pwc-archive/papers-with-abstracts huggingface dataset:
Speech and Speaker Recognition from Raw Waveform with SincNet
Authors: Mirco Ravanelli, Yoshua Bengio
Date: 2018-12-13 00:00:00
Tasks: Inductive Bias, Speaker Recognition, speech-recognition, Speech Recognition

Deep neural networks can learn complex and abstract representations, that are
progressively obtained by combining simpler ones. A recent trend in speech and
speaker recognition consists in discovering these representations starting from
raw audio samples directly. Differently from standard hand-crafted features
such as MFCCs or FBANK, the raw waveform can potentially help neural networks
discover better and more customized representations. The high-dimensional raw
inputs, however, can make training significantly more challenging. This paper
summarizes our recent efforts to develop a neural architecture that efficiently
processes speech from audio waveforms. In particular, we propose SincNet, a
novel Convolutional Neural Network (CNN) that encourages the first layer to
discover meaningful filters by exploiting parametrized sinc functions. In
contrast to standard CNNs, which learn all the elements of each filter, only
low and high cutoff frequencies of band-pass filters are directly learned from
data. This inductive bias offers a very compact way to derive a customized
front-end, that only depends on some parameters with a clear physical meaning.
Our experiments, conducted on both speaker and speech recognition, show that
the proposed architecture converges faster, performs better, and is more
computationally efficient than standard CNNs.
Deep neural networks algorithms for stochastic control problems on finite horizon: numerical applications
Authors: Achref Bachouch, Côme Huré, Nicolas Langrené, Huyen Pham
Date: 2018-12-13 00:00:00
Tasks: Management, Quantization

This paper presents several numerical applications of deep learning-based algorithms that have been introduced in [HPBL18]. Numerical and comparative tests using TensorFlow illustrate the performance of our different algorithms, namely control learning by performance iteration (algorithms NNcontPI and ClassifPI), control learning by hybrid iteration (algorithms Hybrid-Now and Hybrid-LaterQ), on the 100-dimensional nonlinear PDEs examples from [EHJ17] and on quadratic backward stochastic differential equations as in [CR16]. We also performed tests on low-dimension control problems such as an option hedging problem in finance, as well as energy storage problems arising in the valuation of gas storage and in microgrid management. Numerical results and comparisons to quantization-type algorithms Qknn, as an efficient algorithm to numerically solve low-dimensional control problems, are also provided; and some corresponding codes are available on https://github.com/comeh/.
Visual Social Relationship Recognition
Authors: Junnan Li, Yongkang Wong, Qi Zhao, Mohan S. Kankanhalli
Date: 2018-12-13 00:00:00
Tasks: Visual Social Relationship Recognition

Social relationships form the basis of social structure of humans. Developing
computational models to understand social relationships from visual data is
essential for building intelligent machines that can better interact with
humans in a social environment. In this work, we study the problem of visual
social relationship recognition in images. We propose a Dual-Glance model for
social relationship recognition, where the first glance fixates at the person
of interest and the second glance deploys attention mechanism to exploit
contextual cues. To enable this study, we curated a large scale People in
Social Context (PISC) dataset, which comprises of 23,311 images and 79,244
person pairs with annotated social relationships. Since visually identifying
social relationship bears certain degree of uncertainty, we further propose an
Adaptive Focal Loss to leverage the ambiguous annotations for more effective
learning. We conduct extensive experiments to quantitatively and qualitatively
demonstrate the efficacy of our proposed method, which yields state-of-the-art
performance on social relationship recognition.
Soft Actor-Critic Algorithms and Applications
Authors: Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, George Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter Abbeel, Sergey Levine
Date: 2018-12-13 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
End to End Video Segmentation for Driving : Lane Detection For Autonomous Car
Authors: Wenhui Zhang, Tejas Mahale
Date: 2018-12-13 00:00:00
Tasks: Autonomous Driving, Lane Detection, Semantic Segmentation, Video Segmentation, Video Semantic Segmentation

Safety and decline of road traffic accidents remain important issues of
autonomous driving. Statistics show that unintended lane departure is a leading
cause of worldwide motor vehicle collisions, making lane detection the most
promising and challenge task for self-driving. Today, numerous groups are
combining deep learning techniques with computer vision problems to solve
self-driving problems. In this paper, a Global Convolution Networks (GCN) model
is used to address both classification and localization issues for semantic
segmentation of lane. We are using color-based segmentation is presented and
the usability of the model is evaluated. A residual-based boundary refinement
and Adam optimization is also used to achieve state-of-art performance. As
normal cars could not afford GPUs on the car, and training session for a
particular road could be shared by several cars. We propose a framework to get
it work in real world. We build a real time video transfer system to get video
from the car, get the model trained in edge server (which is equipped with
GPUs), and send the trained model back to the car.
Construction of Differentially Private Empirical Distributions from a low-order Marginals Set through Solving Linear Equations with l2 Regularization
Authors: Evercita C. Eugenio, Fang Liu
Date: 2018-12-12 00:00:00
Tasks: L2 Regularization

