pwc-archive/papers-with-abstracts huggingface dataset:
Whispered-to-voiced Alaryngeal Speech Conversion with Generative Adversarial Networks
Authors: Santiago Pascual, Antonio Bonafonte, Joan Serrà, Jose A. Gonzalez
Date: 2018-08-31 00:00:00
Tasks: Speech Enhancement, Speech Recognition

Most methods of voice restoration for patients suffering from aphonia either
produce whispered or monotone speech. Apart from intelligibility, this type of
speech lacks expressiveness and naturalness due to the absence of pitch
(whispered speech) or artificial generation of it (monotone speech). Existing
techniques to restore prosodic information typically combine a vocoder, which
parameterises the speech signal, with machine learning techniques that predict
prosodic information. In contrast, this paper describes an end-to-end neural
approach for estimating a fully-voiced speech waveform from whispered
alaryngeal speech. By adapting our previous work in speech enhancement with
generative adversarial networks, we develop a speaker-dependent model to
perform whispered-to-voiced speech conversion. Preliminary qualitative results
show effectiveness in re-generating voiced speech, with the creation of
realistic pitch contours.
Knowledge and Blameworthiness
Authors: Pavel Naumov, Jia Tao
Date: 2018-11-05 00:00:00

Blameworthiness of an agent or a coalition of agents is often defined in
terms of the principle of alternative possibilities: for the coalition to be
responsible for an outcome, the outcome must take place and the coalition
should have had a strategy to prevent it. In this article we argue that in the
settings with imperfect information, not only should the coalition have had a
strategy, but it also should have known that it had a strategy, and it should
have known what the strategy was. The main technical result of the article is a
sound and complete bimodal logic that describes the interplay between knowledge
and blameworthiness in strategic games with imperfect information.
Multi-Level Sensor Fusion with Deep Learning
Authors: Valentin Vielzeuf, Alexis Lechervy, Stéphane Pateux, Frédéric Jurie
Date: 2018-11-05 00:00:00
Tasks: Deep Learning, Sensor Fusion

In the context of deep learning, this article presents an original deep
network, namely CentralNet, for the fusion of information coming from different
sensors. This approach is designed to efficiently and automatically balance the
trade-off between early and late fusion (i.e. between the fusion of low-level
vs high-level information). More specifically, at each level of abstraction-the
different levels of deep networks-uni-modal representations of the data are fed
to a central neural network which combines them into a common embedding. In
addition, a multi-objective regularization is also introduced, helping to both
optimize the central network and the unimodal networks. Experiments on four
multimodal datasets not only show state-of-the-art performance, but also
demonstrate that CentralNet can actually choose the best possible fusion
strategy for a given problem.
Trainable Adaptive Window Switching for Speech Enhancement
Authors: Yuma Koizumi, Noboru Harada, Yoichi Haneda
Date: 2018-11-05 00:00:00
Tasks: Speech Enhancement

This study proposes a trainable adaptive window switching (AWS) method and
apply it to a deep-neural-network (DNN) for speech enhancement in the modified
discrete cosine transform domain. Time-frequency (T-F) mask processing in the
short-time Fourier transform (STFT)-domain is a typical speech enhancement
method. To recover the target signal precisely, DNN-based short-time frequency
transforms have recently been investigated and used instead of the STFT.
However, since such a fixed-resolution short-time frequency transform method
has a T-F resolution problem based on the uncertainty principle, not only the
short-time frequency transform but also the length of the windowing function
should be optimized. To overcome this problem, we incorporate AWS into the
speech enhancement procedure, and the windowing function of each time-frame is
manipulated using a DNN depending on the input signal. We confirmed that the
proposed method achieved a higher signal-to-distortion ratio than conventional
speech enhancement methods in fixed-resolution frequency domains.
A personal model of trumpery: Deception detection in a real-world high-stakes setting
Authors: Sophie van der Zee, Ronald Poppe, Alice Havrileck, Aurelien Baillon
Date: 2018-11-05 00:00:00
Tasks: Deception Detection

