pwc-archive/papers-with-abstracts huggingface dataset:
Exploring the Political Agenda of the European Parliament Using a Dynamic Topic Modeling Approach
Authors: Derek Greene, James P. Cross
Date: 2016-07-11 00:00:00
Tasks: Dynamic Topic Modeling

This study analyzes the political agenda of the European Parliament (EP)
plenary, how it has evolved over time, and the manner in which Members of the
European Parliament (MEPs) have reacted to external and internal stimuli when
making plenary speeches. To unveil the plenary agenda and detect latent themes
in legislative speeches over time, MEP speech content is analyzed using a new
dynamic topic modeling method based on two layers of Non-negative Matrix
Factorization (NMF). This method is applied to a new corpus of all English
language legislative speeches in the EP plenary from the period 1999-2014. Our
findings suggest that two-layer NMF is a valuable alternative to existing
dynamic topic modeling approaches found in the literature, and can unveil niche
topics and associated vocabularies not captured by existing methods.
Substantively, our findings suggest that the political agenda of the EP evolves
significantly over time and reacts to exogenous events such as EU Treaty
referenda and the emergence of the Euro-crisis. MEP contributions to the
plenary agenda are also found to be impacted upon by voting behaviour and the
committee structure of the Parliament.
Learning a metric for class-conditional KNN
Authors: Daniel Jiwoong Im, Graham W. Taylor
Date: 2016-07-11 00:00:00
Tasks: Classification, General Classification, Metric Learning, Retrieval

Naive Bayes Nearest Neighbour (NBNN) is a simple and effective framework
which addresses many of the pitfalls of K-Nearest Neighbour (KNN)
classification. It has yielded competitive results on several computer vision
benchmarks. Its central tenet is that during NN search, a query is not compared
to every example in a database, ignoring class information. Instead, NN
searches are performed within each class, generating a score per class. A key
problem with NN techniques, including NBNN, is that they fail when the data
representation does not capture perceptual (e.g.~class-based) similarity. NBNN
circumvents this by using independent engineered descriptors (e.g.~SIFT). To
extend its applicability outside of image-based domains, we propose to learn a
metric which captures perceptual similarity. Similar to how Neighbourhood
Components Analysis optimizes a differentiable form of KNN classification, we
propose "Class Conditional" metric learning (CCML), which optimizes a soft form
of the NBNN selection rule. Typical metric learning algorithms learn either a
global or local metric. However, our proposed method can be adjusted to a
particular level of locality by tuning a single parameter. An empirical
evaluation on classification and retrieval tasks demonstrates that our proposed
method clearly outperforms existing learned distance metrics across a variety
of image and non-image datasets.
Retrospective Causal Inference with Machine Learning Ensembles: An Application to Anti-Recidivism Policies in Colombia
Authors: Cyrus Samii, Laura Paler, Sarah Zukerman Daly
Date: 2016-07-11 00:00:00
Tasks: BIG-bench Machine Learning, Causal Inference, regression

We present new methods to estimate causal effects retrospectively from micro
data with the assistance of a machine learning ensemble. This approach
overcomes two important limitations in conventional methods like regression
modeling or matching: (i) ambiguity about the pertinent retrospective
counterfactuals and (ii) potential misspecification, overfitting, and otherwise
bias-prone or inefficient use of a large identifying covariate set in the
estimation of causal effects. Our method targets the analysis toward a well
defined ``retrospective intervention effect'' (RIE) based on hypothetical
population interventions and applies a machine learning ensemble that allows
data to guide us, in a controlled fashion, on how to use a large identifying
covariate set. We illustrate with an analysis of policy options for reducing
ex-combatant recidivism in Colombia.
Salient Region Detection and Segmentation in Images using Dynamic Mode Decomposition
Authors: Sikha O K, Sachin Kumar S, K. P. Soman
Date: 2016-07-11 00:00:00
Tasks: Saliency Prediction

