pwc-archive/papers-with-abstracts huggingface dataset:
BayesDB: A probabilistic programming system for querying the probable implications of data
Authors: Vikash Mansinghka, Richard Tibbetts, Jay Baxter, Pat Shafto, Baxter Eaves
Date: 2015-12-15 00:00:00
Tasks: Probabilistic Programming

Is it possible to make statistical inference broadly accessible to
non-statisticians without sacrificing mathematical rigor or inference quality?
This paper describes BayesDB, a probabilistic programming platform that aims to
enable users to query the probable implications of their data as directly as
SQL databases enable them to query the data itself. This paper focuses on four
aspects of BayesDB: (i) BQL, an SQL-like query language for Bayesian data
analysis, that answers queries by averaging over an implicit space of
probabilistic models; (ii) techniques for implementing BQL using a broad class
of multivariate probabilistic models; (iii) a semi-parametric Bayesian
model-builder that auomatically builds ensembles of factorial mixture models to
serve as baselines; and (iv) MML, a "meta-modeling" language for imposing
qualitative constraints on the model-builder and combining baseline models with
custom algorithmic and statistical models that can be implemented in external
software. BayesDB is illustrated using three applications: cleaning and
exploring a public database of Earth satellites; assessing the evidence for
temporal dependence between macroeconomic indicators; and analyzing a salary
survey.
An Average Classification Algorithm
Authors: Brendan van Rooyen, Aditya Krishna Menon, Robert C. Williamson
Date: 2015-06-04 00:00:00
Tasks: Classification, General Classification

Many classification algorithms produce a classifier that is a weighted
average of kernel evaluations. When working with a high or infinite dimensional
kernel, it is imperative for speed of evaluation and storage issues that as few
training samples as possible are used in the kernel expansion. Popular existing
approaches focus on altering standard learning algorithms, such as the Support
Vector Machine, to induce sparsity, as well as post-hoc procedures for sparse
approximations. Here we adopt the latter approach. We begin with a very simple
classifier, given by the kernel mean $$ f(x) = \frac{1}{n}
\sum\limits_{i=i}^{n} y_i K(x_i,x) $$ We then find a sparse approximation to
this kernel mean via herding. The result is an accurate, easily parallelized
algorithm for learning classifiers.
Adopting Robustness and Optimality in Fitting and Learning
Authors: Zhiguang Wang, Tim Oates, James Lo
Date: 2015-10-13 00:00:00

We generalized a modified exponentialized estimator by pushing the robust-optimal (RO) index $\lambda$ to $-\infty$ for achieving robustness to outliers by optimizing a quasi-Minimin function. The robustness is realized and controlled adaptively by the RO index without any predefined threshold. Optimality is guaranteed by expansion of the convexity region in the Hessian matrix to largely avoid local optima. Detailed quantitative analysis on both robustness and optimality are provided. The results of proposed experiments on fitting tasks for three noisy non-convex functions and the digits recognition task on the MNIST dataset consolidate the conclusions.
Conditions for Normative Decision Making at the Fire Ground
Authors: Adam Krasuski
Date: 2015-12-15 00:00:00
Tasks: Decision Making, Descriptive

We discuss the changes in an attitude to decision making at the fire ground.
The changes are driven by the recent technological shift. The emerging new
approaches in sensing and data processing (under common umbrella of
Cyber-Physical Systems) allow for leveling off the gap, between humans and
machines, in perception of the fire ground. Furthermore, results from
descriptive decision theory question the rationality of human choices. This
creates the need for searching and testing new approaches for decision making
during emergency. We propose the framework that addresses this need. The
primary feature of the framework are possibilities for incorporation of
normative and prescriptive approaches to decision making. The framework also
allows for comparison of the performance of decisions, between human and
machine.
An Operator for Entity Extraction in MapReduce
Authors: Ndapandula Nakashole
Date: 2015-12-15 00:00:00
Tasks: Entity Extraction using GAN

