pwc-archive/papers-with-abstracts huggingface dataset:
Worst-case vs Average-case Design for Estimation from Fixed Pairwise Comparisons
Authors: Ashwin Pananjady, Cheng Mao, Vidya Muthukumar, Martin J. Wainwright, Thomas A. Courtade
Date: 2017-07-19 00:00:00

Pairwise comparison data arises in many domains, including tournament
rankings, web search, and preference elicitation. Given noisy comparisons of a
fixed subset of pairs of items, we study the problem of estimating the
underlying comparison probabilities under the assumption of strong stochastic
transitivity (SST). We also consider the noisy sorting subclass of the SST
model. We show that when the assignment of items to the topology is arbitrary,
these permutation-based models, unlike their parametric counterparts, do not
admit consistent estimation for most comparison topologies used in practice. We
then demonstrate that consistent estimation is possible when the assignment of
items to the topology is randomized, thus establishing a dichotomy between
worst-case and average-case designs. We propose two estimators in the
average-case setting and analyze their risk, showing that it depends on the
comparison topology only through the degree sequence of the topology. The rates
achieved by these estimators are shown to be optimal for a large class of
graphs. Our results are corroborated by simulations on multiple comparison
topologies.
Crowdsourcing Multiple Choice Science Questions
Authors: Johannes Welbl, Nelson F. Liu, Matt Gardner
Date: 2017-07-19 00:00:00
Proceeding: WS 2017 9
Tasks: Diversity, Multiple-choice, Question Generation, Question-Generation

We present a novel method for obtaining high-quality, domain-targeted
multiple choice questions from crowd workers. Generating these questions can be
difficult without trading away originality, relevance or diversity in the
answer options. Our method addresses these problems by leveraging a large
corpus of domain-specific text and a small set of existing questions. It
produces model suggestions for document selection and answer distractor choice
which aid the human question generation process. With this method we have
assembled SciQ, a dataset of 13.7K multiple choice science exam questions
(Dataset available at http://allenai.org/data.html). We demonstrate that the
method produces in-domain questions by providing an analysis of this new
dataset and by showing that humans cannot distinguish the crowdsourced
questions from original questions. When using SciQ as additional training data
to existing questions, we observe accuracy improvements on real science exams.
Measuring Offensive Speech in Online Political Discourse
Authors: Rishab Nithyanand, Brian Schaffner, Phillipa Gill
Date: 2017-06-06 00:00:00

The Internet and online forums such as Reddit have become an increasingly
popular medium for citizens to engage in political conversations. However, the
online disinhibition effect resulting from the ability to use pseudonymous
identities may manifest in the form of offensive speech, consequently making
political discussions more aggressive and polarizing than they already are.
Such environments may result in harassment and self-censorship from its
targets. In this paper, we present preliminary results from a large-scale
temporal measurement aimed at quantifying offensiveness in online political
discussions.
  To enable our measurements, we develop and evaluate an offensive speech
classifier. We then use this classifier to quantify and compare offensiveness
in the political and general contexts. We perform our study using a database of
over 168M Reddit comments made by over 7M pseudonyms between January 2015 and
January 2017 -- a period covering several divisive political events including
the 2016 US presidential elections.
Can GAN Learn Topological Features of a Graph?
Authors: Weiyi Liu, Pin-Yu Chen, Hal Cooper, Min Hwan Oh, Sailung Yeung, Toyotaro Suzumura
Date: 2017-07-19 00:00:00
Tasks: Graph Reconstruction

This paper is first-line research expanding GANs into graph topology
analysis. By leveraging the hierarchical connectivity structure of a graph, we
have demonstrated that generative adversarial networks (GANs) can successfully
capture topological features of any arbitrary graph, and rank edge sets by
different stages according to their contribution to topology reconstruction.
Moreover, in addition to acting as an indicator of graph reconstruction, we
find that these stages can also preserve important topological features in a
graph.
Fast and Accurate OOV Decoder on High-Level Features
Authors: Yuri Khokhlov, Natalia Tomashenko, Ivan Medennikov, Alexei Romanenko
Date: 2017-07-19 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, speech-recognition, Speech Recognition, Vocal Bursts Intensity Prediction

