pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Level Cause-Effect Systems
Authors: Krzysztof Chalupka, Pietro Perona, Frederick Eberhardt
Date: 2015-12-25 00:00:00

We present a domain-general account of causation that applies to settings in
which macro-level causal relations between two systems are of interest, but the
relevant causal features are poorly understood and have to be aggregated from
vast arrays of micro-measurements. Our approach generalizes that of Chalupka et
al. (2015) to the setting in which the macro-level effect is not specified. We
formalize the connection between micro- and macro-variables in such situations
and provide a coherent framework describing causal relations at multiple levels
of analysis. We present an algorithm that discovers macro-variable causes and
effects from micro-level measurements obtained from an experiment. We further
show how to design experiments to discover macro-variables from observational
micro-variable data. Finally, we show that under specific conditions, one can
identify multiple levels of causal structure. Throughout the article, we use a
simulated neuroscience multi-unit recording experiment to illustrate the ideas
and the algorithms.
Sufficient Forecasting Using Factor Models
Authors: Jianqing Fan, Lingzhou Xue, Jiawei Yao
Date: 2015-05-27 00:00:00
Tasks: Dimensionality Reduction, regression, Time Series Analysis

We consider forecasting a single time series when there is a large number of
predictors and a possible nonlinear effect. The dimensionality was first
reduced via a high-dimensional (approximate) factor model implemented by the
principal component analysis. Using the extracted factors, we develop a novel
forecasting method called the sufficient forecasting, which provides a set of
sufficient predictive indices, inferred from high-dimensional predictors, to
deliver additional predictive power. The projected principal component analysis
will be employed to enhance the accuracy of inferred factors when a
semi-parametric (approximate) factor model is assumed. Our method is also
applicable to cross-sectional sufficient regression using extracted factors.
The connection between the sufficient forecasting and the deep learning
architecture is explicitly stated. The sufficient forecasting correctly
estimates projection indices of the underlying factors even in the presence of
a nonparametric forecasting function. The proposed method extends the
sufficient dimension reduction to high-dimensional regimes by condensing the
cross-sectional information through factor models. We derive asymptotic
properties for the estimate of the central subspace spanned by these projection
directions as well as the estimates of the sufficient predictive indices. We
further show that the natural method of running multiple regression of target
on estimated factors yields a linear estimate that actually falls into this
central subspace. Our method and theory allow the number of predictors to be
larger than the number of observations. We finally demonstrate that the
sufficient forecasting improves upon the linear forecasting in both simulation
studies and an empirical study of forecasting macroeconomic variables.
Probabilistic Model-Based Approach for Heart Beat Detection
Authors: Hugh Chen, Yusuf Erol, Eric Shen, Stuart Russell
Date: 2015-12-24 00:00:00
Tasks: Bayesian Inference, model

Nowadays, hospitals are ubiquitous and integral to modern society. Patients
flow in and out of a veritable whirlwind of paperwork, consultations, and
potential inpatient admissions, through an abstracted system that is not
without flaws. One of the biggest flaws in the medical system is perhaps an
unexpected one: the patient alarm system. One longitudinal study reported an
88.8% rate of false alarms, with other studies reporting numbers of similar
magnitudes. These false alarm rates lead to a number of deleterious effects
that manifest in a significantly lower standard of care across clinics.
  This paper discusses a model-based probabilistic inference approach to
identifying variables at a detection level. We design a generative model that
complies with an overview of human physiology and perform approximate Bayesian
inference. One primary goal of this paper is to justify a Bayesian modeling
approach to increasing robustness in a physiological domain.
  We use three data sets provided by Physionet, a research resource for complex
physiological signals, in the form of the Physionet 2014 Challenge set-p1 and
set-p2, as well as the MGH/MF Waveform Database. On the extended data set our
algorithm is on par with the other top six submissions to the Physionet 2014
challenge.
Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network
Authors: Seunghoon Hong, Junhyuk Oh, Bohyung Han, Honglak Lee
Date: 2015-12-24 00:00:00
Proceeding: CVPR 2016 6
Tasks: Decoder, Foreground Segmentation, Segmentation, Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