Dictionary-based entity extraction involves finding mentions of dictionary
entities in text. Text mentions are often noisy, containing spurious or missing
words. Efficient algorithms for detecting approximate entity mentions follow
one of two general techniques. The first approach is to build an index on the
entities and perform index lookups of document substrings. The second approach
recognizes that the number of substrings generated from documents can explode
to large numbers, to get around this, they use a filter to prune many such
substrings which do not match any dictionary entity and then only verify the
remaining substrings if they are entity mentions of dictionary entities, by
means of a text join. The choice between the index-based approach and the
filter & verification-based approach is a case-to-case decision as the best
approach depends on the characteristics of the input entity dictionary, for
example frequency of entity mentions. Choosing the right approach for the
setting can make a substantial difference in execution time. Making this choice
is however non-trivial as there are parameters within each of the approaches
that make the space of possible approaches very large. In this paper, we
present a cost-based operator for making the choice among execution plans for
entity extraction. Since we need to deal with large dictionaries and even
larger large datasets, our operator is developed for implementations of
MapReduce distributed algorithms.
Context Driven Label Fusion for segmentation of Subcutaneous and Visceral Fat in CT Volumes
Authors: Sarfaraz Hussein, Aileen Green, Arjun Watane, Georgios Papadakis, Medhat Osman, Ulas Bagci
Date: 2015-12-15 00:00:00
Tasks: Computed Tomography (CT), Image Segmentation, Segmentation, Semantic Segmentation

Quantification of adipose tissue (fat) from computed tomography (CT) scans is
conducted mostly through manual or semi-automated image segmentation algorithms
with limited efficacy. In this work, we propose a completely unsupervised and
automatic method to identify adipose tissue, and then separate Subcutaneous
Adipose Tissue (SAT) from Visceral Adipose Tissue (VAT) at the abdominal
region. We offer a three-phase pipeline consisting of (1) Initial boundary
estimation using gradient points, (2) boundary refinement using Geometric
Median Absolute Deviation and Appearance based Local Outlier Scores (3) Context
driven label fusion using Conditional Random Fields (CRF) to obtain the final
boundary between SAT and VAT. We evaluate the proposed method on 151 abdominal
CT scans and obtain state-of-the-art 94% and 91% dice similarity scores for SAT
and VAT segmentation, as well as significant reduction in fat quantification
error measure.
Relative Density and Exact Recovery in Heterogeneous Stochastic Block Models
Authors: Amin Jalali, Qiyang Han, Ioana Dumitriu, Maryam Fazel
Date: 2015-12-15 00:00:00
Tasks: Stochastic Block Model

The Stochastic Block Model (SBM) is a widely used random graph model for
networks with communities. Despite the recent burst of interest in recovering
communities in the SBM from statistical and computational points of view, there
are still gaps in understanding the fundamental information theoretic and
computational limits of recovery. In this paper, we consider the SBM in its
full generality, where there is no restriction on the number and sizes of
communities or how they grow with the number of nodes, as well as on the
connection probabilities inside or across communities. This generality allows
us to move past the artifacts of homogenous SBM, and understand the right
parameters (such as the relative densities of communities) that define the
various recovery thresholds. We outline the implications of our generalizations
via a set of illustrative examples. For instance, $\log n$ is considered to be
the standard lower bound on the cluster size for exact recovery via convex
methods, for homogenous SBM. We show that it is possible, in the right
circumstances (when sizes are spread and the smaller the cluster, the denser),
to recover very small clusters (up to $\sqrt{\log n}$ size), if there are just
a few of them (at most polylogarithmic in $n$).
Strategies for Training Large Vocabulary Neural Language Models
Authors: Welin Chen, David Grangier, Michael Auli
Date: 2015-12-15 00:00:00
Proceeding: ACL 2016 8
Tasks: Machine Translation, speech-recognition, Speech Recognition, Translation

Training neural network language models over large vocabularies is still
computationally very costly compared to count-based models such as Kneser-Ney.
At the same time, neural language models are gaining popularity for many
applications such as speech recognition and machine translation whose success
depends on scalability. We present a systematic comparison of strategies to
represent and train large vocabularies, including softmax, hierarchical
softmax, target sampling, noise contrastive estimation and self normalization.
We further extend self normalization to be a proper estimator of likelihood and
introduce an efficient variant of softmax. We evaluate each method on three
popular benchmarks, examining performance on rare words, the speed/accuracy
trade-off and complementarity to Kneser-Ney.
Fast Steerable Principal Component Analysis
Authors: Zhizhen Zhao, Yoel Shkolnisky, Amit Singer
Date: 2014-12-02 00:00:00