This work proposes a novel approach to out-of-vocabulary (OOV) keyword search
(KWS) task. The proposed approach is based on using high-level features from an
automatic speech recognition (ASR) system, so called phoneme posterior based
(PPB) features, for decoding. These features are obtained by calculating
time-dependent phoneme posterior probabilities from word lattices, followed by
their smoothing. For the PPB features we developed a special novel very fast,
simple and efficient OOV decoder. Experimental results are presented on the
Georgian language from the IARPA Babel Program, which was the test language in
the OpenKWS 2016 evaluation campaign. The results show that in terms of maximum
term weighted value (MTWV) metric and computational speed, for single ASR
systems, the proposed approach significantly outperforms the state-of-the-art
approach based on using in-vocabulary proxies for OOV keywords in the indexed
database. The comparison of the two OOV KWS approaches on the fusion results of
the nine different ASR systems demonstrates that the proposed OOV decoder
outperforms the proxy-based approach in terms of MTWV metric given the
comparable processing speed. Other important advantages of the OOV decoder
include extremely low memory consumption and simplicity of its implementation
and parameter optimization.
Entropy-based Pruning for Learning Bayesian Networks using BIC
Authors: Cassio P. de Campos, Mauro Scanagatta, Giorgio Corani, Marco Zaffalon
Date: 2017-07-19 00:00:00

For decomposable score-based structure learning of Bayesian networks,
existing approaches first compute a collection of candidate parent sets for
each variable and then optimize over this collection by choosing one parent set
for each variable without creating directed cycles while maximizing the total
score. We target the task of constructing the collection of candidate parent
sets when the score of choice is the Bayesian Information Criterion (BIC). We
provide new non-trivial results that can be used to prune the search space of
candidate parent sets of each node. We analyze how these new results relate to
previous ideas in the literature both theoretically and empirically. We show in
experiments with UCI data sets that gains can be significant. Since the new
pruning rules are easy to implement and have low computational costs, they can
be promptly integrated into all state-of-the-art methods for structure learning
of Bayesian networks.
Capturing the diversity of biological tuning curves using generative adversarial networks
Authors: Takafumi Arakaki, G. Barello, Yashar Ahmadian
Date: 2017-07-14 00:00:00
Tasks: BIG-bench Machine Learning, Diversity

Tuning curves characterizing the response selectivities of biological neurons
often exhibit large degrees of irregularity and diversity across neurons.
Theoretical network models that feature heterogeneous cell populations or
random connectivity also give rise to diverse tuning curves. However, a general
framework for fitting such models to experimentally measured tuning curves is
lacking. We address this problem by proposing to view mechanistic network
models as generative models whose parameters can be optimized to fit the
distribution of experimentally measured tuning curves. A major obstacle for
fitting such models is that their likelihood function is not explicitly
available or is highly intractable to compute. Recent advances in machine
learning provide ways for fitting generative models without the need to
evaluate the likelihood and its gradient. Generative Adversarial Networks (GAN)
provide one such framework which has been successful in traditional machine
learning tasks. We apply this approach in two separate experiments, showing how
GANs can be used to fit commonly used mechanistic models in theoretical
neuroscience to datasets of measured tuning curves. This fitting procedure
avoids the computationally expensive step of inferring latent variables, e.g.
the biophysical parameters of individual cells or the particular realization of
the full synaptic connectivity matrix, and directly learns model parameters
which characterize the statistics of connectivity or of single-cell properties.
Another strength of this approach is that it fits the entire, joint
distribution of experimental tuning curves, instead of matching a few summary
statistics picked a priori by the user. More generally, this framework opens
the door to fitting theoretically motivated dynamical network models directly
to simultaneously or non-simultaneously recorded neural responses.
From Bach to the Beatles: The simulation of human tonal expectation using ecologically-trained predictive models
Authors: Carlos Cancino-Chacón, Maarten Grachten, Kat Agres
Date: 2017-07-19 00:00:00

