pwc-archive/papers-with-abstracts huggingface dataset:
Together We Know How to Achieve: An Epistemic Logic of Know-How
Authors: Pavel Naumov, Jia Tao
Date: 2017-05-25 00:00:00

The existence of a coalition strategy to achieve a goal does not necessarily
mean that the coalition has enough information to know how to follow the
strategy. Neither does it mean that the coalition knows that such a strategy
exists. The article studies an interplay between the distributed knowledge,
coalition strategies, and coalition "know-how" strategies. The main technical
result is a sound and complete trimodal logical system that describes the
properties of this interplay.
Distance weighted discrimination of face images for gender classification
Authors: Mónica Benito, Eduardo García-Portugués, J. S. Marron, Daniel Peña
Date: 2017-06-15 00:00:00
Tasks: Classification, Gender Classification, General Classification

We illustrate the advantages of distance weighted discrimination for classification and feature extraction in a High Dimension Low Sample Size (HDLSS) situation. The HDLSS context is a gender classification problem of face images in which the dimension of the data is several orders of magnitude larger than the sample size. We compare distance weighted discrimination with Fisher's linear discriminant, support vector machines, and principal component analysis by exploring their classification interpretation through insightful visuanimations and by examining the classifiers' discriminant errors. This analysis enables us to make new contributions to the understanding of the drivers of human discrimination between males and females.
German in Flux: Detecting Metaphoric Change via Word Entropy
Authors: Dominik Schlechtweg, Stefanie Eckmann, Enrico Santus, Sabine Schulte im Walde, Daniel Hole
Date: 2017-06-15 00:00:00
Proceeding: CONLL 2017 8

This paper explores the information-theoretic measure entropy to detect
metaphoric change, transferring ideas from hypernym detection to research on
language change. We also build the first diachronic test set for German as a
standard for metaphoric change annotation. Our model shows high performance, is
unsupervised, language-independent and generalizable to other processes of
semantic change.
Deep adversarial neural decoding
Authors: Yağmur Güçlütürk, Umut Güçlü, Katja Seeliger, Sander Bosch, Rob Van Lier, Marcel van Gerven
Date: 2017-05-19 00:00:00

Here, we present a novel approach to solve the problem of reconstructing
perceived stimuli from brain responses by combining probabilistic inference
with deep learning. Our approach first inverts the linear transformation from
latent features to brain responses with maximum a posteriori estimation and
then inverts the nonlinear transformation from perceived stimuli to latent
features with adversarial training of convolutional neural networks. We test
our approach with a functional magnetic resonance imaging experiment and show
that it can generate state-of-the-art reconstructions of perceived faces from
brain activations.
A Simple and Accurate Syntax-Agnostic Neural Model for Dependency-based Semantic Role Labeling
Authors: Diego Marcheggiani, Anton Frolov, Ivan Titov
Date: 2017-01-10 00:00:00
Proceeding: CONLL 2017 8
Tasks: Semantic Role Labeling

We introduce a simple and accurate neural model for dependency-based semantic
role labeling. Our model predicts predicate-argument dependencies relying on
states of a bidirectional LSTM encoder. The semantic role labeler achieves
competitive performance on English, even without any kind of syntactic
information and only using local inference. However, when automatically
predicted part-of-speech tags are provided as input, it substantially
outperforms all previous local models and approaches the best reported results
on the English CoNLL-2009 dataset. We also consider Chinese, Czech and Spanish
where our approach also achieves competitive results. Syntactic parsers are
unreliable on out-of-domain data, so standard (i.e., syntactically-informed)
SRL models are hindered when tested in this setting. Our syntax-agnostic model
appears more robust, resulting in the best reported results on standard
out-of-domain test sets.
DOTE: Dual cOnvolutional filTer lEarning for Super-Resolution and Cross-Modality Synthesis in MRI
Authors: Yawen Huang, Ling Shao, Alejandro F. Frangi
Date: 2017-06-15 00:00:00
Tasks: Image Generation, Image Super-Resolution, Super-Resolution

