pwc-archive/papers-with-abstracts huggingface dataset:
Human collective intelligence as distributed Bayesian inference
Authors: Peter M. Krafft, Julia Zheng, Wei Pan, Nicolás Della Penna, Yaniv Altshuler, Erez Shmueli, Joshua B. Tenenbaum, Alex Pentland
Date: 2016-08-05 00:00:00
Tasks: Bayesian Inference, Decision Making, Sequential Decision Making, Thompson Sampling

Collective intelligence is believed to underly the remarkable success of
human society. The formation of accurate shared beliefs is one of the key
components of human collective intelligence. How are accurate shared beliefs
formed in groups of fallible individuals? Answering this question requires a
multiscale analysis. We must understand both the individual decision mechanisms
people use, and the properties and dynamics of those mechanisms in the
aggregate. As of yet, mathematical tools for such an approach have been
lacking. To address this gap, we introduce a new analytical framework: We
propose that groups arrive at accurate shared beliefs via distributed Bayesian
inference. Distributed inference occurs through information processing at the
individual level, and yields rational belief formation at the group level. We
instantiate this framework in a new model of human social decision-making,
which we validate using a dataset we collected of over 50,000 users of an
online social trading platform where investors mimic each others' trades using
real money in foreign exchange and other asset markets. We find that in this
setting people use a decision mechanism in which popularity is treated as a
prior distribution for which decisions are best to make. This mechanism is
boundedly rational at the individual level, but we prove that in the aggregate
implements a type of approximate "Thompson sampling"---a well-known and highly
effective single-agent Bayesian machine learning algorithm for sequential
decision-making. The perspective of distributed Bayesian inference therefore
reveals how collective rationality emerges from the boundedly rational decision
mechanisms people use.
Paraconsistency and Word Puzzles
Authors: Tiantian Gao, Paul Fodor, Michael Kifer
Date: 2016-08-03 00:00:00

Word puzzles and the problem of their representations in logic languages have
received considerable attention in the last decade (Ponnuru et al. 2004;
Shapiro 2011; Baral and Dzifcak 2012; Schwitter 2013). Of special interest is
the problem of generating such representations directly from natural language
(NL) or controlled natural language (CNL). An interesting variation of this
problem, and to the best of our knowledge, scarcely explored variation in this
context, is when the input information is inconsistent. In such situations, the
existing encodings of word puzzles produce inconsistent representations and
break down. In this paper, we bring the well-known type of paraconsistent
logics, called Annotated Predicate Calculus (APC) (Kifer and Lozinskii 1992),
to bear on the problem. We introduce a new kind of non-monotonic semantics for
APC, called consistency preferred stable models and argue that it makes APC
into a suitable platform for dealing with inconsistency in word puzzles and,
more generally, in NL sentences. We also devise a number of general principles
to help the user choose among the different representations of NL sentences,
which might seem equivalent but, in fact, behave differently when inconsistent
information is taken into account. These principles can be incorporated into
existing CNL translators, such as Attempto Controlled English (ACE) (Fuchs et
al. 2008) and PENG Light (White and Schwitter 2009). Finally, we show that APC
with the consistency preferred stable model semantics can be equivalently
embedded in ASP with preferences over stable models, and we use this embedding
to implement this version of APC in Clingo (Gebser et al. 2011) and its Asprin
add-on (Brewka et al. 2015).
Kernel Ridge Regression via Partitioning
Authors: Rashish Tandon, Si Si, Pradeep Ravikumar, Inderjit Dhillon
Date: 2016-08-05 00:00:00
Tasks: Clustering, Generalization Bounds, regression