Visual Saliency is the capability of vision system to select distinctive
parts of scene and reduce the amount of visual data that need to be processed.
The presentpaper introduces (1) a novel approach to detect salient regions by
considering color and luminance based saliency scores using Dynamic Mode
Decomposition (DMD), (2) a new interpretation to use DMD approach in static
image processing. This approach integrates two data analysis methods: (1)
Fourier Transform, (2) Principle Component Analysis.The key idea of our work is
to create a color based saliency map. This is based on the observation
thatsalient part of an image usually have distinct colors compared to the
remaining portion of the image. We have exploited the power of different color
spaces to model the complex and nonlinear behavior of human visual system to
generate a color based saliency map. To further improve the effect of final
saliency map, weutilized luminance information exploiting the fact that human
eye is more sensitive towards brightness than color.The experimental results
shows that our method based on DMD theory is effective in comparison with
previous state-of-art saliency estimation approaches. The approach presented in
this paperis evaluated using ROC curve, F-measure rate, Precision-Recall rate,
AUC score etc.
Enhanced Boolean Correlation Matrix Memory
Authors: Mario Mastriani
Date: 2016-07-11 00:00:00

This paper introduces an Enhanced Boolean version of the Correlation Matrix
Memory (CMM), which is useful to work with binary memories. A novel Boolean
Orthonormalization Process (BOP) is presented to convert a non-orthonormal
Boolean basis, i.e., a set of non-orthonormal binary vectors (in a Boolean
sense) to an orthonormal Boolean basis, i.e., a set of orthonormal binary
vectors (in a Boolean sense). This work shows that it is possible to improve
the performance of Boolean CMM thanks BOP algorithm. Besides, the BOP algorithm
has a lot of additional fields of applications, e.g.: Steganography, Hopfield
Networks, Bi-level image processing, etc. Finally, it is important to mention
that the BOP is an extremely stable and fast algorithm.
Systholic Boolean Orthonormalizer Network in Wavelet Domain for SAR Image Despeckling
Authors: Mario Mastriani
Date: 2016-07-11 00:00:00
Tasks: Sar Image Despeckling

We describe a novel method for removing speckle (in wavelet domain) of
unknown variance from SAR images. The me-thod is based on the following
procedure: We apply 1) Bidimentional Discrete Wavelet Transform (DWT-2D) to the
speckled image, 2) scaling and rounding to the coefficients of the highest
subbands (to obtain integer and positive coefficients), 3) bit-slicing to the
new highest subbands (to obtain bit-planes), 4) then we apply the Systholic
Boolean Orthonormalizer Network (SBON) to the input bit-plane set and we obtain
two orthonormal output bit-plane sets (in a Boolean sense), we project a set on
the other one, by means of an AND operation, and then, 5) we apply
re-assembling, and, 6) re-sca-ling. Finally, 7) we apply Inverse DWT-2D and
reconstruct a SAR image from the modified wavelet coefficients. Despeckling
results compare favorably to the most of methods in use at the moment.
Adaptive Least Mean Squares Estimation of Graph Signals
Authors: Paolo Di Lorenzo, Sergio Barbarossa, Paolo Banelli, Stefania Sardellitti
Date: 2016-02-18 00:00:00
Tasks: Graph Sampling

The aim of this paper is to propose a least mean squares (LMS) strategy for
adaptive estimation of signals defined over graphs. Assuming the graph signal
to be band-limited, over a known bandwidth, the method enables reconstruction,
with guaranteed performance in terms of mean-square error, and tracking from a
limited number of observations over a subset of vertices. A detailed mean
square analysis provides the performance of the proposed method, and leads to
several insights for designing useful sampling strategies for graph signals.
Numerical results validate our theoretical findings, and illustrate the
performance of the proposed method. Furthermore, to cope with the case where
the bandwidth is not known beforehand, we propose a method that performs a
sparse online estimation of the signal support in the (graph) frequency domain,
which enables online adaptation of the graph sampling strategy. Finally, we
apply the proposed method to build the power spatial density cartography of a
given operational region in a cognitive network environment.
Persistence Images: A Stable Vector Representation of Persistent Homology
Authors: Henry Adams, Sofya Chepushtanova, Tegan Emerson, Eric Hanson, Michael Kirby, Francis Motta, Rachel Neville, Chris Peterson, Patrick Shipman, Lori Ziegelmeier
Date: 2015-07-22 00:00:00
Tasks: BIG-bench Machine Learning, Graph Classification, Topological Data Analysis