Cross-modal image synthesis is a topical problem in medical image computing.
Existing methods for image synthesis are either tailored to a specific
application, require large scale training sets, or are based on partitioning
images into overlapping patches. In this paper, we propose a novel Dual
cOnvolutional filTer lEarning (DOTE) approach to overcome the drawbacks of
these approaches. We construct a closed loop joint filter learning strategy
that generates informative feedback for model self-optimization. Our method can
leverage data more efficiently thus reducing the size of the required training
set. We extensively evaluate DOTE in two challenging tasks: image
super-resolution and cross-modality synthesis. The experimental results
demonstrate superior performance of our method over other state-of-the-art
methods.
Deep Clustering and Conventional Networks for Music Separation: Stronger Together
Authors: Yi Luo, Zhuo Chen, John R. Hershey, Jonathan Le Roux, Nima Mesgarani
Date: 2016-11-18 00:00:00
Tasks: Clustering, Deep Clustering, Multi-Task Learning, Music Source Separation, Speech Separation

Deep clustering is the first method to handle general audio separation
scenarios with multiple sources of the same type and an arbitrary number of
sources, performing impressively in speaker-independent speech separation
tasks. However, little is known about its effectiveness in other challenging
situations such as music source separation. Contrary to conventional networks
that directly estimate the source signals, deep clustering generates an
embedding for each time-frequency bin, and separates sources by clustering the
bins in the embedding space. We show that deep clustering outperforms
conventional networks on a singing voice separation task, in both matched and
mismatched conditions, even though conventional networks have the advantage of
end-to-end training for best signal approximation, presumably because its more
flexible objective engenders better regularization. Since the strengths of deep
clustering and conventional network architectures appear complementary, we
explore combining them in a single hybrid network trained via an approach akin
to multi-task learning. Remarkably, the combination significantly outperforms
either of its components.
Okutama-Action: An Aerial View Video Dataset for Concurrent Human Action Detection
Authors: Mohammadamin Barekatain, Miquel Martí, Hsueh-Fu Shih, Samuel Murray, Kotaro Nakayama, Yutaka Matsuo, Helmut Prendinger
Date: 2017-06-09 00:00:00
Tasks: Action Detection

Despite significant progress in the development of human action detection
datasets and algorithms, no current dataset is representative of real-world
aerial view scenarios. We present Okutama-Action, a new video dataset for
aerial view concurrent human action detection. It consists of 43 minute-long
fully-annotated sequences with 12 action classes. Okutama-Action features many
challenges missing in current datasets, including dynamic transition of
actions, significant changes in scale and aspect ratio, abrupt camera movement,
as well as multi-labeled actors. As a result, our dataset is more challenging
than existing ones, and will help push the field forward to enable real-world
applications.
Multi-objective Bandits: Optimizing the Generalized Gini Index
Authors: Robert Busa-Fekete, Balazs Szorenyi, Paul Weng, Shie Mannor
Date: 2017-06-15 00:00:00
Proceeding: ICML 2017 8

We study the multi-armed bandit (MAB) problem where the agent receives a
vectorial feedback that encodes many possibly competing objectives to be
optimized. The goal of the agent is to find a policy, which can optimize these
objectives simultaneously in a fair way. This multi-objective online
optimization problem is formalized by using the Generalized Gini Index (GGI)
aggregation function. We propose an online gradient descent algorithm which
exploits the convexity of the GGI aggregation function, and controls the
exploration in a careful way achieving a distribution-free regret
$\tilde{\bigO} (T^{-1/2} )$ with high probability. We test our algorithm on
synthetic data as well as on an electric battery control problem where the goal
is to trade off the use of the different cells of a battery in order to balance
their respective degradation rates.
Just Sort It! A Simple and Effective Approach to Active Preference Learning
Authors: Lucas Maystre, Matthias Grossglauser
Date: 2015-02-19 00:00:00
Proceeding: ICML 2017 8
Tasks: Active Learning

We address the problem of learning a ranking by using adaptively chosen
pairwise comparisons. Our goal is to recover the ranking accurately but to
sample the comparisons sparingly. If all comparison outcomes are consistent
with the ranking, the optimal solution is to use an efficient sorting
algorithm, such as Quicksort. But how do sorting algorithms behave if some
comparison outcomes are inconsistent with the ranking? We give favorable
guarantees for Quicksort for the popular Bradley-Terry model, under natural
assumptions on the parameters. Furthermore, we empirically demonstrate that
sorting algorithms lead to a very simple and effective active learning
strategy: repeatedly sort the items. This strategy performs as well as
state-of-the-art methods (and much better than random sampling) at a minuscule
fraction of the computational cost.
Neural Domain Adaptation for Biomedical Question Answering
Authors: Georg Wiese, Dirk Weissenborn, Mariana Neves
Date: 2017-06-12 00:00:00
Proceeding: CONLL 2017 8
Tasks: Articles, Domain Adaptation, Question Answering, Transfer Learning, Word Embeddings