In this paper, we investigate a divide and conquer approach to Kernel Ridge
Regression (KRR). Given n samples, the division step involves separating the
points based on some underlying disjoint partition of the input space (possibly
via clustering), and then computing a KRR estimate for each partition. The
conquering step is simple: for each partition, we only consider its own local
estimate for prediction. We establish conditions under which we can give
generalization bounds for this estimator, as well as achieve optimal minimax
rates. We also show that the approximation error component of the
generalization error is lesser than when a single KRR estimate is fit on the
data: thus providing both statistical and computational advantages over a
single KRR estimate over the entire data (or an averaging over random
partitions as in other recent work, [30]). Lastly, we provide experimental
validation for our proposed estimator and our assumptions.
OpenCL-accelerated object classification in video streams using Spatial Pooler of Hierarchical Temporal Memory
Authors: Maciej Wielgosz, Marcin Pietroń
Date: 2016-08-05 00:00:00
Tasks: CPU, General Classification, GPU

We present a method to classify objects in video streams using a
brain-inspired Hierarchical Temporal Memory (HTM) algorithm. Object
classification is a challenging task where humans still significantly
outperform machine learning algorithms due to their unique capabilities. We
have implemented a system which achieves very promising performance in terms of
recognition accuracy. Unfortunately, conducting more advanced experiments is
very computationally demanding; some of the trials run on a standard CPU may
take as long as several days for 960x540 video streams frames. Therefore we
have decided to accelerate selected parts of the system using OpenCL. In
particular, we seek to determine to what extent porting selected and
computationally demanding parts of a core may speed up calculations.
  The classification accuracy of the system was examined through a series of
experiments and the performance was given in terms of F1 score as a function of
the number of columns, synapses, $min\_overlap$ and $winners\_set\_size$. The
system achieves the highest F1 score of 0.95 and 0.91 for $min\_overlap=4$ and
256 synapses, respectively. We have also conduced a series of experiments with
different hardware setups and measured CPU/GPU acceleration. The best kernel
speed-up of 632x and 207x was reached for 256 synapses and 1024 columns.
However, overall acceleration including transfer time was significantly lower
and amounted to 6.5x and 3.2x for the same setup.
Enhanced Directional Smoothing Algorithm for Edge-Preserving Smoothing of Synthetic-Aperture Radar Images
Authors: Mario Mastriani, Alberto E. Giraldez
Date: 2016-08-05 00:00:00

Synthetic aperture radar (SAR) images are subject to prominent speckle noise,
which is generally considered a purely multiplicative noise process. In theory,
this multiplicative noise is that the ratio of the standard deviation to the
signal value, the "coefficient of variation," is theoretically constant at
every point in a SAR image. Most of the filters for speckle reduction are based
on this property. Such property is irrelevant for the new filter structure,
which is based on directional smoothing (DS) theory, the enhanced directional
smoothing (EDS) that removes speckle noise from SAR images without blurring
edges. We demonstrate the effectiveness of this new filtering method by
comparing it to established speckle noise removal techniques on SAR images.
De-Conflated Semantic Representations
Authors: Mohammad Taher Pilehvar, Nigel Collier
Date: 2016-08-05 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Semantic Similarity, Semantic Textual Similarity

One major deficiency of most semantic representation techniques is that they
usually model a word type as a single point in the semantic space, hence
conflating all the meanings that the word can have. Addressing this issue by
learning distinct representations for individual meanings of words has been the
subject of several research studies in the past few years. However, the
generated sense representations are either not linked to any sense inventory or
are unreliable for infrequent word senses. We propose a technique that tackles
these problems by de-conflating the representations of words based on the deep
knowledge it derives from a semantic network. Our approach provides multiple
advantages in comparison to the past work, including its high coverage and the
ability to generate accurate representations even for infrequent word senses.
We carry out evaluations on six datasets across two semantic similarity tasks
and report state-of-the-art results on most of them.
Iterative Learning of Answer Set Programs from Context Dependent Examples
Authors: Mark Law, Alessandra Russo, Krysia Broda
Date: 2016-08-05 00:00:00
Tasks: Inductive logic programming