We propose a novel weakly-supervised semantic segmentation algorithm based on
Deep Convolutional Neural Network (DCNN). Contrary to existing
weakly-supervised approaches, our algorithm exploits auxiliary segmentation
annotations available for different categories to guide segmentations on images
with only image-level class labels. To make the segmentation knowledge
transferrable across categories, we design a decoupled encoder-decoder
architecture with attention model. In this architecture, the model generates
spatial highlights of each category presented in an image using an attention
model, and subsequently generates foreground segmentation for each highlighted
region using decoder. Combining attention model, we show that the decoder
trained with segmentation annotations in different categories can boost the
performance of weakly-supervised semantic segmentation. The proposed algorithm
demonstrates substantially improved performance compared to the
state-of-the-art weakly-supervised techniques in challenging PASCAL VOC 2012
dataset when our model is trained with the annotations in 60 exclusive
categories in Microsoft COCO dataset.
Feature Elimination in Kernel Machines in moderately high dimensions
Authors: Sayan Dasgupta, Yair Goldberg, Michael Kosorok
Date: 2013-04-18 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We develop an approach for feature elimination in statistical learning with
kernel machines, based on recursive elimination of features.We present
theoretical properties of this method and show that it is uniformly consistent
in finding the correct feature space under certain generalized assumptions.We
present four case studies to show that the assumptions are met in most
practical situations and present simulation results to demonstrate performance
of the proposed approach.
The Information-theoretic and Algorithmic Approach to Human, Animal and Artificial Cognition
Authors: Nicolas Gauvrit, Hector Zenil, Jesper Tegnér
Date: 2015-01-17 00:00:00

We survey concepts at the frontier of research connecting artificial, animal
and human cognition to computation and information processing---from the Turing
test to Searle's Chinese Room argument, from Integrated Information Theory to
computational and algorithmic complexity. We start by arguing that passing the
Turing test is a trivial computational problem and that its pragmatic
difficulty sheds light on the computational nature of the human mind more than
it does on the challenge of artificial intelligence. We then review our
proposed algorithmic information-theoretic measures for quantifying and
characterizing cognition in various forms. These are capable of accounting for
known biases in human behavior, thus vindicating a computational algorithmic
view of cognition as first suggested by Turing, but this time rooted in the
concept of algorithmic probability, which in turn is based on computational
universality while being independent of computational model, and which has the
virtue of being predictive and testable as a model theory of cognitive
behavior.
Distinguishing cause from effect using observational data: methods and benchmarks
Authors: Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Schölkopf
Date: 2014-12-11 00:00:00
Tasks: Causal Discovery, Causal Inference, Selection bias

The discovery of causal relationships from purely observational data is a
fundamental problem in science. The most elementary form of such a causal
discovery problem is to decide whether X causes Y or, alternatively, Y causes
X, given joint observations of two variables X, Y. An example is to decide
whether altitude causes temperature, or vice versa, given only joint
measurements of both variables. Even under the simplifying assumptions of no
confounding, no feedback loops, and no selection bias, such bivariate causal
discovery problems are challenging. Nevertheless, several approaches for
addressing those problems have been proposed in recent years. We review two
families of such methods: Additive Noise Methods (ANM) and Information
Geometric Causal Inference (IGCI). We present the benchmark CauseEffectPairs
that consists of data for 100 different cause-effect pairs selected from 37
datasets from various domains (e.g., meteorology, biology, medicine,
engineering, economy, etc.) and motivate our decisions regarding the "ground
truth" causal directions of all pairs. We evaluate the performance of several
bivariate causal discovery methods on these real-world benchmark data and in
addition on artificially simulated data. Our empirical results on real-world
data indicate that certain methods are indeed able to distinguish cause from
effect using only purely observational data, although more benchmark data would
be needed to obtain statistically significant conclusions. One of the best
performing methods overall is the additive-noise method originally proposed by
Hoyer et al. (2009), which obtains an accuracy of 63+-10 % and an AUC of
0.74+-0.05 on the real-world benchmark. As the main theoretical contribution of
this work we prove the consistency of that method.
Hardware Architecture for Large Parallel Array of Random Feature Extractors applied to Image Recognition
Authors: Aakash Patil, Shanlan Shen, Enyi Yao, Arindam Basu
Date: 2015-12-24 00:00:00