Many datasets can be viewed as a noisy sampling of an underlying space, and
tools from topological data analysis can characterize this structure for the
purpose of knowledge discovery. One such tool is persistent homology, which
provides a multiscale description of the homological features within a dataset.
A useful representation of this homological information is a persistence
diagram (PD). Efforts have been made to map PDs into spaces with additional
structure valuable to machine learning tasks. We convert a PD to a
finite-dimensional vector representation which we call a persistence image
(PI), and prove the stability of this transformation with respect to small
perturbations in the inputs. The discriminatory power of PIs is compared
against existing methods, showing significant performance gains. We explore the
use of PIs with vector-based machine learning tools, such as linear sparse
support vector machines, which identify features containing discriminating
topological information. Finally, high accuracy inference of parameter values
from the dynamic output of a discrete dynamical system (the linked twist map)
and a partial differential equation (the anisotropic Kuramoto-Sivashinsky
equation) provide a novel application of the discriminatory power of PIs.
Persistent Homology on Grassmann Manifolds for Analysis of Hyperspectral Movies
Authors: Sofya Chepushtanova, Michael Kirby, Chris Peterson, Lori Ziegelmeier
Date: 2016-07-07 00:00:00
Tasks: Topological Data Analysis

The existence of characteristic structure, or shape, in complex data sets has
been recognized as increasingly important for mathematical data analysis. This
realization has motivated the development of new tools such as persistent
homology for exploring topological invariants, or features, in large data sets.
In this paper we apply persistent homology to the characterization of gas
plumes in time dependent sequences of hyperspectral cubes, i.e. the analysis of
4-way arrays. We investigate hyperspectral movies of Long-Wavelength Infrared
data monitoring an experimental release of chemical simulant into the air. Our
approach models regions of interest within the hyperspectral data cubes as
points on the real Grassmann manifold $G(k, n)$ (whose points parameterize the
$k$-dimensional subspaces of $\mathbb{R}^n$), contrasting our approach with the
more standard framework in Euclidean space. An advantage of this approach is
that it allows a sequence of time slices in a hyperspectral movie to be
collapsed to a sequence of points in such a way that some of the key structure
within and between the slices is encoded by the points on the Grassmann
manifold. This motivates the search for topological features, associated with
the evolution of the frames of a hyperspectral movie, within the corresponding
points on the Grassmann manifold. The proposed mathematical model affords the
processing of large data sets while retaining valuable discriminatory
information. In this paper, we discuss how embedding our data in the Grassmann
manifold, together with topological data analysis, captures dynamical events
that occur as the chemical plume is released and evolves.
Minimum Description Length Principle in Supervised Learning with Application to Lasso
Authors: Masanori Kawakita, Jun'ichi Takeuchi
Date: 2016-07-11 00:00:00

The minimum description length (MDL) principle in supervised learning is
studied. One of the most important theories for the MDL principle is Barron and
Cover's theory (BC theory), which gives a mathematical justification of the MDL
principle. The original BC theory, however, can be applied to supervised
learning only approximately and limitedly. Though Barron et al. recently
succeeded in removing a similar approximation in case of unsupervised learning,
their idea cannot be essentially applied to supervised learning in general. To
overcome this issue, an extension of BC theory to supervised learning is
proposed. The derived risk bound has several advantages inherited from the
original BC theory. First, the risk bound holds for finite sample size. Second,
it requires remarkably few assumptions. Third, the risk bound has a form of
redundancy of the two-stage code for the MDL procedure. Hence, the proposed
extension gives a mathematical justification of the MDL principle to supervised
learning like the original BC theory. As an important example of application,
new risk and (probabilistic) regret bounds of lasso with random design are
derived. The derived risk bound holds for any finite sample size $n$ and
feature number $p$ even if $n\ll p$ without boundedness of features in contrast
to the past work. Behavior of the regret bound is investigated by numerical
simulations. We believe that this is the first extension of BC theory to
general supervised learning with random design without approximation.
sk_p: a neural program corrector for MOOCs
Authors: Yewen Pu, Karthik Narasimhan, Armando Solar-Lezama, Regina Barzilay
Date: 2016-07-11 00:00:00
Tasks: Machine Translation, Translation

