pwc-archive/papers-with-abstracts huggingface dataset:
End-to-end Audiovisual Speech Activity Detection with Bimodal Recurrent Neural Models
Authors: Fei Tao, Carlos Busso
Date: 2018-09-12 00:00:00
Tasks: Action Detection, Activity Detection, Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Speech activity detection (SAD) plays an important role in current speech
processing systems, including automatic speech recognition (ASR). SAD is
particularly difficult in environments with acoustic noise. A practical
solution is to incorporate visual information, increasing the robustness of the
SAD approach. An audiovisual system has the advantage of being robust to
different speech modes (e.g., whisper speech) or background noise. Recent
advances in audiovisual speech processing using deep learning have opened
opportunities to capture in a principled way the temporal relationships between
acoustic and visual features. This study explores this idea proposing a
\emph{bimodal recurrent neural network} (BRNN) framework for SAD. The approach
models the temporal dynamic of the sequential audiovisual data, improving the
accuracy and robustness of the proposed SAD system. Instead of estimating
hand-crafted features, the study investigates an end-to-end training approach,
where acoustic and visual features are directly learned from the raw data
during training. The experimental evaluation considers a large audiovisual
corpus with over 60.8 hours of recordings, collected from 105 speakers. The
results demonstrate that the proposed framework leads to absolute improvements
up to 1.2% under practical scenarios over a VAD baseline using only audio
implemented with deep neural network (DNN). The proposed approach achieves
92.7% F1-score when it is evaluated using the sensors from a portable tablet
under noisy acoustic environment, which is only 1.0% lower than the performance
obtained under ideal conditions (e.g., clean speech obtained with a high
definition camera and a close-talking microphone).
Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications
Authors: Geir Thore Berge, Ole-Christoffer Granmo, Tor Oddbjørn Tveit, Morten Goodwin, Lei Jiao, Bernt Viggo Matheussen
Date: 2018-09-12 00:00:00
Tasks: CPU, GPU, Natural Language Understanding, Text Categorization

Medical applications challenge today's text categorization techniques by
demanding both high accuracy and ease-of-interpretation. Although deep learning
has provided a leap ahead in accuracy, this leap comes at the sacrifice of
interpretability. To address this accuracy-interpretability challenge, we here
introduce, for the first time, a text categorization approach that leverages
the recently introduced Tsetlin Machine. In all brevity, we represent the terms
of a text as propositional variables. From these, we capture categories using
simple propositional formulae, such as: if "rash" and "reaction" and
"penicillin" then Allergy. The Tsetlin Machine learns these formulae from a
labelled text, utilizing conjunctive clauses to represent the particular facets
of each category. Indeed, even the absence of terms (negated features) can be
used for categorization purposes. Our empirical comparison with Na\"ive Bayes,
decision trees, linear support vector machines (SVMs), random forest, long
short-term memory (LSTM) neural networks, and other techniques, is quite
conclusive. The Tsetlin Machine either performs on par with or outperforms all
of the evaluated methods on both the 20 Newsgroups and IMDb datasets, as well
as on a non-public clinical dataset. On average, the Tsetlin Machine delivers
the best recall and precision scores across the datasets. Finally, our GPU
implementation of the Tsetlin Machine executes 5 to 15 times faster than the
CPU implementation, depending on the dataset. We thus believe that our novel
approach can have a significant impact on a wide range of text analysis
applications, forming a promising starting point for deeper natural language
understanding with the Tsetlin Machine.
The Inductive Bias of Restricted f-GANs
Authors: Shuang Liu, Kamalika Chaudhuri
Date: 2018-09-12 00:00:00
Tasks: Inductive Bias