We demonstrate a low-power and compact hardware implementation of Random
Feature Extractor (RFE) core. With complex tasks like Image Recognition
requiring a large set of features, we show how weight reuse technique can allow
to virtually expand the random features available from RFE core. Further, we
show how to avoid computation cost wasted for propagating "incognizant" or
redundant random features. For proof of concept, we validated our approach by
using our RFE core as the first stage of Extreme Learning Machine (ELM)--a two
layer neural network--and were able to achieve $>97\%$ accuracy on MNIST
database of handwritten digits. ELM's first stage of RFE is done on an analog
ASIC occupying $5$mm$\times5$mm area in $0.35\mu$m CMOS and consuming $5.95$
$\mu$J/classify while using $\approx 5000$ effective hidden neurons. The ELM
second stage consisting of just adders can be implemented as digital circuit
with estimated power consumption of $20.9$ nJ/classify. With a total energy
consumption of only $5.97$ $\mu$J/classify, this low-power mixed signal ASIC
can act as a co-processor in portable electronic gadgets with cameras.
Low-Rank Approximation of Weighted Tree Automata
Authors: Guillaume Rabusseau, Borja Balle, Shay B. Cohen
Date: 2015-11-04 00:00:00

We describe a technique to minimize weighted tree automata (WTA), a powerful
formalisms that subsumes probabilistic context-free grammars (PCFGs) and
latent-variable PCFGs. Our method relies on a singular value decomposition of
the underlying Hankel matrix defined by the WTA. Our main theoretical result is
an efficient algorithm for computing the SVD of an infinite Hankel matrix
implicitly represented as a WTA. We provide an analysis of the approximation
error induced by the minimization, and we evaluate our method on real-world
data originating in newswire treebank. We show that the model achieves lower
perplexity than previous methods for PCFG minimization, and also is much more
stable due to the absence of local optima.
Real-Time Audio-to-Score Alignment of Music Performances Containing Errors and Arbitrary Repeats and Skips
Authors: Tomohiko Nakamura, Eita Nakamura, Shigeki Sagayama
Date: 2015-12-24 00:00:00

This paper discusses real-time alignment of audio signals of music
performance to the corresponding score (a.k.a. score following) which can
handle tempo changes, errors and arbitrary repeats and/or skips (repeats/skips)
in performances. This type of score following is particularly useful in
automatic accompaniment for practices and rehearsals, where errors and
repeats/skips are often made. Simple extensions of the algorithms previously
proposed in the literature are not applicable in these situations for scores of
practical length due to the problem of large computational complexity. To cope
with this problem, we present two hidden Markov models of monophonic
performance with errors and arbitrary repeats/skips, and derive efficient
score-following algorithms with an assumption that the prior probability
distributions of score positions before and after repeats/skips are independent
from each other. We confirmed real-time operation of the algorithms with music
scores of practical length (around 10000 notes) on a modern laptop and their
tracking ability to the input performance within 0.7 s on average after
repeats/skips in clarinet performance data. Further improvements and extension
for polyphonic signals are also discussed.
Differential Evolution with Event-Triggered Impulsive Control
Authors: Wei Du, Sunney Yung Sun Leung, Yang Tang, Athanasios V. Vasilakos
Date: 2015-12-17 00:00:00