We present a novel technique for automatic program correction in MOOCs,
capable of fixing both syntactic and semantic errors without manual, problem
specific correction strategies. Given an incorrect student program, it
generates candidate programs from a distribution of likely corrections, and
checks each candidate for correctness against a test suite.
  The key observation is that in MOOCs many programs share similar code
fragments, and the seq2seq neural network model, used in the natural-language
processing task of machine translation, can be modified and trained to recover
these fragments.
  Experiment shows our scheme can correct 29% of all incorrect submissions and
out-performs state of the art approach which requires manual, problem specific
correction strategies.
LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection
Authors: Pankaj Malhotra, Anusha Ramakrishnan, Gaurangi Anand, Lovekesh Vig, Puneet Agarwal, Gautam Shroff
Date: 2016-07-01 00:00:00
Tasks: Anomaly Detection, Decoder, Outlier Detection, Time Series, Time Series Analysis, Time Series Anomaly Detection, Time Series Classification

Mechanical devices such as engines, vehicles, aircrafts, etc., are typically
instrumented with numerous sensors to capture the behavior and health of the
machine. However, there are often external factors or variables which are not
captured by sensors leading to time-series which are inherently unpredictable.
For instance, manual controls and/or unmonitored environmental conditions or
load may lead to inherently unpredictable time-series. Detecting anomalies in
such scenarios becomes challenging using standard approaches based on
mathematical models that rely on stationarity, or prediction models that
utilize prediction errors to detect anomalies. We propose a Long Short Term
Memory Networks based Encoder-Decoder scheme for Anomaly Detection (EncDec-AD)
that learns to reconstruct 'normal' time-series behavior, and thereafter uses
reconstruction error to detect anomalies. We experiment with three publicly
available quasi predictable time-series datasets: power demand, space shuttle,
and ECG, and two real-world engine datasets with both predictive and
unpredictable behavior. We show that EncDec-AD is robust and can detect
anomalies from predictable, unpredictable, periodic, aperiodic, and
quasi-periodic time-series. Further, we show that EncDec-AD is able to detect
anomalies from short time-series (length as small as 30) as well as long
time-series (length as large as 500).
Incremental Factorization Machines for Persistently Cold-starting Online Item Recommendation
Authors: Takuya Kitazawa
Date: 2016-07-11 00:00:00
Tasks: Recommendation Systems

Real-world item recommenders commonly suffer from a persistent cold-start
problem which is caused by dynamically changing users and items. In order to
overcome the problem, several context-aware recommendation techniques have been
recently proposed. In terms of both feasibility and performance, factorization
machine (FM) is one of the most promising methods as generalization of the
conventional matrix factorization techniques. However, since online algorithms
are suitable for dynamic data, the static FMs are still inadequate. Thus, this
paper proposes incremental FMs (iFMs), a general online factorization
framework, and specially extends iFMs into an online item recommender. The
proposed framework can be a promising baseline for further development of the
production recommender systems. Evaluation is done empirically both on
synthetic and real-world unstable datasets.
Classifying Variable-Length Audio Files with All-Convolutional Networks and Masked Global Pooling
Authors: Lars Hertel, Huy Phan, Alfred Mertins
Date: 2016-07-11 00:00:00
Tasks: Acoustic Scene Classification, All, Audio Tagging, Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Scene Classification, Scene Recognition

We trained a deep all-convolutional neural network with masked global pooling
to perform single-label classification for acoustic scene classification and
multi-label classification for domestic audio tagging in the DCASE-2016
contest. Our network achieved an average accuracy of 84.5% on the four-fold
cross-validation for acoustic scene recognition, compared to the provided
baseline of 72.5%, and an average equal error rate of 0.17 for domestic audio
tagging, compared to the baseline of 0.21. The network therefore improves the
baselines by a relative amount of 17% and 19%, respectively. The network only
consists of convolutional layers to extract features from the short-time
Fourier transform and one global pooling layer to combine those features. It
particularly possesses neither fully-connected layers, besides the
fully-connected output layer, nor dropout layers.
Nesterov's Accelerated Gradient and Momentum as approximations to Regularised Update Descent
Authors: Aleksandar Botev, Guy Lever, David Barber
Date: 2016-07-07 00:00:00

