pwc-archive/papers-with-abstracts huggingface dataset:
Discriminating between Indo-Aryan Languages Using SVM Ensembles
Authors: Alina Maria Ciobanu, Marcos Zampieri, Shervin Malmasi, Santanu Pal, Liviu P. Dinu
Date: 2018-07-09 00:00:00
Proceeding: COLING 2018 8
Tasks: Language Identification

In this paper we present a system based on SVM ensembles trained on
characters and words to discriminate between five similar languages of the
Indo-Aryan family: Hindi, Braj Bhasha, Awadhi, Bhojpuri, and Magahi. We
investigate the performance of individual features and combine the output of
single classifiers to maximize performance. The system competed in the
Indo-Aryan Language Identification (ILI) shared task organized within the
VarDial Evaluation Campaign 2018. Our best entry in the competition, named
ILIdentification, scored 88:95% F1 score and it was ranked 3rd out of 8 teams.
Robust Text-to-SQL Generation with Execution-Guided Decoding
Authors: Chenglong Wang, Kedar Tatwawadi, Marc Brockschmidt, Po-Sen Huang, Yi Mao, Oleksandr Polozov, Rishabh Singh
Date: 2018-07-09 00:00:00
Tasks: Semantic Parsing, Text to SQL, Text-To-SQL

We consider the problem of neural semantic parsing, which translates natural
language questions into executable SQL queries. We introduce a new mechanism,
execution guidance, to leverage the semantics of SQL. It detects and excludes
faulty programs during the decoding procedure by conditioning on the execution
of partially generated program. The mechanism can be used with any
autoregressive generative model, which we demonstrate on four state-of-the-art
recurrent or template-based semantic parsing models. We demonstrate that
execution guidance universally improves model performance on various
text-to-SQL datasets with different scales and query complexity: WikiSQL, ATIS,
and GeoQuery. As a result, we achieve new state-of-the-art execution accuracy
of 83.8% on WikiSQL.
NMT-Keras: a Very Flexible Toolkit with a Focus on Interactive NMT and Online Learning
Authors: Álvaro Peris, Francisco Casacuberta
Date: 2018-07-09 00:00:00
Tasks: General Classification, Machine Translation, NMT, Question Answering, Sentence, Sentence Classification, Translation, Video Captioning, Visual Question Answering, Visual Question Answering (VQA)

We present NMT-Keras, a flexible toolkit for training deep learning models,
which puts a particular emphasis on the development of advanced applications of
neural machine translation systems, such as interactive-predictive translation
protocols and long-term adaptation of the translation system via continuous
learning. NMT-Keras is based on an extended version of the popular Keras
library, and it runs on Theano and Tensorflow. State-of-the-art neural machine
translation models are deployed and used following the high-level framework
provided by Keras. Given its high modularity and flexibility, it also has been
extended to tackle different problems, such as image and video captioning,
sentence classification and visual question answering.
Deep Multimodal Clustering for Unsupervised Audiovisual Learning
Authors: Di Hu, Feiping Nie, Xuelong. Li
Date: 2018-07-09 00:00:00
Proceeding: CVPR 2019 6
Tasks: Clustering

The seen birds twitter, the running cars accompany with noise, etc. These
naturally audiovisual correspondences provide the possibilities to explore and
understand the outside world. However, the mixed multiple objects and sounds
make it intractable to perform efficient matching in the unconstrained
environment. To settle this problem, we propose to adequately excavate audio
and visual components and perform elaborate correspondence learning among them.
Concretely, a novel unsupervised audiovisual learning model is proposed, named
as \Deep Multimodal Clustering (DMC), that synchronously performs sets of
clustering with multimodal vectors of convolutional maps in different shared
spaces for capturing multiple audiovisual correspondences. And such integrated
multimodal clustering network can be effectively trained with max-margin loss
in the end-to-end fashion. Amounts of experiments in feature evaluation and
audiovisual tasks are performed. The results demonstrate that DMC can learn
effective unimodal representation, with which the classifier can even
outperform human performance. Further, DMC shows noticeable performance in
sound localization, multisource detection, and audiovisual understanding.
Computer Assisted Localization of a Heart Arrhythmia
Authors: Chris Vogl, Peng Zheng, Stephen P. Seslar, Aleksandr Y. Aravkin
Date: 2018-07-09 00:00:00
Tasks: Anatomy, Diagnostic