Cryo-electron microscopy nowadays often requires the analysis of hundreds of
thousands of 2D images as large as a few hundred pixels in each direction. Here
we introduce an algorithm that efficiently and accurately performs principal
component analysis (PCA) for a large set of two-dimensional images, and, for
each image, the set of its uniform rotations in the plane and their
reflections. For a dataset consisting of $n$ images of size $L \times L$
pixels, the computational complexity of our algorithm is $O(nL^3 + L^4)$, while
existing algorithms take $O(nL^4)$. The new algorithm computes the expansion
coefficients of the images in a Fourier-Bessel basis efficiently using the
non-uniform fast Fourier transform. We compare the accuracy and efficiency of
the new algorithm with traditional PCA and existing algorithms for steerable
PCA.
Radon-Nikodym approximation in application to image analysis
Authors: Vladislav Gennadievich Malyshkin
Date: 2015-11-05 00:00:00
Tasks: Image Reconstruction

For an image pixel information can be converted to the moments of some basis
$Q_k$, e.g. Fourier-Mellin, Zernike, monomials, etc. Given sufficient number of
moments pixel information can be completely recovered, for insufficient number
of moments only partial information can be recovered and the image
reconstruction is, at best, of interpolatory type. Standard approach is to
present interpolated value as a linear combination of basis functions, what is
equivalent to least squares expansion. However, recent progress in numerical
stability of moments estimation allows image information to be recovered from
moments in a completely different manner, applying Radon-Nikodym type of
expansion, what gives the result as a ratio of two quadratic forms of basis
functions. In contrast with least squares the Radon-Nikodym approach has
oscillation near the boundaries very much suppressed and does not diverge
outside of basis support. While least squares theory operate with vectors
$<fQ_k>$, Radon-Nikodym theory operates with matrices $<fQ_jQ_k>$, what make
the approach much more suitable to image transforms and statistical property
estimation.
Norm-Free Radon-Nikodym Approach to Machine Learning
Authors: Vladislav Gennadievich Malyshkin
Date: 2015-12-10 00:00:00
Tasks: BIG-bench Machine Learning

For Machine Learning (ML) classification problem, where a vector of
$\mathbf{x}$--observations (values of attributes) is mapped to a single $y$
value (class label), a generalized Radon--Nikodym type of solution is proposed.
Quantum--mechanics --like probability states $\psi^2(\mathbf{x})$ are
considered and "Cluster Centers", corresponding to the extremums of
$<y\psi^2(\mathbf{x})>/<\psi^2(\mathbf{x})>$, are found from generalized
eigenvalues problem. The eigenvalues give possible $y^{[i]}$ outcomes and
corresponding to them eigenvectors $\psi^{[i]}(\mathbf{x})$ define "Cluster
Centers". The projection of a $\psi$ state, localized at given $\mathbf{x}$ to
classify, on these eigenvectors define the probability of $y^{[i]}$ outcome,
thus avoiding using a norm ($L^2$ or other types), required for "quality
criteria" in a typical Machine Learning technique. A coverage of each `Cluster
Center" is calculated, what potentially allows to separate system properties
(described by $y^{[i]}$ outcomes) and system testing conditions (described by
$C^{[i]}$ coverage). As an example of such application $y$ distribution
estimator is proposed in a form of pairs $(y^{[i]},C^{[i]})$, that can be
considered as Gauss quadratures generalization. This estimator allows to
perform $y$ probability distribution estimation in a strongly non--Gaussian
case.
Increasing the Action Gap: New Operators for Reinforcement Learning
Authors: Marc G. Bellemare, Georg Ostrovski, Arthur Guez, Philip S. Thomas, Rémi Munos
Date: 2015-12-15 00:00:00
Tasks: Atari Games, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper introduces new optimality-preserving operators on Q-functions. We
first describe an operator for tabular representations, the consistent Bellman
operator, which incorporates a notion of local policy consistency. We show that
this local consistency leads to an increase in the action gap at each state;
increasing this gap, we argue, mitigates the undesirable effects of
approximation and estimation errors on the induced greedy policies. This
operator can also be applied to discretized continuous space and time problems,
and we provide empirical results evidencing superior performance in this
context. Extending the idea of a locally consistent operator, we then derive
sufficient conditions for an operator to preserve optimality, leading to a
family of operators which includes our consistent Bellman operator. As
corollaries we provide a proof of optimality for Baird's advantage learning
algorithm and derive other gap-increasing operators with interesting
properties. We conclude with an empirical study on 60 Atari 2600 games
illustrating the strong potential of these new operators.
Energy-Efficient Classification for Anomaly Detection: The Wireless Channel as a Helper
Authors: Kiril Ralinovski, Mario Goldenbaum, Sławomir Stańczak
Date: 2015-12-15 00:00:00
Tasks: Anomaly Detection, Fault Diagnosis, General Classification