Language use reveals information about who we are and how we feel1-3. One of
the pioneers in text analysis, Walter Weintraub, manually counted which types
of words people used in medical interviews and showed that the frequency of
first-person singular pronouns (i.e., I, me, my) was a reliable indicator of
depression, with depressed people using I more often than people who are not
depressed4. Several studies have demonstrated that language use also differs
between truthful and deceptive statements5-7, but not all differences are
consistent across people and contexts, making prediction difficult8. Here we
show how well linguistic deception detection performs at the individual level
by developing a model tailored to a single individual: the current US
president. Using tweets fact-checked by an independent third party (Washington
Post), we found substantial linguistic differences between factually correct
and incorrect tweets and developed a quantitative model based on these
differences. Next, we predicted whether out-of-sample tweets were either
factually correct or incorrect and achieved a 73% overall accuracy. Our results
demonstrate the power of linguistic analysis in real-world deception research
when applied at the individual level and provide evidence that factually
incorrect tweets are not random mistakes of the sender.
Superregular grammars do not provide additional explanatory power but allow for a compact analysis of animal song
Authors: Takashi Morita, Hiroki Koda
Date: 2018-11-05 00:00:00

A pervasive belief with regard to the differences between human language and animal vocal sequences (song) is that they belong to different classes of computational complexity, with animal song belonging to regular languages, whereas human language is superregular. This argument, however, lacks empirical evidence since superregular analyses of animal song are understudied. The goal of this paper is to perform a superregular analysis of animal song, using data from gibbons as a case study, and demonstrate that a superregular analysis can be effectively used with non-human data. A key finding is that a superregular analysis does not increase explanatory power but rather provides for compact analysis: Fewer grammatical rules are necessary once superregularity is allowed. This pattern is analogous to a previous computational analysis of human language, and accordingly, the null hypothesis, that human language and animal song are governed by the same type of grammatical systems, cannot be rejected.
Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning
Authors: Jakob N. Foerster, Francis Song, Edward Hughes, Neil Burch, Iain Dunning, Shimon Whiteson, Matthew Botvinick, Michael Bowling
Date: 2018-11-04 00:00:00
Tasks: Decoder, Multi-agent Reinforcement Learning, Policy Gradient Methods, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

When observing the actions of others, humans make inferences about why they acted as they did, and what this implies about the world; humans also use the fact that their actions will be interpreted in this manner, allowing them to act informatively and thereby communicate efficiently with others. Although learning algorithms have recently achieved superhuman performance in a number of two-player, zero-sum games, scalable multi-agent reinforcement learning algorithms that can discover effective strategies and conventions in complex, partially observable settings have proven elusive. We present the Bayesian action decoder (BAD), a new multi-agent learning method that uses an approximate Bayesian update to obtain a public belief that conditions on the actions taken by all agents in the environment. BAD introduces a new Markov decision process, the public belief MDP, in which the action space consists of all deterministic partial policies, and exploits the fact that an agent acting only on this public belief state can still learn to use its private information if the action space is augmented to be over all partial policies mapping private information into environment actions. The Bayesian update is closely related to the theory of mind reasoning that humans carry out when observing others' actions. We first validate BAD on a proof-of-principle two-step matrix game, where it outperforms policy gradient methods; we then evaluate BAD on the challenging, cooperative partial-information card game Hanabi, where, in the two-player setting, it surpasses all previously published learning and hand-coded approaches, establishing a new state of the art.
DAPPER: Scaling Dynamic Author Persona Topic Model to Billion Word Corpora
Authors: Robert Giaquinto, Arindam Banerjee
Date: 2018-11-03 00:00:00
Tasks: Variational Inference