We consider the problem of locating a point-source heart arrhythmia using
data from a standard diagnostic procedure, where a reference catheter is placed
in the heart, and arrival times from a second diagnostic catheter are recorded
as the diagnostic catheter moves around within the heart. We model this
situation as a nonconvex feasibility problem, where given a set of arrival
times, we look for a source location that is consistent with the available
data. We develop a new optimization approach and fast algorithm to obtain
online proposals for the next location to suggest to the operator as she
collects data. We validate the procedure using a Monte Carlo simulation based
on patients' electrophysiological data. The proposed procedure robustly and
quickly locates the source of arrhythmias without any prior knowledge of heart
anatomy.
Video Summarisation by Classification with Deep Reinforcement Learning
Authors: Kaiyang Zhou, Tao Xiang, Andrea Cavallaro
Date: 2018-07-09 00:00:00
Tasks: Classification, Decision Making, Deep Reinforcement Learning, General Classification, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

Most existing video summarisation methods are based on either supervised or
unsupervised learning. In this paper, we propose a reinforcement learning-based
weakly supervised method that exploits easy-to-obtain, video-level category
labels and encourages summaries to contain category-related information and
maintain category recognisability. Specifically, We formulate video
summarisation as a sequential decision-making process and train a summarisation
network with deep Q-learning (DQSN). A companion classification network is also
trained to provide rewards for training the DQSN. With the classification
network, we develop a global recognisability reward based on the classification
result. Critically, a novel dense ranking-based reward is also proposed in
order to cope with the temporally delayed and sparse reward problems for long
sequence reinforcement learning. Extensive experiments on two benchmark
datasets show that the proposed approach achieves state-of-the-art performance.
Evaluating Active Learning Heuristics for Sequential Diagnosis
Authors: Patrick Rodler, Wolfgang Schmid
Date: 2018-07-09 00:00:00
Tasks: Active Learning, Sequential Diagnosis

Given a malfunctioning system, sequential diagnosis aims at identifying the root cause of the failure in terms of abnormally behaving system components. As initial system observations usually do not suffice to deterministically pin down just one explanation of the system's misbehavior, additional system measurements can help to differentiate between possible explanations. The goal is to restrict the space of explanations until there is only one (highly probable) explanation left. To achieve this with a minimal-cost set of measurements, various (active learning) heuristics for selecting the best next measurement have been proposed. We report preliminary results of extensive ongoing experiments with a set of selection heuristics on real-world diagnosis cases. In particular, we try to answer questions such as "Is some heuristic always superior to all others?", "On which factors does the (relative) performance of the particular heuristics depend?" or "Under which circumstances should I use which heuristic?"
Temporal Difference Learning with Neural Networks - Study of the Leakage Propagation Problem
Authors: Hugo Penedones, Damien Vincent, Hartmut Maennel, Sylvain Gelly, Timothy Mann, Andre Barreto
Date: 2018-07-09 00:00:00

Temporal-Difference learning (TD) [Sutton, 1988] with function approximation
can converge to solutions that are worse than those obtained by Monte-Carlo
regression, even in the simple case of on-policy evaluation. To increase our
understanding of the problem, we investigate the issue of approximation errors
in areas of sharp discontinuities of the value function being further
propagated by bootstrap updates. We show empirical evidence of this leakage
propagation, and show analytically that it must occur, in a simple Markov
chain, when function approximation errors are present. For reversible policies,
the result can be interpreted as the tension between two terms of the loss
function that TD minimises, as recently described by [Ollivier, 2018]. We show
that the upper bounds from [Tsitsiklis and Van Roy, 1997] hold, but they do not
imply that leakage propagation occurs and under what conditions. Finally, we
test whether the problem could be mitigated with a better state representation,
and whether it can be learned in an unsupervised manner, without rewards or
privileged information.
ChestNet: A Deep Neural Network for Classification of Thoracic Diseases on Chest Radiography
Authors: Hongyu Wang, Yong Xia
Date: 2018-07-09 00:00:00
Tasks: General Classification, Weakly-supervised Learning