In recent years, several frameworks and systems have been proposed that
extend Inductive Logic Programming (ILP) to the Answer Set Programming (ASP)
paradigm. In ILP, examples must all be explained by a hypothesis together with
a given background knowledge. In existing systems, the background knowledge is
the same for all examples; however, examples may be context-dependent. This
means that some examples should be explained in the context of some
information, whereas others should be explained in different contexts. In this
paper, we capture this notion and present a context-dependent extension of the
Learning from Ordered Answer Sets framework. In this extension, contexts can be
used to further structure the background knowledge. We then propose a new
iterative algorithm, ILASP2i, which exploits this feature to scale up the
existing ILASP2 system to learning tasks with large numbers of examples. We
demonstrate the gain in scalability by applying both algorithms to various
learning tasks. Our results show that, compared to ILASP2, the newly proposed
ILASP2i system can be two orders of magnitude faster and use two orders of
magnitude less memory, whilst preserving the same average accuracy. This paper
is under consideration for acceptance in TPLP.
Classification with Asymmetric Label Noise: Consistency and Maximal Denoising
Authors: Gilles Blanchard, Marek Flaska, Gregory Handy, Sara Pozzi, Clayton Scott
Date: 2013-03-05 00:00:00
Tasks: Classification, Denoising, General Classification

In many real-world classification problems, the labels of training examples
are randomly corrupted. Most previous theoretical work on classification with
label noise assumes that the two classes are separable, that the label noise is
independent of the true class label, or that the noise proportions for each
class are known. In this work, we give conditions that are necessary and
sufficient for the true class-conditional distributions to be identifiable.
These conditions are weaker than those analyzed previously, and allow for the
classes to be nonseparable and the noise levels to be asymmetric and unknown.
The conditions essentially state that a majority of the observed labels are
correct and that the true class-conditional distributions are "mutually
irreducible," a concept we introduce that limits the similarity of the two
distributions. For any label noise problem, there is a unique pair of true
class-conditional distributions satisfying the proposed conditions, and we
argue that this pair corresponds in a certain sense to maximal denoising of the
observed distributions.
  Our results are facilitated by a connection to "mixture proportion
estimation," which is the problem of estimating the maximal proportion of one
distribution that is present in another. We establish a novel rate of
convergence result for mixture proportion estimation, and apply this to obtain
consistency of a discrimination rule based on surrogate loss minimization.
Experimental results on benchmark data and a nuclear particle classification
problem demonstrate the efficacy of our approach.
Resolving Out-of-Vocabulary Words with Bilingual Embeddings in Machine Translation
Authors: Pranava Swaroop Madhyastha, Cristina España-Bonet
Date: 2016-08-05 00:00:00
Tasks: Machine Translation, Translation, Word Embeddings

Out-of-vocabulary words account for a large proportion of errors in machine
translation systems, especially when the system is used on a different domain
than the one where it was trained. In order to alleviate the problem, we
propose to use a log-bilinear softmax-based model for vocabulary expansion,
such that given an out-of-vocabulary source word, the model generates a
probabilistic list of possible translations in the target language. Our model
uses only word embeddings trained on significantly large unlabelled monolingual
corpora and trains over a fairly small, word-to-word bilingual dictionary. We
input this probabilistic list into a standard phrase-based statistical machine
translation system and obtain consistent improvements in translation quality on
the English-Spanish language pair. Especially, we get an improvement of 3.9
BLEU points when tested over an out-of-domain test set.
Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization
Authors: Changyou Chen, David Carlson, Zhe Gan, Chunyuan Li, Lawrence Carin
Date: 2015-12-25 00:00:00
Tasks: Stochastic Optimization

Stochastic gradient Markov chain Monte Carlo (SG-MCMC) methods are Bayesian
analogs to popular stochastic optimization methods; however, this connection is
not well studied. We explore this relationship by applying simulated annealing
to an SGMCMC algorithm. Furthermore, we extend recent SG-MCMC methods with two
key components: i) adaptive preconditioners (as in ADAgrad or RMSprop), and ii)
adaptive element-wise momentum weights. The zero-temperature limit gives a
novel stochastic optimization method with adaptive element-wise momentum
weights, while conventional optimization methods only have a shared, static
momentum weight. Under certain assumptions, our theoretical analysis suggests
the proposed simulated annealing approach converges close to the global optima.
Experiments on several deep neural network models show state-of-the-art results
compared to related stochastic optimization algorithms.
Blind Deconvolution of PET Images using Anatomical Priors
Authors: Stéphanie Guérit, Adriana González, Anne Bol, John A. Lee, Laurent Jacques
Date: 2016-08-05 00:00:00