We present a unifying framework for adapting the update direction in
gradient-based iterative optimization methods. As natural special cases we
re-derive classical momentum and Nesterov's accelerated gradient method,
lending a new intuitive interpretation to the latter algorithm. We show that a
new algorithm, which we term Regularised Gradient Descent, can converge more
quickly than either Nesterov's algorithm or the classical momentum algorithm.
Near-optimal Bayesian Active Learning with Correlated and Noisy Tests
Authors: Yuxin Chen, S. Hamed Hassani, Andreas Krause
Date: 2016-05-24 00:00:00
Tasks: Active Learning, Experimental Design

We consider the Bayesian active learning and experimental design problem,
where the goal is to learn the value of some unknown target variable through a
sequence of informative, noisy tests. In contrast to prior work, we focus on
the challenging, yet practically relevant setting where test outcomes can be
conditionally dependent given the hidden target variable. Under such
assumptions, common heuristics, such as greedily performing tests that maximize
the reduction in uncertainty of the target, often perform poorly. In this
paper, we propose ECED, a novel, computationally efficient active learning
algorithm, and prove strong theoretical guarantees that hold with correlated,
noisy tests. Rather than directly optimizing the prediction error, at each
step, ECED picks the test that maximizes the gain in a surrogate objective,
which takes into account the dependencies between tests. Our analysis relies on
an information-theoretic auxiliary function to track the progress of ECED, and
utilizes adaptive submodularity to attain the near-optimal bound. We
demonstrate strong empirical performance of ECED on two problem instances,
including a Bayesian experimental design task intended to distinguish among
economic theories of how people make risky decisions, and an active preference
learning task via pairwise comparisons.
Collaborative Learning of Stochastic Bandits over a Social Network
Authors: Ravi Kumar Kolla, Krishna Jagannathan, Aditya Gopalan
Date: 2016-02-29 00:00:00

We consider a collaborative online learning paradigm, wherein a group of
agents connected through a social network are engaged in playing a stochastic
multi-armed bandit game. Each time an agent takes an action, the corresponding
reward is instantaneously observed by the agent, as well as its neighbours in
the social network. We perform a regret analysis of various policies in this
collaborative learning setting. A key finding of this paper is that natural
extensions of widely-studied single agent learning policies to the network
setting need not perform well in terms of regret. In particular, we identify a
class of non-altruistic and individually consistent policies, and argue by
deriving regret lower bounds that they are liable to suffer a large regret in
the networked setting. We also show that the learning performance can be
substantially improved if the agents exploit the structure of the network, and
develop a simple learning algorithm based on dominating sets of the network.
Specifically, we first consider a star network, which is a common motif in
hierarchical social networks, and show analytically that the hub agent can be
used as an information sink to expedite learning and improve the overall
regret. We also derive networkwide regret bounds for the algorithm applied to
general networks. We conduct numerical experiments on a variety of networks to
corroborate our analytical results.
Hypergraph Modelling for Geometric Model Fitting
Authors: Guobao Xiao, Hanzi Wang, Taotao Lai, David Suter
Date: 2016-07-11 00:00:00
Tasks: model

In this paper, we propose a novel hypergraph based method (called HF) to fit
and segment multi-structural data. The proposed HF formulates the geometric
model fitting problem as a hypergraph partition problem based on a novel
hypergraph model. In the hypergraph model, vertices represent data points and
hyperedges denote model hypotheses. The hypergraph, with large and
"data-determined" degrees of hyperedges, can express the complex relationships
between model hypotheses and data points. In addition, we develop a robust
hypergraph partition algorithm to detect sub-hypergraphs for model fitting. HF
can effectively and efficiently estimate the number of, and the parameters of,
model instances in multi-structural data heavily corrupted with outliers
simultaneously. Experimental results show the advantages of the proposed method
over previous methods on both synthetic data and real images.