Generative adversarial networks are a novel method for statistical inference
that have achieved much empirical success; however, the factors contributing to
this success remain ill-understood. In this work, we attempt to analyze
generative adversarial learning -- that is, statistical inference as the result
of a game between a generator and a discriminator -- with the view of
understanding how it differs from classical statistical inference solutions
such as maximum likelihood inference and the method of moments.
  Specifically, we provide a theoretical characterization of the distribution
inferred by a simple form of generative adversarial learning called restricted
f-GANs -- where the discriminator is a function in a given function class, the
distribution induced by the generator is restricted to lie in a pre-specified
distribution class and the objective is similar to a variational form of the
f-divergence. A consequence of our result is that for linear KL-GANs -- that
is, when the discriminator is a linear function over some feature space and f
corresponds to the KL-divergence -- the distribution induced by the optimal
generator is neither the maximum likelihood nor the method of moments solution,
but an interesting combination of both.
Unpaired Brain MR-to-CT Synthesis using a Structure-Constrained CycleGAN
Authors: Heran Yang, Jian Sun, Aaron Carass, Can Zhao, Junghoon Lee, Zongben Xu, Jerry Prince
Date: 2018-09-12 00:00:00
Tasks: Image Generation, Position

The cycleGAN is becoming an influential method in medical image synthesis.
However, due to a lack of direct constraints between input and synthetic
images, the cycleGAN cannot guarantee structural consistency between these two
images, and such consistency is of extreme importance in medical imaging. To
overcome this, we propose a structure-constrained cycleGAN for brain MR-to-CT
synthesis using unpaired data that defines an extra structure-consistency loss
based on the modality independent neighborhood descriptor to constrain
structural consistency. Additionally, we use a position-based selection
strategy for selecting training images instead of a completely random selection
scheme. Experimental results on synthesizing CT images from brain MR images
demonstrate that our method is better than the conventional cycleGAN and
approximates the cycleGAN trained with paired data.
Image contrast enhancement using fuzzy logic
Authors: Sandeep Joshi, Samrudh Kumar
Date: 2018-09-12 00:00:00
Tasks: Image Enhancement

Image enhancement is a method of improving the quality of an image and
contrast is a major aspect. Traditional methods of contrast enhancement like
histogram equalization results in over/under enhancement of the image
especially a lower resolution one. This paper aims at developing a new Fuzzy
Inference System to enhance the contrast of the low resolution images
overcoming the shortcomings of the traditional methods. Results obtained using
both the approaches are compared.
Joint Sub-bands Learning with Clique Structures for Wavelet Domain Super-Resolution
Authors: Zhisheng Zhong, Tiancheng Shen, Yibo Yang, Zhouchen Lin, Chao Zhang
Date: 2018-09-12 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Image Super-Resolution, Super-Resolution

Convolutional neural networks (CNNs) have recently achieved great success in
single-image super-resolution (SISR). However, these methods tend to produce
over-smoothed outputs and miss some textural details. To solve these problems,
we propose the Super-Resolution CliqueNet (SRCliqueNet) to reconstruct the high
resolution (HR) image with better textural details in the wavelet domain. The
proposed SRCliqueNet firstly extracts a set of feature maps from the low
resolution (LR) image by the clique blocks group. Then we send the set of
feature maps to the clique up-sampling module to reconstruct the HR image. The
clique up-sampling module consists of four sub-nets which predict the high
resolution wavelet coefficients of four sub-bands. Since we consider the edge
feature properties of four sub-bands, the four sub-nets are connected to the
others so that they can learn the coefficients of four sub-bands jointly.
Finally we apply inverse discrete wavelet transform (IDWT) to the output of
four sub-nets at the end of the clique up-sampling module to increase the
resolution and reconstruct the HR image. Extensive quantitative and qualitative
experiments on benchmark datasets show that our method achieves superior
performance over the state-of-the-art methods.
Combined Reinforcement Learning via Abstract Representations
Authors: Vincent François-Lavet, Yoshua Bengio, Doina Precup, Joelle Pineau
Date: 2018-09-12 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