Anomaly detection has various applications including condition monitoring and
fault diagnosis. The objective is to sense the environment, learn the normal
system state, and then periodically classify whether the instantaneous state
deviates from the normal one or not. A flexible and cost-effective way of
monitoring a system state is to use a wireless sensor network. In the
traditional approach, the sensors encode their observations and transmit them
to a fusion center by means of some interference avoiding channel access
method. The fusion center then decodes all the data and classifies the
corresponding system state. As this approach can be highly inefficient in terms
of energy consumption, in this paper we propose a transmission scheme that
exploits interference for carrying out the anomaly detection directly in the
air. In other words, the wireless channel helps the fusion center to retrieve
the sought classification outcome immediately from the channel output. To
achieve this, the chosen learning model is linear support vector machines.
After discussing the proposed scheme and proving its reliability, we present
numerical examples demonstrating that the scheme reduces the energy consumption
for anomaly detection by up to 53% compared to a strategy that uses time
division multiple-access.
Causal and anti-causal learning in pattern recognition for neuroimaging
Authors: Sebastian Weichwald, Bernhard Schölkopf, Tonio Ball, Moritz Grosse-Wentrup
Date: 2015-12-15 00:00:00
Tasks: Causal Inference

Pattern recognition in neuroimaging distinguishes between two types of
models: encoding- and decoding models. This distinction is based on the insight
that brain state features, that are found to be relevant in an experimental
paradigm, carry a different meaning in encoding- than in decoding models. In
this paper, we argue that this distinction is not sufficient: Relevant features
in encoding- and decoding models carry a different meaning depending on whether
they represent causal- or anti-causal relations. We provide a theoretical
justification for this argument and conclude that causal inference is essential
for interpretation in neuroimaging.
Using Linear Constraints for Logic Program Termination Analysis
Authors: Marco Calautti, Sergio Greco, Cristian Molinaro, Irina Trubitsyna
Date: 2015-12-13 00:00:00

It is widely acknowledged that function symbols are an important feature in
answer set programming, as they make modeling easier, increase the expressive
power, and allow us to deal with infinite domains. The main issue with their
introduction is that the evaluation of a program might not terminate and
checking whether it terminates or not is undecidable. To cope with this
problem, several classes of logic programs have been proposed where the use of
function symbols is restricted but the program evaluation termination is
guaranteed. Despite the significant body of work in this area, current
approaches do not include many simple practical programs whose evaluation
terminates. In this paper, we present the novel classes of rule-bounded and
cycle-bounded programs, which overcome different limitations of current
approaches by performing a more global analysis of how terms are propagated
from the body to the head of rules. Results on the correctness, the complexity,
and the expressivity of the proposed approach are provided.
Learning optimal nonlinearities for iterative thresholding algorithms
Authors: Ulugbek S. Kamilov, Hassan Mansour
Date: 2015-12-15 00:00:00

Iterative shrinkage/thresholding algorithm (ISTA) is a well-studied method
for finding sparse solutions to ill-posed inverse problems. In this letter, we
present a data-driven scheme for learning optimal thresholding functions for
ISTA. The proposed scheme is obtained by relating iterations of ISTA to layers
of a simple deep neural network (DNN) and developing a corresponding error
backpropagation algorithm that allows to fine-tune the thresholding functions.
Simulations on sparse statistical signals illustrate potential gains in
estimation quality due to the proposed data adaptive ISTA.
Joint Image-Text News Topic Detection and Tracking with And-Or Graph Representation
Authors: Weixin Li, Jungseock Joo, Hang Qi, Song-Chun Zhu
Date: 2015-12-15 00:00:00
Tasks: Clustering