Extracting common narratives from multi-author dynamic text corpora requires
complex models, such as the Dynamic Author Persona (DAP) topic model. However,
such models are complex and can struggle to scale to large corpora, often
because of challenging non-conjugate terms. To overcome such challenges, in
this paper we adapt new ideas in approximate inference to the DAP model,
resulting in the DAP Performed Exceedingly Rapidly (DAPPER) topic model.
Specifically, we develop Conjugate-Computation Variational Inference (CVI)
based variational Expectation-Maximization (EM) for learning the model,
yielding fast, closed form updates for each document, replacing iterative
optimization in earlier work. Our results show significant improvements in
model fit and training time without needing to compromise the model's temporal
structure or the application of Regularized Variation Inference (RVI). We
demonstrate the scalability and effectiveness of the DAPPER model by extracting
health journeys from the CaringBridge corpus --- a collection of 9 million
journals written by 200,000 authors during health crises.
Variational Bayes Inference in Digital Receivers
Authors: Viet Hung Tran
Date: 2018-11-03 00:00:00
Tasks: LEMMA

The digital telecommunications receiver is an important context for inference
methodology, the key objective being to minimize the expected loss function in
recovering the transmitted information. For that criterion, the optimal
decision is the Bayesian minimum-risk estimator. However, the computational
load of the Bayesian estimator is often prohibitive and, hence, efficient
computational schemes are required. The design of novel schemes, striking new
balances between accuracy and computational load, is the primary concern of
this thesis. Two popular techniques, one exact and one approximate, will be
studied.
  The exact scheme is a recursive one, namely the generalized distributive law
(GDL), whose purpose is to distribute all operators across the conditionally
independent (CI) factors of the joint model, so as to reduce the total number
of operators required. In a novel theorem derived in this thesis, GDL, if
applicable, will be shown to guarantee such a reduction in all cases. An
associated lemma also quantifies this reduction. For practical use, two novel
algorithms, namely the no-longer-needed (NLN) algorithm and the generalized
form of the Markovian Forward-Backward (FB) algorithm, recursively factorizes
and computes the CI factors of an arbitrary model, respectively.
  The approximate scheme is an iterative one, namely the Variational Bayes (VB)
approximation, whose purpose is to find the independent (i.e. zero-order
Markov) model closest to the true joint model in the minimum Kullback-Leibler
divergence (KLD) sense. Despite being computationally efficient, this naive
mean field approximation confers only modest performance for highly correlated
models. A novel approximation, namely Transformed Variational Bayes (TVB), will
be designed in the thesis in order to relax the zero-order constraint in the VB
approximation, further reducing the KLD of the optimal approximation.
Low-Rank Tensor Modeling for Hyperspectral Unmixing Accounting for Spectral Variability
Authors: Tales Imbiriba, Ricardo Augusto Borsoi, José Carlos Moreira Bermudez
Date: 2018-11-02 00:00:00
Tasks: Hyperspectral Unmixing

Traditional hyperspectral unmixing methods neglect the underlying variability of spectral signatures often observed in typical hyperspectral images (HI), propagating these missmodeling errors throughout the whole unmixing process. Attempts to model material spectra as members of sets or as random variables tend to lead to severely ill-posed unmixing problems. Although parametric models have been proposed to overcome this drawback by handling endmember variability through generalizations of the mixing model, the success of these techniques depend on employing appropriate regularization strategies. Moreover, the existing approaches fail to adequately explore the natural multidimensinal representation of HIs. Recently, tensor-based strategies considered low-rank decompositions of hyperspectral images as an alternative to impose low-dimensional structures on the solutions of standard and multitemporal unmixing problems. These strategies, however, present two main drawbacks: 1) they confine the solutions to low-rank tensors, which often cannot represent the complexity of real-world scenarios; and 2) they lack guarantees that endmembers and abundances will be correctly factorized in their respective tensors. In this work, we propose a more flexible approach, called ULTRA-V, that imposes low-rank structures through regularizations whose strictness is controlled by scalar parameters. Simulations attest the superior accuracy of the method when compared with state-of-the-art unmixing algorithms that account for spectral variability.
An Adaptive Pruning Algorithm for Spoofing Localisation Based on Tropical Geometry
Authors: Emmanouil Theodosis, Petros Maragos
Date: 2018-11-01 00:00:00