In the quest for efficient and robust reinforcement learning methods, both
model-free and model-based approaches offer advantages. In this paper we
propose a new way of explicitly bridging both approaches via a shared
low-dimensional learned encoding of the environment, meant to capture
summarizing abstractions. We show that the modularity brought by this approach
leads to good generalization while being computationally efficient, with
planning happening in a smaller latent state space. In addition, this approach
recovers a sufficient low-dimensional representation of the environment, which
opens up new strategies for interpretable AI, exploration and transfer
learning.
Emo2Vec: Learning Generalized Emotion Representation by Multi-task Training
Authors: Peng Xu, Andrea Madotto, Chien-Sheng Wu, Ji Ho Park, Pascale Fung
Date: 2018-09-12 00:00:00
Proceeding: WS 2018 10
Tasks: Abusive Language, Classification, General Classification, Multi-Task Learning, regression, Sentiment Analysis

In this paper, we propose Emo2Vec which encodes emotional semantics into
vectors. We train Emo2Vec by multi-task learning six different emotion-related
tasks, including emotion/sentiment analysis, sarcasm classification, stress
detection, abusive language classification, insult detection, and personality
recognition. Our evaluation of Emo2Vec shows that it outperforms existing
affect-related representations, such as Sentiment-Specific Word Embedding and
DeepMoji embeddings with much smaller training corpora. When concatenated with
GloVe, Emo2Vec achieves competitive performances to state-of-the-art results on
several tasks using a simple logistic regression classifier.
Hyperprior Induced Unsupervised Disentanglement of Latent Representations
Authors: Abdul Fatir Ansari, Harold Soh
Date: 2018-09-12 00:00:00
Tasks: Disentanglement

We address the problem of unsupervised disentanglement of latent
representations learnt via deep generative models. In contrast to current
approaches that operate on the evidence lower bound (ELBO), we argue that
statistical independence in the latent space of VAEs can be enforced in a
principled hierarchical Bayesian manner. To this effect, we augment the
standard VAE with an inverse-Wishart (IW) prior on the covariance matrix of the
latent code. By tuning the IW parameters, we are able to encourage (or
discourage) independence in the learnt latent dimensions. Extensive
experimental results on a range of datasets (2DShapes, 3DChairs, 3DFaces and
CelebA) show our approach to outperform the $\beta$-VAE and is competitive with
the state-of-the-art FactorVAE. Our approach achieves significantly better
disentanglement and reconstruction on a new dataset (CorrelatedEllipses) which
introduces correlations between the factors of variation.
Discovering Topical Interactions in Text-based Cascades using Hidden Markov Hawkes Processes
Authors: Srikanta Bedathur, Indrajit Bhattacharya, Jayesh Choudhari, Anirban Dasgupta
Date: 2018-09-12 00:00:00

Social media conversations unfold based on complex interactions between
users, topics and time. While recent models have been proposed to capture
network strengths between users, users' topical preferences and temporal
patterns between posting and response times, interaction patterns between
topics has not been studied. We propose the Hidden Markov Hawkes Process (HMHP)
that incorporates topical Markov Chains within Hawkes processes to jointly
model topical interactions along with user-user and user-topic patterns. We
propose a Gibbs sampling algorithm for HMHP that jointly infers the network
strengths, diffusion paths, the topics of the posts as well as the topic-topic
interactions. We show using experiments on real and semi-synthetic data that
HMHP is able to generalize better and recover the network strengths, topics and
diffusion paths more accurately than state-of-the-art baselines. More
interestingly, HMHP finds insightful interactions between topics in real tweets
which no existing model is able to do.
But How Does It Work in Theory? Linear SVM with Random Features
Authors: Yitong Sun, Anna Gilbert, Ambuj Tewari
Date: 2018-09-12 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: feature selection