We introduce a new algorithm, Construction of dIfferentially Private Empirical Distributions from a low-order marginals set tHrough solving linear Equations with l2 Regularization (CIPHER), that produces differentially private empirical joint distributions from a set of low-order marginals. CIPHER is conceptually simple and requires no more than decomposing joint probabilities via basic probability rules to construct a linear equation set and subsequently solving the equations. Compared to the full-dimensional histogram (FDH) sanitization, CIPHER has drastic\-ally lower requirements on computational storage and memory, which is practically attractive especially considering that the high-order signals preserved by the FDH sanitization are likely just sample randomness and rarely of interest. Our experiments demonstrate that CIPHER outperforms the multiplicative weighting exponential mechanism in preserving original information and has similar or superior cost-normalized utility to FDH sanitization at the same privacy budget.
Bayesian Sparsification of Gated Recurrent Neural Networks
Authors: Ekaterina Lobacheva, Nadezhda Chirkova, Dmitry Vetrov
Date: 2018-12-12 00:00:00
Proceeding: NIPS Workshop CDNNRIA 2018

Bayesian methods have been successfully applied to sparsify weights of neural
networks and to remove structure units from the networks, e. g. neurons. We
apply and further develop this approach for gated recurrent architectures.
Specifically, in addition to sparsification of individual weights and neurons,
we propose to sparsify preactivations of gates and information flow in LSTM. It
makes some gates and information flow components constant, speeds up forward
pass and improves compression. Moreover, the resulting structure of gate
sparsity is interpretable and depends on the task. Code is available on github:
https://github.com/tipt0p/SparseBayesianRNN
FPETS : Fully Parallel End-to-End Text-to-Speech System
Authors: Dabiao Ma, Zhiba Su, Wenxuan Wang, Yuhao Lu
Date: 2018-12-12 00:00:00
Tasks: text-to-speech, Text to Speech

End-to-end Text-to-speech (TTS) system can greatly improve the quality of synthesised speech. But it usually suffers form high time latency due to its auto-regressive structure. And the synthesised speech may also suffer from some error modes, e.g. repeated words, mispronunciations, and skipped words. In this paper, we propose a novel non-autoregressive, fully parallel end-to-end TTS system (FPETS). It utilizes a new alignment model and the recently proposed U-shape convolutional structure, UFANS. Different from RNN, UFANS can capture long term information in a fully parallel manner. Trainable position encoding and two-step training strategy are used for learning better alignments. Experimental results show FPETS utilizes the power of parallel computation and reaches a significant speed up of inference compared with state-of-the-art end-to-end TTS systems. More specifically, FPETS is 600X faster than Tacotron2, 50X faster than DCTTS and 10X faster than Deep Voice3. And FPETS can generates audios with equal or better quality and fewer errors comparing with other system. As far as we know, FPETS is the first end-to-end TTS system which is fully parallel.
Persian Vowel recognition with MFCC and ANN on PCVC speech dataset
Authors: Saber Malekzadeh, Mohammad Hossein Gholizadeh, Seyed Naser Razavi
Date: 2018-12-17 00:00:00
Tasks: Phoneme Recognition

In this paper a new method for recognition of consonant-vowel phonemes
combination on a new Persian speech dataset titled as PCVC (Persian
Consonant-Vowel Combination) is proposed which is used to recognize Persian
phonemes. In PCVC dataset, there are 20 sets of audio samples from 10 speakers
which are combinations of 23 consonant and 6 vowel phonemes of Persian
language. In each sample, there is a combination of one vowel and one
consonant. First, the consonant phoneme is pronounced and just after it, the
vowel phoneme is pronounced. Each sound sample is a frame of 2 seconds of
audio. In every 2 seconds, there is an average of 0.5 second speech and the
rest is silence. In this paper, the proposed method is the implementations of
the MFCC (Mel Frequency Cepstrum Coefficients) on every partitioned sound
sample. Then, every train sample of MFCC vector is given to a multilayer
perceptron feed-forward ANN (Artificial Neural Network) for training process.
At the end, the test samples are examined on ANN model for phoneme recognition.
After training and testing process, the results are presented in recognition of
vowels. Then, the average percent of recognition for vowel phonemes are
computed.
Domain Adaptation on Graphs by Learning Graph Topologies: Theoretical Analysis and an Algorithm
Authors: Elif Vural
Date: 2018-12-17 00:00:00
Tasks: Domain Adaptation, General Classification, GRAPH DOMAIN ADAPTATION, Graph Learning