Computer-aided techniques may lead to more accurate and more acces-sible
diagnosis of thorax diseases on chest radiography. Despite the success of deep
learning-based solutions, this task remains a major challenge in smart
healthcare, since it is intrinsically a weakly supervised learning problem. In
this paper, we incorporate the attention mechanism into a deep convolutional
neural network, and thus propose the ChestNet model to address effective
diagnosis of thorax diseases on chest radiography. This model consists of two
branches: a classification branch serves as a uniform feature
extraction-classification network to free users from troublesome handcrafted
feature extraction, and an attention branch exploits the correlation between
class labels and the locations of patholog-ical abnormalities and allows the
model to concentrate adaptively on the patholog-ically abnormal regions. We
evaluated our model against three state-of-the-art deep learning models on the
Chest X-ray 14 dataset using the official patient-wise split. The results
indicate that our model outperforms other methods, which use no extra training
data, in diagnosing 14 thorax diseases on chest radiography.
Deriving Neural Network Architectures using Precision Learning: Parallel-to-fan beam Conversion
Authors: Christopher Syben, Bernhard Stimpel, Jonathan Lommen, Tobias Würfl, Arnd Dörfler, Andreas Maier
Date: 2018-07-09 00:00:00

In this paper, we derive a neural network architecture based on an analytical
formulation of the parallel-to-fan beam conversion problem following the
concept of precision learning. The network allows to learn the unknown
operators in this conversion in a data-driven manner avoiding interpolation and
potential loss of resolution. Integration of known operators results in a small
number of trainable parameters that can be estimated from synthetic data only.
The concept is evaluated in the context of Hybrid MRI/X-ray imaging where
transformation of the parallel-beam MRI projections to fan-beam X-ray
projections is required. The proposed method is compared to a traditional
rebinning method. The results demonstrate that the proposed method is superior
to ray-by-ray interpolation and is able to deliver sharper images using the
same amount of parallel-beam input projections which is crucial for
interventional applications. We believe that this approach forms a basis for
further work uniting deep learning, signal processing, physics, and traditional
pattern recognition.
A deep learning approach for understanding natural language commands for mobile service robots
Authors: Pedro Henrique Martins, Luís Custódio, Rodrigo Ventura
Date: 2018-07-09 00:00:00
Tasks: Action Detection, Intent Detection, Natural Language Understanding, slot-filling, Slot Filling

Using natural language to give instructions to robots is challenging, since
natural language understanding is still largely an open problem. In this paper
we address this problem by restricting our attention to commands modeled as one
action, plus arguments (also known as slots). For action detection (also called
intent detection) and slot filling various architectures of Recurrent Neural
Networks and Long Short Term Memory (LSTM) networks were evaluated, having
LSTMs achieved a superior accuracy. As the action requested may not fall within
the robots capabilities, a Support Vector Machine(SVM) is used to determine
whether it is or not. For the input of the neural networks, several word
embedding algorithms were compared. Finally, to implement the system in a
robot, a ROS package is created using a SMACH state machine. The proposed
system is then evaluated both using well-known datasets and benchmarks in the
context of domestic service robots.
Position-aware Self-attention with Relative Positional Encodings for Slot Filling
Authors: Ivan Bilan, Benjamin Roth
Date: 2018-07-09 00:00:00
Tasks: Position, Relation Extraction, Sentence, slot-filling, Slot Filling

This paper describes how to apply self-attention with relative positional
encodings to the task of relation extraction. We propose to use the
self-attention encoder layer together with an additional position-aware
attention layer that takes into account positions of the query and the object
in the sentence. The self-attention encoder also uses a custom implementation
of relative positional encodings which allow each word in the sentence to take
into account its left and right context. The evaluation of the model is done on
the TACRED dataset. The proposed model relies only on attention (no recurrent
or convolutional layers are used), while improving performance w.r.t. the
previous state of the art.
Deep Learning for Singing Processing: Achievements, Challenges and Impact on Singers and Listeners
Authors: Emilia Gómez, Merlijn Blaauw, Jordi Bonada, Pritish Chandna, Helena Cuesta
Date: 2018-07-09 00:00:00