The problem of spoofing attacks is increasingly relevant as digital systems
are becoming more ubiquitous. Thus the detection of such attacks and the
localisation of attackers have been objects of recent study. After an attack
has been detected, various algorithms have been proposed in order to localise
the attacker. In this work we propose a new adaptive pruning algorithm inspired
by the tropical and geometrical analysis of the traditional Viterbi pruning
algorithm to solve the localisation problem. In particular, the proposed
algorithm tries to localise the attacker by adapting the leniency parameter
based on estimates about the state of the solution space. These estimates stem
from the enclosed volume and the entropy of the solution space, as they were
introduced in our previous works.
AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification
Authors: Ronghui You, Zihan Zhang, Ziye Wang, Suyang Dai, Hiroshi Mamitsuka, Shanfeng Zhu
Date: 2018-11-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: General Classification, Multi-Label Text Classification, News Annotation, Product Categorization, Text Classification, Web Page Tagging

Extreme multi-label text classification (XMTC) is an important problem in the era of big data, for tagging a given text with the most relevant multiple labels from an extremely large-scale label set. XMTC can be found in many applications, such as item categorization, web page tagging, and news annotation. Traditionally most methods used bag-of-words (BOW) as inputs, ignoring word context as well as deep semantic information. Recent attempts to overcome the problems of BOW by deep learning still suffer from 1) failing to capture the important subtext for each label and 2) lack of scalability against the huge number of labels. We propose a new label tree-based deep learning model for XMTC, called AttentionXML, with two unique features: 1) a multi-label attention mechanism with raw text as input, which allows to capture the most relevant part of text to each label; and 2) a shallow and wide probabilistic label tree (PLT), which allows to handle millions of labels, especially for "tail labels". We empirically compared the performance of AttentionXML with those of eight state-of-the-art methods over six benchmark datasets, including Amazon-3M with around 3 million labels. AttentionXML outperformed all competing methods under all experimental settings. Experimental results also show that AttentionXML achieved the best performance against tail labels among label tree-based methods. The code and datasets are available at http://github.com/yourh/AttentionXML .
Can automated smoothing significantly improve benchmark time series classification algorithms?
Authors: James Large, Paul Southam, Anthony Bagnall
Date: 2018-11-01 00:00:00
Tasks: Classification, Dynamic Time Warping, General Classification, Time Series, Time Series Analysis, Time Series Classification

tl;dr: no, it cannot, at least not on average on the standard archive
problems. We assess whether using six smoothing algorithms (moving average,
exponential smoothing, Gaussian filter, Savitzky-Golay filter, Fourier
approximation and a recursive median sieve) could be automatically applied to
time series classification problems as a preprocessing step to improve the
performance of three benchmark classifiers (1-Nearest Neighbour with Euclidean
and Dynamic Time Warping distances, and Rotation Forest). We found no
significant improvement over unsmoothed data even when we set the smoothing
parameter through cross validation. We are not claiming smoothing has no worth.
It has an important role in exploratory analysis and helps with specific
classification problems where domain knowledge can be exploited. What we
observe is that the automatic application does not help and that we cannot
explain the improvement of other time series classification algorithms over the
baseline classifiers simply as a function of the absence of smoothing.
Online Diverse Learning to Rank from Partial-Click Feedback
Authors: Prakhar Gupta, Gaurush Hiranandani, Harvineet Singh, Branislav Kveton, Zheng Wen, Iftikhar Ahamath Burhanuddin
Date: 2018-11-01 00:00:00
Tasks: Learning-To-Rank, Recommendation Systems

Learning to rank is an important problem in machine learning and recommender
systems. In a recommender system, a user is typically recommended a list of
items. Since the user is unlikely to examine the entire recommended list,
partial feedback arises naturally. At the same time, diverse recommendations
are important because it is challenging to model all tastes of the user in
practice. In this paper, we propose the first algorithm for online learning to
rank diverse items from partial-click feedback. We assume that the user
examines the list of recommended items until the user is attracted by an item,
which is clicked, and does not examine the rest of the items. This model of
user behavior is known as the cascade model. We propose an online learning
algorithm, cascadelsb, for solving our problem. The algorithm actively explores
the tastes of the user with the objective of learning to recommend the optimal
diverse list. We analyze the algorithm and prove a gap-free upper bound on its
n-step regret. We evaluate cascadelsb on both synthetic and real-world
datasets, compare it to various baselines, and show that it learns even when
our modeling assumptions do not hold exactly.
Characterizing Well-Behaved vs. Pathological Deep Neural Networks
Authors: Antoine Labatie
Date: 2018-11-07 00:00:00