Traditional machine learning algorithms assume that the training and test
data have the same distribution, while this assumption does not necessarily
hold in real applications. Domain adaptation methods take into account the
deviations in the data distribution. In this work, we study the problem of
domain adaptation on graphs. We consider a source graph and a target graph
constructed with samples drawn from data manifolds. We study the problem of
estimating the unknown class labels on the target graph using the label
information on the source graph and the similarity between the two graphs. We
particularly focus on a setting where the target label function is learnt such
that its spectrum is similar to that of the source label function. We first
propose a theoretical analysis of domain adaptation on graphs and present
performance bounds that characterize the target classification error in terms
of the properties of the graphs and the data manifolds. We show that the
classification performance improves as the topologies of the graphs get more
balanced, i.e., as the numbers of neighbors of different graph nodes become
more proportionate, and weak edges with small weights are avoided. Our results
also suggest that graph edges between too distant data samples should be
avoided for good generalization performance. We then propose a graph domain
adaptation algorithm inspired by our theoretical findings, which estimates the
label functions while learning the source and target graph topologies at the
same time. The joint graph learning and label estimation problem is formulated
through an objective function relying on our performance bounds, which is
minimized with an alternating optimization scheme. Experiments on synthetic and
real data sets suggest that the proposed method outperforms baseline
approaches.
Three-Dimensional Dose Prediction for Lung IMRT Patients with Deep Neural Networks: Robust Learning from Heterogeneous Beam Configurations
Authors: Ana M. Barragan-Montero, Dan Nguyen, Weiguo Lu, Mu-Han Lin, Xavier Geets, Edmond Sterpin, Steve Jiang
Date: 2018-12-17 00:00:00
Tasks: Anatomy

The use of neural networks to directly predict three-dimensional dose
distributions for automatic planning is becoming popular. However, the existing
methods only use patient anatomy as input and assume consistent beam
configuration for all patients in the training database. The purpose of this
work is to develop a more general model that, in addition to patient anatomy,
also considers variable beam configurations, to achieve a more comprehensive
automatic planning with a potentially easier clinical implementation, without
the need of training specific models for different beam settings.
Fast Learning-based Registration of Sparse 3D Clinical Images
Authors: Kathleen M. Lewis, Natalia S. Rost, John Guttag, Adrian V. Dalca
Date: 2018-12-17 00:00:00
Tasks: Image Registration, Registration Of Sparse Clinical Images

We introduce SparseVM, a method that registers clinical-quality 3D MR scans both faster and more accurately than previously possible. Deformable alignment, or registration, of clinical scans is a fundamental task for many clinical neuroscience studies. However, most registration algorithms are designed for high-resolution research-quality scans. In contrast to research-quality scans, clinical scans are often sparse, missing up to 86% of the slices available in research-quality scans. Existing methods for registering these sparse images are either inaccurate or extremely slow. We present a learning-based registration method, SparseVM, that is more accurate and orders of magnitude faster than the most accurate clinical registration methods. To our knowledge, it is the first method to use deep learning specifically tailored to registering clinical images. We demonstrate our method on a clinically-acquired MRI dataset of stroke patients and on a simulated sparse MRI dataset. Our code is available as part of the VoxelMorph package at http://voxelmorph.mit.edu/.
A Globally Optimal Energy-Efficient Power Control Framework and its Efficient Implementation in Wireless Interference Networks
Authors: Bho Matthiesen, Alessio Zappone, Karl-L. Besser, Eduard A. Jorswieck, Merouane Debbah
Date: 2018-12-17 00:00:00
Tasks: global-optimization

This work develops a novel power control framework for energy-efficient power control in wireless networks. The proposed method is a new branch-and-bound procedure based on problem-specific bounds for energy-efficiency maximization that allow for faster convergence. This enables to find the global solution for all of the most common energy-efficient power control problems with a complexity that, although still exponential in the number of variables, is much lower than other available global optimization frameworks. Moreover, the reduced complexity of the proposed framework allows its practical implementation through the use of deep neural networks. Specifically, thanks to its reduced complexity, the proposed method can be used to train an artificial neural network to predict the optimal resource allocation. This is in contrast with other power control methods based on deep learning, which train the neural network based on suboptimal power allocations due to the large complexity that generating large training sets of optimal power allocations would have with available global optimization methods. As a benchmark, we also develop a novel first-order optimal power allocation algorithm. Numerical results show that a neural network can be trained to predict the optimal power allocation policy.
User Association and Load Balancing for Massive MIMO through Deep Learning
Authors: Alessio Zappone, Luca Sanguinetti, Merouane Debbah
Date: 2018-12-17 00:00:00