This paper summarizes some recent advances on a set of tasks related to the
processing of singing using state-of-the-art deep learning techniques. We
discuss their achievements in terms of accuracy and sound quality, and the
current challenges, such as availability of data and computing resources. We
also discuss the impact that these advances do and will have on listeners and
singers when they are integrated in commercial applications.
Convolutional Recurrent Neural Networks for Glucose Prediction
Authors: Kezhi Li, John Daniels, Chengyuan Liu, Pau Herrero, Pantelis Georgiou
Date: 2018-07-09 00:00:00
Tasks: Management, Prediction

Control of blood glucose is essential for diabetes management. Current
digital therapeutic approaches for subjects with Type 1 diabetes mellitus
(T1DM) such as the artificial pancreas and insulin bolus calculators leverage
machine learning techniques for predicting subcutaneous glucose for improved
control. Deep learning has recently been applied in healthcare and medical
research to achieve state-of-the-art results in a range of tasks including
disease diagnosis, and patient state prediction among others. In this work, we
present a deep learning model that is capable of forecasting glucose levels
with leading accuracy for simulated patient cases (RMSE = 9.38$\pm$0.71 [mg/dL]
over a 30-minute horizon, RMSE = 18.87$\pm$2.25 [mg/dL] over a 60-minute
horizon) and real patient cases (RMSE = 21.07$\pm$2.35 [mg/dL] for 30-minute,
RMSE = 33.27$\pm$4.79\% for 60-minute). In addition, the model provides
competitive performance in providing effective prediction horizon ($PH_{eff}$)
with minimal time lag both in a simulated patient dataset ($PH_{eff}$ =
29.0$\pm$0.7 for 30-min and $PH_{eff}$ = 49.8$\pm$2.9 for 60-min) and in a real
patient dataset ($PH_{eff}$ = 19.3$\pm$3.1 for 30-min and $PH_{eff}$ =
29.3$\pm$9.4 for 60-min). This approach is evaluated on a dataset of 10
simulated cases generated from the UVa/Padova simulator and a clinical dataset
of 10 real cases each containing glucose readings, insulin bolus, and meal
(carbohydrate) data. Performance of the recurrent convolutional neural network
is benchmarked against four algorithms. The proposed algorithm is implemented
on an Android mobile phone, with an execution time of $6$ms on a phone compared
to an execution time of $780$ms on a laptop.
Glow: Generative Flow with Invertible 1x1 Convolutions
Authors: Diederik P. Kingma, Prafulla Dhariwal
Date: 2018-07-09 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Density Estimation, Image Generation, Unsupervised Anomaly Detection

Flow-based generative models (Dinh et al., 2014) are conceptually attractive
due to tractability of the exact log-likelihood, tractability of exact
latent-variable inference, and parallelizability of both training and
synthesis. In this paper we propose Glow, a simple type of generative flow
using an invertible 1x1 convolution. Using our method we demonstrate a
significant improvement in log-likelihood on standard benchmarks. Perhaps most
strikingly, we demonstrate that a generative model optimized towards the plain
log-likelihood objective is capable of efficient realistic-looking synthesis
and manipulation of large images. The code for our model is available at
https://github.com/openai/glow
Image Restoration Using Conditional Random Fields and Scale Mixtures of Gaussians
Authors: Milad Niknejad, Jose M. Bioucas-Dias, Mario A. T. Figueiredo
Date: 2018-07-09 00:00:00
Tasks: Denoising, Image Denoising, Image Restoration

This paper proposes a general framework for internal patch-based image
restoration based on Conditional Random Fields (CRF). Unlike related models
based on Markov Random Fields (MRF), our approach explicitly formulates the
posterior distribution for the entire image. The potential functions are taken
as proportional to the product of a likelihood and prior for each patch. By
assuming identical parameters for similar patches, our approach can be
classified as a model-based non-local method. For the prior term in the
potential function of the CRF model, multivariate Gaussians and multivariate
scale-mixture of Gaussians are considered, with the latter being a novel prior
for image patches. Our results show that the proposed approach outperforms
methods based on Gaussian mixture models for image denoising and
state-of-the-art methods for image interpolation/inpainting.
Pioneer Networks: Progressively Growing Generative Autoencoder
Authors: Ari Heljakka, Arno Solin, Juho Kannala
Date: 2018-07-09 00:00:00
Tasks: Image Generation

