed method is
termed as "smooth PARAFAC tensor completion (SPC)." In order to impose the
smoothness constraints, we employ two strategies, total variation (SPC-TV) and
quadratic variation (SPC-QV), and invoke the corresponding algorithms for model
learning. Extensive experimental evaluations on both synthetic and real-world
visual data illustrate the significant improvements of our method, in terms of
both prediction performance and efficiency, compared with many state-of-the-art
tensor completion methods.
Learning Binary Features Online from Motion Dynamics for Incremental Loop-Closure Detection and Place Recognition
Authors: Guangcong Zhang, Mason J. Lilly, Patricio A. Vela
Date: 2016-01-15 00:00:00
Tasks: Loop Closure Detection

This paper proposes a simple yet effective approach to learn visual features
online for improving loop-closure detection and place recognition, based on
bag-of-words frameworks. The approach learns a codeword in bag-of-words model
from a pair of matched features from two consecutive frames, such that the
codeword has temporally-derived perspective invariance to camera motion. The
learning algorithm is efficient: the binary descriptor is generated from the
mean image patch, and the mask is learned based on discriminative projection by
minimizing the intra-class distances among the learned feature and the two
original features. A codeword for bag-of-words models is generated by packaging
the learned descriptor and mask, with a masked Hamming distance defined to
measure the distance between two codewords. The geometric properties of the
learned codewords are then mathematically justified. In addition, hypothesis
constraints are imposed through temporal consistency in matched codewords,
which improves precision. The approach, integrated in an incremental
bag-of-words system, is validated on multiple benchmark data sets and compared
to state-of-the-art methods. Experiments demonstrate improved precision/recall
outperforming state of the art with little loss in runtime.
Information Limits for Recovering a Hidden Community
Authors: Bruce Hajek, Yihong Wu, Jiaming Xu
Date: 2015-09-25 00:00:00

We study the problem of recovering a hidden community of cardinality $K$ from
an $n \times n$ symmetric data matrix $A$, where for distinct indices $i,j$,
$A_{ij} \sim P$ if $i, j$ both belong to the community and $A_{ij} \sim Q$
otherwise, for two known probability distributions $P$ and $Q$ depending on
$n$. If $P={\rm Bern}(p)$ and $Q={\rm Bern}(q)$ with $p>q$, it reduces to the
problem of finding a densely-connected $K$-subgraph planted in a large
Erd\"os-R\'enyi graph; if $P=\mathcal{N}(\mu,1)$ and $Q=\mathcal{N}(0,1)$ with
$\mu>0$, it corresponds to the problem of locating a $K \times K$ principal
submatrix of elevated means in a large Gaussian random matrix. We focus on two
types of asymptotic recovery guarantees as $n \to \infty$: (1) weak recovery:
expected number of classification errors is $o(K)$; (2) exact recovery:
probability of classifying all indices correctly converges to one. Under mild
assumptions on $P$ and $Q$, and allowing the community size to scale
sublinearly with $n$, we derive a set of sufficient conditions and a set of
necessary conditions for recovery, which are asymptotically tight with sharp
constants. The results hold in particular for the Gaussian case, and for the
case of bounded log likelihood ratio, including the Bernoulli case whenever
$\frac{p}{q}$ and $\frac{1-p}{1-q}$ are bounded away from zero and infinity. An
important algorithmic implication is that, whenever exact recovery is
information theoretically possible, any algorithm that provides weak recovery
when the community size is concentrated near $K$ can be upgraded to achieve
exact recovery in linear additional time by a simple voting procedure.
A new correlation clustering method for cancer mutation analysis
Authors: Jack P. Hou, Amin Emad, Gregory J. Puleo, Jian Ma, Olgica Milenkovic
Date: 2016-01-25 00:00:00
Tasks: Clustering, Community Detection

Cancer genomes exhibit a large number of different alterations that affect
many genes in a diverse manner. It is widely believed that these alterations
follow combinatorial patterns that have a strong connection with the underlying
molecular interaction networks and functional pathways. A better understanding
of the generative mechanisms behind the mutation rules and their influence on
gene communities is of great importance for the process of driver mutations
discovery and for identification of network modules related to cancer
development and progression. We developed a new method for cancer mutation
pattern analysis based on a constrained form of correlation clustering.
Correlation clustering is an agnostic learning method that can be used for
general community detection problems in which the number of communities or
their structure is not known beforehand. The resulting algorithm, named $C^3$,
leverages mutual exclusivity of mutations, patient coverage, and driver network
concentration principles; it accepts as its input a user determined combination
of heterogeneous patient data, such as that available from TCGA (including
mutation, copy number, and gene expression information), and creates a large
number of clusters containing mutually exclusive mutated genes in a particular
type of cancer. The cluster sizes may be required to obey some useful soft size
constraints, without impacting the computational complexity of the algorithm.
To test $C^3$, we performed a detailed analysis on TCGA breast cancer and
glioblastoma data and showed that our algorithm outperforms the
state-of-the-art CoMEt method in terms of discovering mutually exclusive gene
modules and identifying driver genes. Our $C^3$ method represents a unique tool
for efficient and reliable identification of mutation patterns and driver
pathways in large-scale cancer genomics studies.