Factoid question answering (QA) has recently benefited from the development
of deep learning (DL) systems. Neural network models outperform traditional
approaches in domains where large datasets exist, such as SQuAD (ca. 100,000
questions) for Wikipedia articles. However, these systems have not yet been
applied to QA in more specific domains, such as biomedicine, because datasets
are generally too small to train a DL system from scratch. For example, the
BioASQ dataset for biomedical QA comprises less then 900 factoid (single
answer) and list (multiple answers) QA instances. In this work, we adapt a
neural QA system trained on a large open-domain dataset (SQuAD, source) to a
biomedical dataset (BioASQ, target) by employing various transfer learning
techniques. Our network architecture is based on a state-of-the-art QA system,
extended with biomedical word embeddings and a novel mechanism to answer list
questions. In contrast to existing biomedical QA systems, our system does not
rely on domain-specific ontologies, parsers or entity taggers, which are
expensive to create. Despite this fact, our systems achieve state-of-the-art
results on factoid questions and competitive results on list questions.
Robust Submodular Maximization: A Non-Uniform Partitioning Approach
Authors: Ilija Bogunovic, Slobodan Mitrović, Jonathan Scarlett, Volkan Cevher
Date: 2017-06-15 00:00:00
Proceeding: ICML 2017 8
Tasks: Data Summarization

We study the problem of maximizing a monotone submodular function subject to
a cardinality constraint $k$, with the added twist that a number of items
$\tau$ from the returned set may be removed. We focus on the worst-case setting
considered in (Orlin et al., 2016), in which a constant-factor approximation
guarantee was given for $\tau = o(\sqrt{k})$. In this paper, we solve a key
open problem raised therein, presenting a new Partitioned Robust (PRo)
submodular maximization algorithm that achieves the same guarantee for more
general $\tau = o(k)$. Our algorithm constructs partitions consisting of
buckets with exponentially increasing sizes, and applies standard submodular
optimization subroutines on the buckets in order to construct the robust
solution. We numerically demonstrate the performance of PRo in data
summarization and influence maximization, demonstrating gains over both the
greedy algorithm and the algorithm of (Orlin et al., 2016).
ChoiceRank: Identifying Preferences from Node Traffic in Networks
Authors: Lucas Maystre, Matthias Grossglauser
Date: 2016-10-20 00:00:00
Proceeding: ICML 2017 8
Tasks: Navigate

Understanding how users navigate in a network is of high interest in many
applications. We consider a setting where only aggregate node-level traffic is
observed and tackle the task of learning edge transition probabilities. We cast
it as a preference learning problem, and we study a model where choices follow
Luce's axiom. In this case, the $O(n)$ marginal counts of node visits are a
sufficient statistic for the $O(n^2)$ transition probabilities. We show how to
make the inference problem well-posed regardless of the network's structure,
and we present ChoiceRank, an iterative algorithm that scales to networks that
contains billions of nodes and edges. We apply the model to two clickstream
datasets and show that it successfully recovers the transition probabilities
using only the network structure and marginal (node-level) traffic data.
Finally, we also consider an application to mobility networks and apply the
model to one year of rides on New York City's bicycle-sharing system.
Second-Order Kernel Online Convex Optimization with Adaptive Sketching
Authors: Daniele Calandriello, Alessandro Lazaric, Michal Valko
Date: 2017-06-15 00:00:00
Proceeding: ICML 2017 8
Tasks: Second-order methods