Tonal structure is in part conveyed by statistical regularities between
musical events, and research has shown that computational models reflect tonal
structure in music by capturing these regularities in schematic constructs like
pitch histograms. Of the few studies that model the acquisition of perceptual
learning from musical data, most have employed self-organizing models that
learn a topology of static descriptions of musical contexts. Also, the stimuli
used to train these models are often symbolic rather than acoustically faithful
representations of musical material. In this work we investigate whether
sequential predictive models of musical memory (specifically, recurrent neural
networks), trained on audio from commercial CD recordings, induce tonal
knowledge in a similar manner to listeners (as shown in behavioral studies in
music perception). Our experiments indicate that various types of recurrent
neural networks produce musical expectations that clearly convey tonal
structure. Furthermore, the results imply that although implicit knowledge of
tonal structure is a necessary condition for accurate musical expectation, the
most accurate predictive models also use other cues beyond the tonal structure
of the musical context.
Cost-complexity pruning of random forests
Authors: Kiran Bangalore Ravi, Jean Serra
Date: 2017-03-15 00:00:00

Random forests perform bootstrap-aggregation by sampling the training samples
with replacement. This enables the evaluation of out-of-bag error which serves
as a internal cross-validation mechanism. Our motivation lies in using the
unsampled training samples to improve each decision tree in the ensemble. We
study the effect of using the out-of-bag samples to improve the generalization
error first of the decision trees and second the random forest by post-pruning.
A preliminary empirical study on four UCI repository datasets show consistent
decrease in the size of the forests without considerable loss in accuracy.
Sparsity by Worst-Case Penalties
Authors: Yves Grandvalet, Julien Chiquet, Christophe Ambroise
Date: 2012-10-07 00:00:00

This paper proposes a new interpretation of sparse penalties such as the
elastic-net and the group-lasso. Beyond providing a new viewpoint on these
penalization schemes, our approach results in a unified optimization strategy.
Our experiments demonstrate that this strategy, implemented on the elastic-net,
is computationally extremely efficient for small to medium size problems. Our
accompanying software solves problems very accurately, at machine precision, in
the time required to get a rough estimate with competing state-of-the-art
algorithms. We illustrate on real and artificial datasets that this accuracy is
required to for the correctness of the support of the solution, which is an
important element for the interpretability of sparsity-inducing penalties.
Simultaneously Solving Mixed Model Assembly Line Balancing and Sequencing problems with FSS Algorithm
Authors: Joao Batista Monteiro Filho, Isabela Maria Carneiro de Albuquerque, Fernando Buarque de Lima Neto
Date: 2017-07-19 00:00:00
Tasks: Decision Making

Many assembly lines related optimization problems have been tackled by
researchers in the last decades due to its relevance for the decision makers
within manufacturing industry. Many of theses problems, more specifically
Assembly Lines Balancing and Sequencing problems, are known to be NP-Hard.
Therefore, Computational Intelligence solution approaches have been conceived
in order to provide practical use decision making tools. In this work, we
proposed a simultaneous solution approach in order to tackle both Balancing and
Sequencing problems utilizing an effective meta-heuristic algorithm referred as
Fish School Search. Three different test instances were solved with the
original and two modified versions of this algorithm and the results were
compared with Particle Swarm Optimization Algorithm.
Adaptive ADMM with Spectral Penalty Parameter Selection
Authors: Zheng Xu, Mario A. T. Figueiredo, Tom Goldstein
Date: 2016-05-24 00:00:00

The alternating direction method of multipliers (ADMM) is a versatile tool
for solving a wide range of constrained optimization problems, with
differentiable or non-differentiable objective functions. Unfortunately, its
performance is highly sensitive to a penalty parameter, which makes ADMM often
unreliable and hard to automate for a non-expert user. We tackle this weakness
of ADMM by proposing a method to adaptively tune the penalty parameters to
achieve fast convergence. The resulting adaptive ADMM (AADMM) algorithm,
inspired by the successful Barzilai-Borwein spectral method for gradient
descent, yields fast convergence and relative insensitivity to the initial
stepsize and problem scaling.
Domain-adversarial neural networks to address the appearance variability of histopathology images
Authors: Maxime W. Lafarge, Josien P. W. Pluim, Koen A. J. Eppenhof, Pim Moeskops, Mitko Veta
Date: 2017-07-19 00:00:00
Tasks: Mitosis Detection