Images from positron emission tomography (PET) provide metabolic information
about the human body. They present, however, a spatial resolution that is
limited by physical and instrumental factors often modeled by a blurring
function. Since this function is typically unknown, blind deconvolution (BD)
techniques are needed in order to produce a useful restored PET image. In this
work, we propose a general BD technique that restores a low resolution blurry
image using information from data acquired with a high resolution modality
(e.g., CT-based delineation of regions with uniform activity in PET images).
The proposed BD method is validated on synthetic and actual phantoms.
Forward Stagewise Additive Model for Collaborative Multiview Boosting
Authors: Avisek Lahiri, Biswajit Paria, Prabir Kumar Biswas
Date: 2016-08-05 00:00:00
Tasks: Ensemble Learning, General Classification, model, Multiview Learning

Multiview assisted learning has gained significant attention in recent years
in supervised learning genre. Availability of high performance computing
devices enables learning algorithms to search simultaneously over multiple
views or feature spaces to obtain an optimum classification performance. The
paper is a pioneering attempt of formulating a mathematical foundation for
realizing a multiview aided collaborative boosting architecture for multiclass
classification. Most of the present algorithms apply multiview learning
heuristically without exploring the fundamental mathematical changes imposed on
traditional boosting. Also, most of the algorithms are restricted to two class
or view setting. Our proposed mathematical framework enables collaborative
boosting across any finite dimensional view spaces for multiclass learning. The
boosting framework is based on forward stagewise additive model which minimizes
a novel exponential loss function. We show that the exponential loss function
essentially captures difficulty of a training sample space instead of the
traditional `1/0' loss. The new algorithm restricts a weak view from over
learning and thereby preventing overfitting. The model is inspired by our
earlier attempt on collaborative boosting which was devoid of mathematical
justification. The proposed algorithm is shown to converge much nearer to
global minimum in the exponential loss space and thus supersedes our previous
algorithm. The paper also presents analytical and numerical analysis of
convergence and margin bounds for multiview boosting algorithms and we show
that our proposed ensemble learning manifests lower error bound and higher
margin compared to our previous model. Also, the proposed model is compared
with traditional boosting and recent multiview boosting algorithms.
Fusing Deep Convolutional Networks for Large Scale Visual Concept Classification
Authors: Hilal Ergun, Mustafa Sert
Date: 2016-08-05 00:00:00
Tasks: Action Recognition, Classification, Data Augmentation, Event Detection, General Classification, image-classification, Image Classification, object-detection, Object Detection, Temporal Action Localization

Deep learning architectures are showing great promise in various computer
vision domains including image classification, object detection, event
detection and action recognition. In this study, we investigate various aspects
of convolutional neural networks (CNNs) from the big data perspective. We
analyze recent studies and different network architectures both in terms of
running time and accuracy. We present extensive empirical information along
with best practices for big data practitioners. Using these best practices we
propose efficient fusion mechanisms both for single and multiple network
models. We present state-of-the art results on benchmark datasets while keeping
computational costs at a lower level. Another contribution of our paper is that
these state-of-the-art results can be reached without using extensive data
augmentation techniques.
A simple method for estimating the fractal dimension from digital images: The compression dimension
Authors: P. Chamorro-Posada
Date: 2016-02-03 00:00:00