Kernel online convex optimization (KOCO) is a framework combining the
expressiveness of non-parametric kernel models with the regret guarantees of
online learning. First-order KOCO methods such as functional gradient descent
require only $\mathcal{O}(t)$ time and space per iteration, and, when the only
information on the losses is their convexity, achieve a minimax optimal
$\mathcal{O}(\sqrt{T})$ regret. Nonetheless, many common losses in kernel
problems, such as squared loss, logistic loss, and squared hinge loss posses
stronger curvature that can be exploited. In this case, second-order KOCO
methods achieve $\mathcal{O}(\log(\text{Det}(\boldsymbol{K})))$ regret, which
we show scales as $\mathcal{O}(d_{\text{eff}}\log T)$, where $d_{\text{eff}}$
is the effective dimension of the problem and is usually much smaller than
$\mathcal{O}(\sqrt{T})$. The main drawback of second-order methods is their
much higher $\mathcal{O}(t^2)$ space and time complexity. In this paper, we
introduce kernel online Newton step (KONS), a new second-order KOCO method that
also achieves $\mathcal{O}(d_{\text{eff}}\log T)$ regret. To address the
computational complexity of second-order methods, we introduce a new matrix
sketching algorithm for the kernel matrix $\boldsymbol{K}_t$, and show that for
a chosen parameter $\gamma \leq 1$ our Sketched-KONS reduces the space and time
complexity by a factor of $\gamma^2$ to $\mathcal{O}(t^2\gamma^2)$ space and
time per iteration, while incurring only $1/\gamma$ times more regret.
Bayesian Additive Adaptive Basis Tensor Product Models for Modeling High Dimensional Surfaces: An application to high-throughput toxicity testing
Authors: Matthew W. Wheeler
Date: 2017-02-15 00:00:00
Tasks: Gaussian Processes, Vocal Bursts Intensity Prediction

Many modern data sets are sampled with error from complex high-dimensional
surfaces. Methods such as tensor product splines or Gaussian processes are
effective/well suited for characterizing a surface in two or three dimensions
but may suffer from difficulties when representing higher dimensional surfaces.
Motivated by high throughput toxicity testing where observed dose-response
curves are cross sections of a surface defined by a chemical's structural
properties, a model is developed to characterize this surface to predict
untested chemicals' dose-responses. This manuscript proposes a novel approach
that models the multidimensional surface as a sum of learned basis functions
formed as the tensor product of lower dimensional functions, which are
themselves representable by a basis expansion learned from the data. The model
is described, a Gibbs sampling algorithm proposed, and is investigated in a
simulation study as well as data taken from the US EPA's ToxCast high
throughput toxicity testing platform.
Towards a theory of word order. Comment on "Dependency distance: a new perspective on syntactic patterns in natural language" by Haitao Liu et al
Authors: Ramon Ferrer-i-Cancho
Date: 2017-06-15 00:00:00

Comment on "Dependency distance: a new perspective on syntactic patterns in
natural language" by Haitao Liu et al
Arabian Horse Identification Benchmark Dataset
Authors: Ayat Taha, Ashraf Darwish, Aboul Ella Hassanien
Date: 2017-06-15 00:00:00

The lack of a standard muzzle print database is a challenge for conducting
researches in Arabian horse identification systems. Therefore, collecting a
muzzle print images database is a crucial decision. The dataset presented in
this paper is an option for the studies that need a dataset for testing and
comparing the algorithms under development for Arabian horse identification.
Our collected dataset consists of 300 color images that were collected from 50
Arabian horse muzzle species. This dataset has been collected from 50 Arabian
horses with 6 muzzle print images each. A special care has been given to the
quality of the collected images. The collected images cover different quality
levels and degradation factors such as image rotation and image partiality for
simulating real time identification operations. This dataset can be used to
test the identification of Arabian horse system including the extracted
features and the selected classifier.
An overview of gradient descent optimization algorithms
Authors: Sebastian Ruder
Date: 2016-09-15 00:00:00

Gradient descent optimization algorithms, while increasingly popular, are
often used as black-box optimizers, as practical explanations of their
strengths and weaknesses are hard to come by. This article aims to provide the
reader with intuitions with regard to the behaviour of different algorithms
that will allow her to put them to use. In the course of this overview, we look
at different variants of gradient descent, summarize challenges, introduce the
most common optimization algorithms, review architectures in a parallel and
distributed setting, and investigate additional strategies for optimizing
gradient descent.
Modelling prosodic structure using Artificial Neural Networks
Authors: Jean-Philippe Bernardy, Charalambos Themistocleous
Date: 2017-06-13 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Classification, General Classification, speech-recognition, Speech Recognition