Differential evolution (DE) is a simple but powerful evolutionary algorithm,
which has been widely and successfully used in various areas. In this paper, an
event-triggered impulsive control scheme (ETI) is introduced to improve the
performance of DE. Impulsive control, the concept of which derives from control
theory, aims at regulating the states of a network by instantly adjusting the
states of a fraction of nodes at certain instants, and these instants are
determined by event-triggered mechanism (ETM). By introducing impulsive control
and ETM into DE, we hope to change the search performance of the population in
a positive way after revising the positions of some individuals at certain
moments. At the end of each generation, the impulsive control operation is
triggered when the update rate of the population declines or equals to zero. In
detail, inspired by the concepts of impulsive control, two types of impulses
are presented within the framework of DE in this paper: stabilizing impulses
and destabilizing impulses. Stabilizing impulses help the individuals with
lower rankings instantly move to a desired state determined by the individuals
with better fitness values. Destabilizing impulses randomly alter the positions
of inferior individuals within the range of the current population. By means of
intelligently modifying the positions of a part of individuals with these two
kinds of impulses, both exploitation and exploration abilities of the whole
population can be meliorated. In addition, the proposed ETI is flexible to be
incorporated into several state-of-the-art DE variants. Experimental results
over the CEC 2014 benchmark functions exhibit that the developed scheme is
simple yet effective, which significantly improves the performance of the
considered DE algorithms.
RDF2Rules: Learning Rules from RDF Knowledge Bases by Mining Frequent Predicate Cycles
Authors: Zhichun Wang, Juanzi Li
Date: 2015-12-24 00:00:00

Recently, several large-scale RDF knowledge bases have been built and applied
in many knowledge-based applications. To further increase the number of facts
in RDF knowledge bases, logic rules can be used to predict new facts based on
the existing ones. Therefore, how to automatically learn reliable rules from
large-scale knowledge bases becomes increasingly important. In this paper, we
propose a novel rule learning approach named RDF2Rules for RDF knowledge bases.
RDF2Rules first mines frequent predicate cycles (FPCs), a kind of interesting
frequent patterns in knowledge bases, and then generates rules from the mined
FPCs. Because each FPC can produce multiple rules, and effective pruning
strategy is used in the process of mining FPCs, RDF2Rules works very
efficiently. Another advantage of RDF2Rules is that it uses the entity type
information when generates and evaluates rules, which makes the learned rules
more accurate. Experiments show that our approach outperforms the compared
approach in terms of both efficiency and accuracy.
Measuring pattern retention in anonymized data -- where one measure is not enough
Authors: Sam Fletcher, Md Zahidul Islam
Date: 2015-12-24 00:00:00

In this paper, we explore how modifying data to preserve privacy affects the
quality of the patterns discoverable in the data. For any analysis of modified
data to be worth doing, the data must be as close to the original as possible.
Therein lies a problem -- how does one make sure that modified data still
contains the information it had before modification? This question is not the
same as asking if an accurate classifier can be built from the modified data.
Often in the literature, the prediction accuracy of a classifier made from
modified (anonymized) data is used as evidence that the data is similar to the
original. We demonstrate that this is not the case, and we propose a new
methodology for measuring the retention of the patterns that existed in the
original data. We then use our methodology to design three measures that can be
easily implemented, each measuring aspects of the data that no pre-existing
techniques can measure. These measures do not negate the usefulness of
prediction accuracy or other measures -- they are complementary to them, and
support our argument that one measure is almost never enough.
Fast Parallel SVM using Data Augmentation
Authors: Hugh Perkins, Minjie Xu, Jun Zhu, Bo Zhang
Date: 2015-12-24 00:00:00
Tasks: Bayesian Inference, Data Augmentation