Preparing and scanning histopathology slides consists of several steps, each
with a multitude of parameters. The parameters can vary between pathology labs
and within the same lab over time, resulting in significant variability of the
tissue appearance that hampers the generalization of automatic image analysis
methods. Typically, this is addressed with ad-hoc approaches such as staining
normalization that aim to reduce the appearance variability. In this paper, we
propose a systematic solution based on domain-adversarial neural networks. We
hypothesize that removing the domain information from the model representation
leads to better generalization. We tested our hypothesis for the problem of
mitosis detection in breast cancer histopathology images and made a comparative
analysis with two other approaches. We show that combining color augmentation
with domain-adversarial training is a better alternative than standard
approaches to improve the generalization of deep learning methods.
Object-Extent Pooling for Weakly Supervised Single-Shot Localization
Authors: Amogh Gudi, Nicolai van Rosmalen, Marco Loog, Jan van Gemert
Date: 2017-07-19 00:00:00
Tasks: Object, Object Localization, Region Proposal, Weakly-Supervised Object Localization

In the face of scarcity in detailed training annotations, the ability to
perform object localization tasks in real-time with weak-supervision is very
valuable. However, the computational cost of generating and evaluating region
proposals is heavy. We adapt the concept of Class Activation Maps (CAM) into
the very first weakly-supervised 'single-shot' detector that does not require
the use of region proposals. To facilitate this, we propose a novel global
pooling technique called Spatial Pyramid Averaged Max (SPAM) pooling for
training this CAM-based network for object extent localisation with only weak
image-level supervision. We show this global pooling layer possesses a near
ideal flow of gradients for extent localization, that offers a good trade-off
between the extremes of max and average pooling. Our approach only requires a
single network pass and uses a fast-backprojection technique, completely
omitting any region proposal steps. To the best of our knowledge, this is the
first approach to do so. Due to this, we are able to perform inference in
real-time at 35fps, which is an order of magnitude faster than all previous
weakly supervised object localization frameworks.
Deformable Part-based Fully Convolutional Network for Object Detection
Authors: Taylor Mordan, Nicolas Thome, Matthieu Cord, Gilles Henaff
Date: 2017-07-19 00:00:00
Tasks: Object, object-detection, Object Detection

Existing region-based object detectors are limited to regions with fixed box
geometry to represent objects, even if those are highly non-rectangular. In
this paper we introduce DP-FCN, a deep model for object detection which
explicitly adapts to shapes of objects with deformable parts. Without
additional annotations, it learns to focus on discriminative elements and to
align them, and simultaneously brings more invariance for classification and
geometric information to refine localization. DP-FCN is composed of three main
modules: a Fully Convolutional Network to efficiently maintain spatial
resolution, a deformable part-based RoI pooling layer to optimize positions of
parts and build invariance, and a deformation-aware localization module
explicitly exploiting displacements of parts to improve accuracy of bounding
box regression. We experimentally validate our model and show significant
gains. DP-FCN achieves state-of-the-art performances of 83.1% and 80.9% on
PASCAL VOC 2007 and 2012 with VOC data only.
Learning model-based planning from scratch
Authors: Razvan Pascanu, Yujia Li, Oriol Vinyals, Nicolas Heess, Lars Buesing, Sebastien Racanière, David Reichert, Théophane Weber, Daan Wierstra, Peter Battaglia
Date: 2017-07-19 00:00:00
Tasks: continuous-control, Continuous Control, Decision Making, model, Sequential Decision Making

Conventional wisdom holds that model-based planning is a powerful approach to
sequential decision-making. It is often very challenging in practice, however,
because while a model can be used to evaluate a plan, it does not prescribe how
to construct a plan. Here we introduce the "Imagination-based Planner", the
first model-based, sequential decision-making agent that can learn to
construct, evaluate, and execute plans. Before any action, it can perform a
variable number of imagination steps, which involve proposing an imagined
action and evaluating it with its model-based imagination. All imagined actions
and outcomes are aggregated, iteratively, into a "plan context" which
conditions future real and imagined actions. The agent can even decide how to
imagine: testing out alternative imagined actions, chaining sequences of
actions together, or building a more complex "imagination tree" by navigating
flexibly among the previously imagined states using a learned policy. And our
agent can learn to plan economically, jointly optimizing for external rewards
and computational costs associated with using its imagination. We show that our
architecture can learn to solve a challenging continuous control problem, and
also learn elaborate planning strategies in a discrete maze-solving task. Our
work opens a new direction toward learning the components of a model-based
planning system and how to use them.
Fish School Search Algorithm for Constrained Optimization
Authors: Joao Batista Monteiro Filho, Isabela Maria Carneiro de Albuquerque, Fernando Buarque de Lima Neto
Date: 2017-07-19 00:00:00