The ability to accurately perceive whether a speaker is asking a question or
is making a statement is crucial for any successful interaction. However,
learning and classifying tonal patterns has been a challenging task for
automatic speech recognition and for models of tonal representation, as tonal
contours are characterized by significant variation. This paper provides a
classification model of Cypriot Greek questions and statements. We evaluate two
state-of-the-art network architectures: a Long Short-Term Memory (LSTM) network
and a convolutional network (ConvNet). The ConvNet outperforms the LSTM in the
classification task and exhibited an excellent performance with 95%
classification accuracy.
Nonparametric Bayesian label prediction on a graph
Authors: Jarno Hartog, Harry van Zanten
Date: 2016-12-06 00:00:00
Tasks: Binary Classification, General Classification, Prediction

An implementation of a nonparametric Bayesian approach to solving binary
classification problems on graphs is described. A hierarchical Bayesian
approach with a randomly scaled Gaussian prior is considered. The prior uses
the graph Laplacian to take into account the underlying geometry of the graph.
A method based on a theoretically optimal prior and a more flexible variant
using partial conjugacy are proposed. Two simulated data examples and two
examples using real data are used in order to illustrate the proposed methods.
Raw Waveform-based Speech Enhancement by Fully Convolutional Networks
Authors: Szu-Wei Fu, Yu Tsao, Xugang Lu, Hisashi Kawai
Date: 2017-03-07 00:00:00
Tasks: Denoising, Speech Enhancement

This study proposes a fully convolutional network (FCN) model for raw
waveform-based speech enhancement. The proposed system performs speech
enhancement in an end-to-end (i.e., waveform-in and waveform-out) manner, which
dif-fers from most existing denoising methods that process the magnitude
spectrum (e.g., log power spectrum (LPS)) only. Because the fully connected
layers, which are involved in deep neural networks (DNN) and convolutional
neural networks (CNN), may not accurately characterize the local information of
speech signals, particularly with high frequency components, we employed fully
convolutional layers to model the waveform. More specifically, FCN consists of
only convolutional layers and thus the local temporal structures of speech
signals can be efficiently and effectively preserved with relatively few
weights. Experimental results show that DNN- and CNN-based models have limited
capability to restore high frequency components of waveforms, thus leading to
decreased intelligibility of enhanced speech. By contrast, the proposed FCN
model can not only effectively recover the waveforms but also outperform the
LPS-based DNN baseline in terms of short-time objective intelligibility (STOI)
and perceptual evaluation of speech quality (PESQ). In addition, the number of
model parameters in FCN is approximately only 0.2% compared with that in both
DNN and CNN.
Automatic Measurement of Pre-aspiration
Authors: Yaniv Sheena, Míša Hejná, Yossi Adi, Joseph Keshet
Date: 2017-04-05 00:00:00
Tasks: Friction, Structured Prediction

Pre-aspiration is defined as the period of glottal friction occurring in
sequences of vocalic/consonantal sonorants and phonetically voiceless
obstruents. We propose two machine learning methods for automatic measurement
of pre-aspiration duration: a feedforward neural network, which works at the
frame level; and a structured prediction model, which relies on manually
designed feature functions, and works at the segment level. The input for both
algorithms is a speech signal of an arbitrary length containing a single
obstruent, and the output is a pair of times which constitutes the
pre-aspiration boundaries. We train both models on a set of manually annotated
examples. Results suggest that the structured model is superior to the
frame-based model as it yields higher accuracy in predicting the boundaries and
generalizes to new speakers and new languages. Finally, we demonstrate the
applicability of our structured prediction algorithm by replicating linguistic
analysis of pre-aspiration in Aberystwyth English with high correlation.
Deep Frame Interpolation
Authors: Vladislav Samsonov
Date: 2017-06-04 00:00:00
Tasks: Optical Flow Estimation

This work presents a supervised learning based approach to the computer
vision problem of frame interpolation. The presented technique could also be
used in the cartoon animations since drawing each individual frame consumes a
noticeable amount of time. The most existing solutions to this problem use
unsupervised methods and focus only on real life videos with already high frame
rate. However, the experiments show that such methods do not work as well when
the frame rate becomes low and object displacements between frames becomes
large. This is due to the fact that interpolation of the large displacement
motion requires knowledge of the motion structure thus the simple techniques
such as frame averaging start to fail. In this work the deep convolutional
neural network is used to solve the frame interpolation problem. In addition,
it is shown that incorporating the prior information such as optical flow
improves the interpolation quality significantly.