This work investigates the use of deep learning to perform user cell
association for sum-rate maximization in Massive MIMO networks. It is shown how
a deep neural network can be trained to approach the optimal association rule
with a much more limited computational complexity, thus enabling to update the
association rule in real-time, on the basis of the mobility patterns of users.
In particular, the proposed neural network design requires as input only the
users' geographical positions. Numerical results show that it guarantees the
same performance of traditional optimization-oriented methods.
Towards a Robust Parameterization for Conditioning Facies Models Using Deep Variational Autoencoders and Ensemble Smoother
Authors: Smith W. A. Canchumuni, Alexandre A. Emerick, Marco Aurélio C. Pacheco
Date: 2018-12-17 00:00:00

The literature about history matching is vast and despite the impressive
number of methods proposed and the significant progresses reported in the last
decade, conditioning reservoir models to dynamic data is still a challenging
task. Ensemble-based methods are among the most successful and efficient
techniques currently available for history matching. These methods are usually
able to achieve reasonable data matches, especially if an iterative formulation
is employed. However, they sometimes fail to preserve the geological realism of
the model, which is particularly evident in reservoir with complex facies
distributions. This occurs mainly because of the Gaussian assumptions inherent
in these methods. This fact has encouraged an intense research activity to
develop parameterizations for facies history matching. Despite the large number
of publications, the development of robust parameterizations for facies remains
an open problem.
  Deep learning techniques have been delivering impressive results in a number
of different areas and the first applications in data assimilation in
geoscience have started to appear in literature. The present paper reports the
current results of our investigations on the use of deep neural networks
towards the construction of a continuous parameterization of facies which can
be used for data assimilation with ensemble methods. Specifically, we use a
convolutional variational autoencoder and the ensemble smoother with multiple
data assimilation. We tested the parameterization in three synthetic
history-matching problems with channelized facies. We focus on this type of
facies because they are among the most challenging to preserve after the
assimilation of data. The parameterization showed promising results
outperforming previous methods and generating well-defined channelized facies.
Multi-task learning to improve natural language understanding
Authors: Stefan Constantin, Jan Niehues, Alex Waibel
Date: 2018-12-17 00:00:00
Tasks: Decoder, Multi-Task Learning, Natural Language Understanding

Recently advancements in sequence-to-sequence neural network architectures
have led to an improved natural language understanding. When building a neural
network-based Natural Language Understanding component, one main challenge is
to collect enough training data. The generation of a synthetic dataset is an
inexpensive and quick way to collect data. Since this data often has less
variety than real natural language, neural networks often have problems to
generalize to unseen utterances during testing. In this work, we address this
challenge by using multi-task learning. We train out-of-domain real data
alongside in-domain synthetic data to improve natural language understanding.
We evaluate this approach in the domain of airline travel information with two
synthetic datasets. As out-of-domain real data, we test two datasets based on
the subtitles of movies and series. By using an attention-based encoder-decoder
model, we were able to improve the F1-score over strong baselines from 80.76 %
to 84.98 % in the smaller synthetic dataset.
Learning Common Representation from RGB and Depth Images
Authors: Giorgio Giannone, Boris Chidlovskii
Date: 2018-12-17 00:00:00
Tasks: Decoder, Depth Estimation, Depth Prediction, MULTI-VIEW LEARNING, Segmentation, Semantic Segmentation

We propose a new deep learning architecture for the tasks of semantic
segmentation and depth prediction from RGB-D images. We revise the state of art
based on the RGB and depth feature fusion, where both modalities are assumed to
be available at train and test time. We propose a new architecture where the
feature fusion is replaced with a common deep representation. Combined with an
encoder-decoder type of the network, the architecture can jointly learn models
for semantic segmentation and depth estimation based on their common
representation. This representation, inspired by multi-view learning, offers
several important advantages, such as using one modality available at test time
to reconstruct the missing modality. In the RGB-D case, this enables the
cross-modality scenarios, such as using depth data for semantically
segmentation and the RGB images for depth estimation. We demonstrate the
effectiveness of the proposed network on two publicly available RGB-D datasets.
The experimental results show that the proposed method works well in both
semantic segmentation and depth estimation tasks.
BriarPatches: Pixel-Space Interventions for Inducing Demographic Parity
Authors: Alexey A. Gritsenko, Alex D'Amour, James Atwood, Yoni Halpern, D. Sculley
Date: 2018-12-17 00:00:00

We introduce the BriarPatch, a pixel-space intervention that obscures
sensitive attributes from representations encoded in pre-trained classifiers.
The patches encourage internal model representations not to encode sensitive
information, which has the effect of pushing downstream predictors towards
exhibiting demographic parity with respect to the sensitive information. The
net result is that these BriarPatches provide an intervention mechanism
available at user level, and complements prior research on fair representations
that were previously only applicable by model developers and ML experts.