We prove that, under low noise assumptions, the support vector machine with
$N\ll m$ random features (RFSVM) can achieve the learning rate faster than
$O(1/\sqrt{m})$ on a training set with $m$ samples when an optimized feature
map is used. Our work extends the previous fast rate analysis of random
features method from least square loss to 0-1 loss. We also show that the
reweighted feature selection method, which approximates the optimized feature
map, helps improve the performance of RFSVM in experiments on a synthetic data
set.
Multi-task Deep Reinforcement Learning with PopArt
Authors: Matteo Hessel, Hubert Soyer, Lasse Espeholt, Wojciech Czarnecki, Simon Schmitt, Hado van Hasselt
Date: 2018-09-12 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, Multi-Task Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The reinforcement learning community has made great strides in designing
algorithms capable of exceeding human performance on specific tasks. These
algorithms are mostly trained one task at the time, each new task requiring to
train a brand new agent instance. This means the learning algorithm is general,
but each solution is not; each agent can only solve the one task it was trained
on. In this work, we study the problem of learning to master not one but
multiple sequential-decision tasks at once. A general issue in multi-task
learning is that a balance must be found between the needs of multiple tasks
competing for the limited resources of a single learning system. Many learning
algorithms can get distracted by certain tasks in the set of tasks to solve.
Such tasks appear more salient to the learning process, for instance because of
the density or magnitude of the in-task rewards. This causes the algorithm to
focus on those salient tasks at the expense of generality. We propose to
automatically adapt the contribution of each task to the agent's updates, so
that all tasks have a similar impact on the learning dynamics. This resulted in
state of the art performance on learning to play all games in a set of 57
diverse Atari games. Excitingly, our method learned a single trained policy -
with a single set of weights - that exceeds median human performance. To our
knowledge, this was the first time a single agent surpassed human-level
performance on this multi-task domain. The same approach also demonstrated
state of the art performance on a set of 30 tasks in the 3D reinforcement
learning platform DeepMind Lab.
Learning structure-from-motion from motion
Authors: Clément Pinard, Laure Chevalley, Antoine Manzanera, David Filliat
Date: 2018-09-12 00:00:00
Tasks: Depth Estimation, Depth Prediction, Domain Adaptation

This work is based on a questioning of the quality metrics used by deep
neural networks performing depth prediction from a single image, and then of
the usability of recently published works on unsupervised learning of depth
from videos. To overcome their limitations, we propose to learn in the same
unsupervised manner a depth map inference system from monocular videos that
takes a pair of images as input. This algorithm actually learns
structure-from-motion from motion, and not only structure from context
appearance. The scale factor issue is explicitly treated, and the absolute
depth map can be estimated from camera displacement magnitude, which can be
easily measured from cheap external sensors. Our solution is also much more
robust with respect to domain variation and adaptation via fine tuning, because
it does not rely entirely in depth from context. Two use cases are considered,
unstabilized moving camera videos, and stabilized ones. This choice is
motivated by the UAV (for Unmanned Aerial Vehicle) use case that generally
provides reliable orientation measurement. We provide a set of experiments
showing that, used in real conditions where only speed can be known, our
network outperforms competitors for most depth quality measures. Results are
given on the well known KITTI dataset, which provides robust stabilization for
our second use case, but also contains moving scenes which are very typical of
the in-car road context. We then present results on a synthetic dataset that we
believe to be more representative of typical UAV scenes. Lastly, we present two
domain adaptation use cases showing superior robustness of our method compared
to single view depth algorithms, which indicates that it is better suited for
highly variable visual contexts.
Multi range Real-time depth inference from a monocular stabilized footage using a Fully Convolutional Neural Network
Authors: Clément Pinard, Laure Chevalley, Antoine Manzanera, David Filliat
Date: 2018-09-12 00:00:00

Using a neural network architecture for depth map inference from monocular
stabilized videos with application to UAV videos in rigid scenes, we propose a
multi-range architecture for unconstrained UAV flight, leveraging flight data
from sensors to make accurate depth maps for uncluttered outdoor environment.
We try our algorithm on both synthetic scenes and real UAV flight data.
Quantitative results are given for synthetic scenes with a slightly noisy
orientation, and show that our multi-range architecture improves depth
inference. Along with this article is a video that present our results more
thoroughly.
Unsupervised Representation Learning of Speech for Dialect Identification
Authors: Suwon Shon, Wei-Ning Hsu, James Glass
Date: 2018-09-12 00:00:00
Tasks: Dialect Identification, Disentanglement, Representation Learning