As one of the most popular classifiers, linear SVMs still have challenges in
dealing with very large-scale problems, even though linear or sub-linear
algorithms have been developed recently on single machines. Parallel computing
methods have been developed for learning large-scale SVMs. However, existing
methods rely on solving local sub-optimization problems. In this paper, we
develop a novel parallel algorithm for learning large-scale linear SVM. Our
approach is based on a data augmentation equivalent formulation, which casts
the problem of learning SVM as a Bayesian inference problem, for which we can
develop very efficient parallel sampling methods. We provide empirical results
for this parallel sampling SVM, and provide extensions for SVR, non-linear
kernels, and provide a parallel implementation of the Crammer and Singer model.
This approach is very promising in its own right, and further is a very useful
technique to parallelize a broader family of general maximum-margin models.
Harnessing the Deep Net Object Models for Enhancing Human Action Recognition
Authors: O. V. Ramana Murthy, Roland Goecke
Date: 2015-12-21 00:00:00
Tasks: Action Recognition, Object, Temporal Action Localization

In this study, the influence of objects is investigated in the scenario of
human action recognition with large number of classes. We hypothesize that the
objects the humans are interacting will have good say in determining the action
being performed. Especially, if the objects are non-moving, such as objects
appearing in the background, features such as spatio-temporal interest points,
dense trajectories may fail to detect them. Hence we propose to detect objects
using pre-trained object detectors in every frame statically. Trained Deep
network models are used as object detectors. Information from different layers
in conjunction with different encoding techniques is extensively studied to
obtain the richest feature vectors. This technique is observed to yield
state-of-the-art performance on HMDB51 and UCF101 datasets.
Fast Acquisition for Quantitative MRI Maps: Sparse Recovery from Non-linear Measurements
Authors: Anupriya Gogna, Angshul Majumdar
Date: 2015-12-24 00:00:00
Tasks: compressed sensing, Quantitative MRI

This work addresses the problem of estimating proton density and T1 maps from
two partially sampled K-space scans such that the total acquisition time
remains approximately the same as a single scan. Existing multi parametric non
linear curve fitting techniques require a large number (8 or more) of echoes to
estimate the maps resulting in prolonged (clinically infeasible) acquisition
times. Our simulation results show that our method yields very accurate and
robust results from only two partially sampled scans (total scan time being the
same as a single echo MRI). We model PD and T1 maps to be sparse in some
transform domain. The PD map is recovered via standard Compressed Sensing based
recovery technique. Estimating the T1 map requires solving an analysis prior
sparse recovery problem from non linear measurements, since the relationship
between T1 values and intensity values or K space samples is not linear. For
the first time in this work, we propose an algorithm for analysis prior sparse
recovery for non linear measurements. We have compared our approach with the
only existing technique based on matrix factorization from non linear
measurements; our method yields considerably superior results.
Service Choreography, SBVR, and Time
Authors: Nurulhuda A. Manaf, Sotiris Moschoyiannis, Paul Krause
Date: 2015-12-24 00:00:00
Tasks: Formal Logic, Service Composition

We propose the use of structured natural language (English) in specifying
service choreographies, focusing on the what rather than the how of the
required coordination of participant services in realising a business
application scenario. The declarative approach we propose uses the OMG standard
Semantics of Business Vocabulary and Rules (SBVR) as a modelling language. The
service choreography approach has been proposed for describing the global
orderings of the invocations on interfaces of participant services. We
therefore extend SBVR with a notion of time which can capture the coordination
of the participant services, in terms of the observable message exchanges
between them. The extension is done using existing modelling constructs in
SBVR, and hence respects the standard specification. The idea is that users -
domain specialists rather than implementation specialists - can verify the
requested service composition by directly reading the structured English used
by SBVR. At the same time, the SBVR model can be represented in formal logic so
it can be parsed and executed by a machine.
Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks
Authors: Chunyuan Li, Changyou Chen, David Carlson, Lawrence Carin
Date: 2015-12-23 00:00:00

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace and more