The fractal structure of real world objects is often analyzed using digital
images. In this context, the compression fractal dimension is put forward. It
provides a simple method for the direct estimation of the dimension of fractals
stored as digital image files. The computational scheme can be implemented
using readily available free software. Its simplicity also makes it very
interesting for introductory elaborations of basic concepts of fractal
geometry, complexity, and information theory. A test of the computational
scheme using limited-quality images of well-defined fractal sets obtained from
the Internet and free software has been performed. Also, a systematic
evaluation of the proposed method using computer generated images of the
Weierstrass cosine function shows an accuracy comparable to those of the
methods most commonly used to estimate the dimension of fractal data sequences
applied to the same test problem.
The Power of Non-Ground Rules in Answer Set Programming
Authors: Manuel Bichler, Michael Morak, Stefan Woltran
Date: 2016-08-05 00:00:00

Answer set programming (ASP) is a well-established logic programming language
that offers an intuitive, declarative syntax for problem solving. In its
traditional application, a fixed ASP program for a given problem is designed
and the actual instance of the problem is fed into the program as a set of
facts. This approach typically results in programs with comparably short and
simple rules. However, as is known from complexity analysis, such an approach
limits the expressive power of ASP; in fact, an entire NP-check can be encoded
into a single large rule body of bounded arity that performs both a guess and a
check within the same rule.
  Here, we propose a novel paradigm for encoding hard problems in ASP by making
explicit use of large rules which depend on the actual instance of the problem.
We illustrate how this new encoding paradigm can be used, providing examples of
problems from the first, second, and even third level of the polynomial
hierarchy. As state-of-the-art solvers are tuned towards short rules, rule
decomposition is a key technique in the practical realization of our approach.
We also provide some preliminary benchmarks which indicate that giving up the
convenient way of specifying a fixed program can lead to a significant
speed-up.
  This paper is under consideration for acceptance into TPLP.
Paraphrase Generation from Latent-Variable PCFGs for Semantic Parsing
Authors: Shashi Narayan, Siva Reddy, Shay B. Cohen
Date: 2016-01-22 00:00:00
Proceeding: WS 2016 9
Tasks: Open-Domain Question Answering, Paraphrase Generation, Question Answering, Semantic Parsing, Sentence

One of the limitations of semantic parsing approaches to open-domain question
answering is the lexicosyntactic gap between natural language questions and
knowledge base entries -- there are many ways to ask a question, all with the
same answer. In this paper we propose to bridge this gap by generating
paraphrases of the input question with the goal that at least one of them will
be correctly mapped to a knowledge-base query. We introduce a novel grammar
model for paraphrase generation that does not require any sentence-aligned
paraphrase corpus. Our key idea is to leverage the flexibility and scalability
of latent-variable probabilistic context-free grammars to sample paraphrases.
We do an extrinsic evaluation of our paraphrases by plugging them into a
semantic parser for Freebase. Our evaluation experiments on the WebQuestions
benchmark dataset show that the performance of the semantic parser
significantly improves over strong baselines.
Jointly Learning Non-negative Projection and Dictionary with Discriminative Graph Constraints for Classification
Authors: Weiyang Liu, Zhiding Yu, Yandong Wen, Rongmei Lin, Meng Yang
Date: 2015-11-14 00:00:00
Tasks: Dictionary Learning, General Classification

Sparse coding with dictionary learning (DL) has shown excellent
classification performance. Despite the considerable number of existing works,
how to obtain features on top of which dictionaries can be better learned
remains an open and interesting question. Many current prevailing DL methods
directly adopt well-performing crafted features. While such strategy may
empirically work well, it ignores certain intrinsic relationship between
dictionaries and features. We propose a framework where features and
dictionaries are jointly learned and optimized. The framework, named joint
non-negative projection and dictionary learning (JNPDL), enables interaction
between the input features and the dictionaries. The non-negative projection
leads to discriminative parts-based object features while DL seeks a more
suitable representation. Discriminative graph constraints are further imposed
to simultaneously maximize intra-class compactness and inter-class
separability. Experiments on both image and image set classification show the
excellent performance of JNPDL by outperforming several state-of-the-art
approaches.