We introduce a novel approach, requiring only mild assumptions, for the characterization of deep neural networks at initialization. Our approach applies both to fully-connected and convolutional networks and easily incorporates batch normalization and skip-connections. Our key insight is to consider the evolution with depth of statistical moments of signal and noise, thereby characterizing the presence or absence of pathologies in the hypothesis space encoded by the choice of hyperparameters. We establish: (i) for feedforward networks, with and without batch normalization, the multiplicativity of layer composition inevitably leads to ill-behaved moments and pathologies; (ii) for residual networks with batch normalization, on the other hand, skip-connections induce power-law rather than exponential behaviour, leading to well-behaved moments and no pathology.
Forging new worlds: high-resolution synthetic galaxies with chained generative adversarial networks
Authors: Levi Fussell, Ben Moews
Date: 2018-11-07 00:00:00
Tasks: Astronomy

Astronomy of the 21st century increasingly finds itself with extreme
quantities of data. This growth in data is ripe for modern technologies such as
deep image processing, which has the potential to allow astronomers to
automatically identify, classify, segment and deblend various astronomical
objects. In this paper, we explore the use of chained generative adversarial
networks (GANs), a class of generative models that learn mappings from latent
spaces to data distributions by modelling the joint distribution of the data,
to produce physically realistic galaxy images as one use case of such models.
In cosmology, such datasets can aid in the calibration of shape measurements
for weak lensing by augmenting data with synthetic images. By measuring the
distributions of multiple physical properties, we show that images generated
with our approach closely follow the distributions of real galaxies, further
establishing state-of-the-art GAN architectures as a valuable tool for
modern-day astronomy.
Class-conditional embeddings for music source separation
Authors: Prem Seetharaman, Gordon Wichern, Shrikant Venkataramani, Jonathan Le Roux
Date: 2018-11-07 00:00:00
Tasks: Clustering, Deep Clustering, Music Source Separation

Isolating individual instruments in a musical mixture has a myriad of
potential applications, and seems imminently achievable given the levels of
performance reached by recent deep learning methods. While most musical source
separation techniques learn an independent model for each instrument, we
propose using a common embedding space for the time-frequency bins of all
instruments in a mixture inspired by deep clustering and deep attractor
networks. Additionally, an auxiliary network is used to generate parameters of
a Gaussian mixture model (GMM) where the posterior distribution over GMM
components in the embedding space can be used to create a mask that separates
individual sources from a mixture. In addition to outperforming a
mask-inference baseline on the MUSDB-18 dataset, our embedding space is easily
interpretable and can be used for query-based separation.
Prototypical Clustering Networks for Dermatological Disease Diagnosis
Authors: Viraj Prabhu, Anitha Kannan, Murali Ravuri, Manish Chablani, David Sontag, Xavier Amatriain
Date: 2018-11-07 00:00:00
Tasks: Clustering, General Classification, image-classification, Image Classification

We consider the problem of image classification for the purpose of aiding
doctors in dermatological diagnosis. Dermatological diagnosis poses two major
challenges for standard off-the-shelf techniques: First, the data distribution
is typically extremely long tailed. Second, intra-class variability is often
large. To address the first issue, we formulate the problem as low-shot
learning, where once deployed, a base classifier must rapidly generalize to
diagnose novel conditions given very few labeled examples. To model diverse
classes effectively, we propose Prototypical Clustering Networks (PCN), an
extension to Prototypical Networks that learns a mixture of prototypes for each
class. Prototypes are initialized for each class via clustering and refined via
an online update scheme. Classification is performed by measuring similarity to
a weighted combination of prototypes within a class, where the weights are the
inferred cluster responsibilities. We demonstrate the strengths of our approach
in effective diagnosis on a realistic dataset of dermatological conditions.