In this paper, we explore the use of a factorized hierarchical variational
autoencoder (FHVAE) model to learn an unsupervised latent representation for
dialect identification (DID). An FHVAE can learn a latent space that separates
the more static attributes within an utterance from the more dynamic attributes
by encoding them into two different sets of latent variables. Useful factors
for dialect identification, such as phonetic or linguistic content, are encoded
by a segmental latent variable, while irrelevant factors that are relatively
constant within a sequence, such as a channel or a speaker information, are
encoded by a sequential latent variable. The disentanglement property makes the
segmental latent variable less susceptible to channel and speaker variation,
and thus reduces degradation from channel domain mismatch. We demonstrate that
on fully-supervised DID tasks, an end-to-end model trained on the features
extracted from the FHVAE model achieves the best performance, compared to the
same model trained on conventional acoustic features and an i-vector based
system. Moreover, we also show that the proposed approach can leverage a large
amount of unlabeled data for FHVAE training to learn domain-invariant features
for DID, and significantly improve the performance in a low-resource condition,
where the labels for the in-domain data are not available.
End-to-end depth from motion with stabilized monocular videos
Authors: Clément Pinard, Laure Chevalley, Antoine Manzanera, David Filliat
Date: 2018-09-12 00:00:00
Tasks: Depth Estimation, Depth Prediction

We propose a depth map inference system from monocular videos based on a
novel dataset for navigation that mimics aerial footage from gimbal stabilized
monocular camera in rigid scenes. Unlike most navigation datasets, the lack of
rotation implies an easier structure from motion problem which can be leveraged
for different kinds of tasks such as depth inference and obstacle avoidance. We
also propose an architecture for end-to-end depth inference with a fully
convolutional network. Results show that although tied to camera inner
parameters, the problem is locally solvable and leads to good quality depth
prediction.
Hate Speech Dataset from a White Supremacy Forum
Authors: Ona de Gibert, Naiara Perez, Aitor García-Pablos, Montse Cuadros
Date: 2018-09-12 00:00:00
Proceeding: WS 2018 10
Tasks: Hate Speech Detection, Sentence

Hate speech is commonly defined as any communication that disparages a target
group of people based on some characteristic such as race, colour, ethnicity,
gender, sexual orientation, nationality, religion, or other characteristic. Due
to the massive rise of user-generated web content on social media, the amount
of hate speech is also steadily increasing. Over the past years, interest in
online hate speech detection and, particularly, the automation of this task has
continuously grown, along with the societal impact of the phenomenon. This
paper describes a hate speech dataset composed of thousands of sentences
manually labelled as containing hate speech or not. The sentences have been
extracted from Stormfront, a white supremacist forum. A custom annotation tool
has been developed to carry out the manual labelling task which, among other
things, allows the annotators to choose whether to read the context of a
sentence before labelling it. The paper also provides a thoughtful qualitative
and quantitative study of the resulting dataset and several baseline
experiments with different classification models. The dataset is publicly
available.
Frame-level speaker embeddings for text-independent speaker recognition and analysis of end-to-end model
Authors: Suwon Shon, Hao Tang, James Glass
Date: 2018-09-12 00:00:00
Tasks: Speaker Recognition, Text-Independent Speaker Recognition

In this paper, we propose a Convolutional Neural Network (CNN) based speaker
recognition model for extracting robust speaker embeddings. The embedding can
be extracted efficiently with linear activation in the embedding layer. To
understand how the speaker recognition model operates with text-independent
input, we modify the structure to extract frame-level speaker embeddings from
each hidden layer. We feed utterances from the TIMIT dataset to the trained
network and use several proxy tasks to study the networks ability to represent
speech input and differentiate voice identity. We found that the networks are
better at discriminating broad phonetic classes than individual phonemes. In
particular, frame-level embeddings that belong to the same phonetic classes are
similar (based on cosine distance) for the same speaker. The frame level
representation also allows us to analyze the networks at the frame level, and
has the potential for other analyses to improve speaker recognition.