In this paper, we aim to develop a method for automatically detecting and
tracking topics in broadcast news. We present a hierarchical And-Or graph (AOG)
to jointly represent the latent structure of both texts and visuals. The AOG
embeds a context sensitive grammar that can describe the hierarchical
composition of news topics by semantic elements about people involved, related
places and what happened, and model contextual relationships between elements
in the hierarchy. We detect news topics through a cluster sampling process
which groups stories about closely related events. Swendsen-Wang Cuts (SWC), an
effective cluster sampling algorithm, is adopted for traversing the solution
space and obtaining optimal clustering solutions by maximizing a Bayesian
posterior probability. Topics are tracked to deal with the continuously updated
news streams. We generate topic trajectories to show how topics emerge, evolve
and disappear over time. The experimental results show that our method can
explicitly describe the textual and visual data in news videos and produce
meaningful topic trajectories. Our method achieves superior performance
compared to state-of-the-art methods on both a public dataset Reuters-21578 and
a self-collected dataset named UCLA Broadcast News Dataset.
Learning Discrete Bayesian Networks from Continuous Data
Authors: Yi-Chun Chen, Tim Allan Wheeler, Mykel John Kochenderfer
Date: 2015-12-08 00:00:00

Learning Bayesian networks from raw data can help provide insights into the
relationships between variables. While real data often contains a mixture of
discrete and continuous-valued variables, many Bayesian network structure
learning algorithms assume all random variables are discrete. Thus, continuous
variables are often discretized when learning a Bayesian network. However, the
choice of discretization policy has significant impact on the accuracy, speed,
and interpretability of the resulting models. This paper introduces a
principled Bayesian discretization method for continuous variables in Bayesian
networks with quadratic complexity instead of the cubic complexity of other
standard techniques. Empirical demonstrations show that the proposed method is
superior to the established minimum description length algorithm. In addition,
this paper shows how to incorporate existing methods into the structure
learning process to discretize all continuous variables and simultaneously
learn Bayesian network structures.
Computational Models for Multiview Dense Depth Maps of Dynamic Scene
Authors: Qifei Wang
Date: 2015-12-08 00:00:00

This paper reviews the recent progresses of the depth map generation for
dynamic scene and its corresponding computational models. This paper mainly
covers the homogeneous ambiguity models in depth sensing, resolution models in
depth processing, and consistency models in depth optimization. We also
summarize the future work in the depth map generation.
Simple Baseline for Visual Question Answering
Authors: Bolei Zhou, Yuandong Tian, Sainbayar Sukhbaatar, Arthur Szlam, Rob Fergus
Date: 2015-12-07 00:00:00
Tasks: Visual Question Answering, Visual Question Answering (VQA)

We describe a very simple bag-of-words baseline for visual question
answering. This baseline concatenates the word features from the question and
CNN features from the image to predict the answer. When evaluated on the
challenging VQA dataset [2], it shows comparable performance to many recent
approaches using recurrent neural networks. To explore the strength and
weakness of the trained model, we also provide an interactive web demo and
open-source code. .
Neural Network Matrix Factorization
Authors: Gintare Karolina Dziugaite, Daniel M. Roy
Date: 2015-11-19 00:00:00
Tasks: Collaborative Filtering

Data often comes in the form of an array or matrix. Matrix factorization
techniques attempt to recover missing or corrupted entries by assuming that the
matrix can be written as the product of two low-rank matrices. In other words,
matrix factorization approximates the entries of the matrix by a simple, fixed
function---namely, the inner product---acting on the latent feature vectors for
the corresponding row and column. Here we consider replacing the inner product
by an arbitrary function that we learn from the data at the same time as we
learn the latent feature vectors. In particular, we replace the inner product
by a multi-layer feed-forward neural network, and learn by alternating between
optimizing the network for fixed latent features, and optimizing the latent
features for a fixed network. The resulting approach---which we call neural
network matrix factorization or NNMF, for short---dominates standard low-rank
techniques on a suite of benchmark but is dominated by some recent proposals
that take advantage of the graph features. Given the vast range of
architectures, activation functions, regularizers, and optimization techniques
that could be used within the NNMF framework, it seems likely the true
potential of the approach has yet to be reached.