We introduce a novel generative autoencoder network model that learns to
encode and reconstruct images with high quality and resolution, and supports
smooth random sampling from the latent space of the encoder. Generative
adversarial networks (GANs) are known for their ability to simulate random
high-quality images, but they cannot reconstruct existing images. Previous
works have attempted to extend GANs to support such inference but, so far, have
not delivered satisfactory high-quality results. Instead, we propose the
Progressively Growing Generative Autoencoder (PIONEER) network which achieves
high-quality reconstruction with $128{\times}128$ images without requiring a
GAN discriminator. We merge recent techniques for progressively building up the
parts of the network with the recently introduced adversarial encoder-generator
network. The ability to reconstruct input images is crucial in many real-world
applications, and allows for precise intelligent manipulation of existing
images. We show promising results in image synthesis and inference, with
state-of-the-art results in CelebA inference tasks.
Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders
Authors: Patrick Forré, Joris M. Mooij
Date: 2018-07-09 00:00:00
Tasks: Causal Discovery

We address the problem of causal discovery from data, making use of the
recently proposed causal modeling framework of modular structural causal models
(mSCM) to handle cycles, latent confounders and non-linearities. We introduce
{\sigma}-connection graphs ({\sigma}-CG), a new class of mixed graphs
(containing undirected, bidirected and directed edges) with additional
structure, and extend the concept of {\sigma}-separation, the appropriate
generalization of the well-known notion of d-separation in this setting, to
apply to {\sigma}-CGs. We prove the closedness of {\sigma}-separation under
marginalisation and conditioning and exploit this to implement a test of
{\sigma}-separation on a {\sigma}-CG. This then leads us to the first causal
discovery algorithm that can handle non-linear functional relations, latent
confounders, cyclic causal relationships, and data from different (stochastic)
perfect interventions. As a proof of concept, we show on synthetic data how
well the algorithm recovers features of the causal graph of modular structural
causal models.
Verisimilar Image Synthesis for Accurate Detection and Recognition of Texts in Scenes
Authors: Fangneng Zhan, Shijian Lu, Chuhui Xue
Date: 2018-07-09 00:00:00
Proceeding: ECCV 2018 9
Tasks: Image Generation, Scene Text Detection, Semantic Segmentation, Text Detection

The requirement of large amounts of annotated images has become one grand
challenge while training deep neural network models for various visual
detection and recognition tasks. This paper presents a novel image synthesis
technique that aims to generate a large amount of annotated scene text images
for training accurate and robust scene text detection and recognition models.
The proposed technique consists of three innovative designs. First, it realizes
"semantic coherent" synthesis by embedding texts at semantically sensible
regions within the background image, where the semantic coherence is achieved
by leveraging the semantic annotations of objects and image regions that have
been created in the prior semantic segmentation research. Second, it exploits
visual saliency to determine the embedding locations within each semantic
sensible region, which coincides with the fact that texts are often placed
around homogeneous regions for better visibility in scenes. Third, it designs
an adaptive text appearance model that determines the color and brightness of
embedded texts by learning from the feature of real scene text images
adaptively. The proposed technique has been evaluated over five public datasets
and the experiments show its superior performance in training accurate and
robust scene text detection and recognition models.
External Patch-Based Image Restoration Using Importance Sampling
Authors: Milad Niknejad, Jose M. Bioucas-Dias, Mario A. T. Figueiredo
Date: 2018-07-09 00:00:00
Tasks: Image Restoration

This paper introduces a new approach to patch-based image restoration based
on external datasets and importance sampling. The Minimum Mean Squared Error
(MMSE) estimate of the image patches, the computation of which requires solving
a multidimensional (typically intractable) integral, is approximated using
samples from an external dataset. The new method, which can be interpreted as a
generalization of the external non-local means (NLM), uses self-normalized
importance sampling to efficiently approximate the MMSE estimates. The use of
self-normalized importance sampling endows the proposed method with great
flexibility, namely regarding the statistical properties of the measurement
noise. The effectiveness of the proposed method is shown in a series of
experiments using both generic large-scale and class-specific external
datasets.