In this work we investigate the effectiveness of the application of niching
able swarm metaheuristic approaches in order to solve constrained optimization
problems. Sub-swarms are used in order to allow the achievement of many
feasible regions to be exploited in terms of fitness function. The niching
approach employed was wFSS, a version of the Fish School Search algorithm
devised specifically to deal with multi-modal search spaces. A base technique
referred as wrFSS was conceived and three variations applying different
constraint handling procedures were also proposed. Tests were performed in
seven problems from CEC 2010 and a comparison with other approaches was carried
out. Results show that the search strategy proposed is able to handle some
heavily constrained problems and achieve results comparable to the
state-of-the-art algorithms. However, we also observed that the local search
operator present in wFSS and inherited by wrFSS makes the fitness convergence
difficult when the feasible region presents some specific geometrical features.
Sentence-level quality estimation by predicting HTER as a multi-component metric
Authors: Eleftherios Avramidis
Date: 2017-07-19 00:00:00
Proceeding: WS 2017 9
Tasks: BIG-bench Machine Learning, Sentence

This submission investigates alternative machine learning models for
predicting the HTER score on the sentence level. Instead of directly predicting
the HTER score, we suggest a model that jointly predicts the amount of the 4
distinct post-editing operations, which are then used to calculate the HTER
score. This also gives the possibility to correct invalid (e.g. negative)
predicted values prior to the calculation of the HTER score. Without any
feature exploration, a multi-layer perceptron with 4 outputs yields small but
significant improvements over the baseline.
Metrical-accent Aware Vocal Onset Detection in Polyphonic Audio
Authors: Georgi Dzhambazov, Andre Holzapfel, Ajay Srinivasamurthy, Xavier Serra
Date: 2017-07-19 00:00:00
Tasks: Onset Detection, Position

The goal of this study is the automatic detection of onsets of the singing
voice in polyphonic audio recordings. Starting with a hypothesis that the
knowledge of the current position in a metrical cycle (i.e. metrical accent)
can improve the accuracy of vocal note onset detection, we propose a novel
probabilistic model to jointly track beats and vocal note onsets. The proposed
model extends a state of the art model for beat and meter tracking, in which
a-priori probability of a note at a specific metrical accent interacts with the
probability of observing a vocal note onset. We carry out an evaluation on a
varied collection of multi-instrument datasets from two music traditions
(English popular music and Turkish makam) with different types of metrical
cycles and singing styles. Results confirm that the proposed model reasonably
improves vocal note onset detection accuracy compared to a baseline model that
does not take metrical position into account.
The Role of Context Types and Dimensionality in Learning Word Embeddings
Authors: Oren Melamud, David McClosky, Siddharth Patwardhan, Mohit Bansal
Date: 2016-01-05 00:00:00
Proceeding: NAACL 2016 6
Tasks: Learning Word Embeddings, Word Embeddings

We provide the first extensive evaluation of how using different types of
context to learn skip-gram word embeddings affects performance on a wide range
of intrinsic and extrinsic NLP tasks. Our results suggest that while intrinsic
tasks tend to exhibit a clear preference to particular types of contexts and
higher dimensionality, more careful tuning is required for finding the optimal
settings for most of the extrinsic tasks that we considered. Furthermore, for
these extrinsic tasks, we find that once the benefit from increasing the
embedding dimensionality is mostly exhausted, simple concatenation of word
embeddings, learned with different context types, can yield further performance
gains. As an additional contribution, we propose a new variant of the skip-gram
model that learns word embeddings from weighted contexts of substitute words.