pwc-archive/papers-with-abstracts huggingface dataset:
Large-Scale Visual Speech Recognition
Authors: Brendan Shillingford, Yannis Assael, Matthew W. Hoffman, Thomas Paine, Cían Hughes, Utsav Prabhu, Hank Liao, Hasim Sak, Kanishka Rao, Lorrayne Bennett, Marie Mulville, Ben Coppin, Ben Laurie, Andrew Senior, Nando de Freitas
Date: 2018-07-13 00:00:00
Proceeding: ICLR 2019 5
Tasks: Decoder, Lipreading, speech-recognition, Speech Recognition, Visual Speech Recognition

This work presents a scalable solution to open-vocabulary visual speech
recognition. To achieve this, we constructed the largest existing visual speech
recognition dataset, consisting of pairs of text and video clips of faces
speaking (3,886 hours of video). In tandem, we designed and trained an
integrated lipreading system, consisting of a video processing pipeline that
maps raw video to stable videos of lips and sequences of phonemes, a scalable
deep neural network that maps the lip videos to sequences of phoneme
distributions, and a production-level speech decoder that outputs sequences of
words. The proposed system achieves a word error rate (WER) of 40.9% as
measured on a held-out set. In comparison, professional lipreaders achieve
either 86.4% or 92.9% WER on the same dataset when having access to additional
types of contextual information. Our approach significantly improves on other
lipreading approaches, including variants of LipNet and of Watch, Attend, and
Spell (WAS), which are only capable of 89.8% and 76.8% WER respectively.
Deep Enhanced Representation for Implicit Discourse Relation Recognition
Authors: Hongxiao Bai, Hai Zhao
Date: 2018-07-13 00:00:00
Proceeding: COLING 2018 8
Tasks: Discourse Parsing, Relation, Relation Prediction, Sentence

Implicit discourse relation recognition is a challenging task as the relation
prediction without explicit connectives in discourse parsing needs
understanding of text spans and cannot be easily derived from surface features
from the input sentence pairs. Thus, properly representing the text is very
crucial to this task. In this paper, we propose a model augmented with
different grained text representations, including character, subword, word,
sentence, and sentence pair levels. The proposed deeper model is evaluated on
the benchmark treebank and achieves state-of-the-art accuracy with greater than
48% in 11-way and $F_1$ score greater than 50% in 4-way classifications for the
first time according to our best knowledge.
Multi-Scale Convolutional-Stack Aggregation for Robust White Matter Hyperintensities Segmentation
Authors: Hongwei Li, Jian-Guo Zhang, Mark Muehlau, Jan Kirschke, Bjoern Menze
Date: 2018-07-13 00:00:00
Tasks: Lesion Segmentation

Segmentation of both large and small white matter hyperintensities/lesions in
brain MR images is a challenging task which has drawn much attention in recent
years. We propose a multi-scale aggregation model framework to deal with
volume-varied lesions. Firstly, we present a specifically-designed network for
small lesion segmentation called Stack-Net, in which multiple convolutional
layers are connected, aiming to preserve rich local spatial information of
small lesions before the sub-sampling layer. Secondly, we aggregate multi-scale
Stack-Nets with different receptive fields to learn multi-scale contextual
information of both large and small lesions. Our model is evaluated on recent
MICCAI WMH Challenge Dataset and outperforms the state-of-the-art on lesion
recall and lesion F1-score under 5-fold cross validation. In addition, we
further test our pre-trained models on a Multiple Sclerosis lesion dataset with
30 subjects under cross-center evaluation. Results show that the aggregation
model is effective in learning multi-scale spatial information.It claimed the
first place on the hidden test set after independent evaluation by the
challenge organizer. In addition, we further test our pre-trained models on a
Multiple Sclerosis lesion dataset with 30 subjects under cross-center
evaluation. Results show that the aggregation model is effective in learning
multi-scale spatial information.
New/s/leak 2.0 - Multilingual Information Extraction and Visualization for Investigative Journalism
Authors: Gregor Wiedemann, Seid Muhie Yimam, Chris Biemann
Date: 2018-07-13 00:00:00
Tasks: Efficient Exploration

Investigative journalism in recent years is confronted with two major
challenges: 1) vast amounts of unstructured data originating from large text
collections such as leaks or answers to Freedom of Information requests, and 2)
multi-lingual data due to intensified global cooperation and communication in
politics, business and civil society. Faced with these challenges, journalists
are increasingly cooperating in international networks. To support such
collaborations, we present the new version of new/s/leak 2.0, our open-source
software for content-based searching of leaks. It includes three novel main
features: 1) automatic language detection and language-dependent information
extraction for 40 languages, 2) entity and keyword visualization for efficient
exploration, and 3) decentral deployment for analysis of confidential data from
various formats. We illustrate the new analysis capabilities with an exemplary
case study.
Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking
Authors: Shikhar Murty*, Patrick Verga*, Luke Vilnis, Irena Radovanovic, Andrew McCallum
Date: 2018-07-13 00:00:00
Proceeding: ACL 2018 7
Tasks: 2k, Entity Linking, Entity Typing

Extraction from raw text to a knowledge base of entities and fine-grained
types is often cast as prediction into a flat set of entity and type labels,
neglecting the rich hierarchies over types and entities contained in curated
ontologies. Previous attempts to incorporate hierarchical structure have
yielded little benefit and are restricted to shallow ontologies. This paper
presents new methods using real and complex bilinear mappings for integrating
hierarchical information, yielding substantial improvement over flat
predictions in entity linking and fine-grained entity typing, and achieving new
state-of-the-art results for end-to-end models on the benchmark FIGER dataset.
We also present two new human-annotated datasets containing wide and deep
hierarchies which we will release to the community to encourage further
research in this direction: MedMentions, a collection of PubMed abstracts in
which 246k mentions have been mapped to the massive UMLS ontology; and TypeNet,
which aligns Freebase types with the WordNet hierarchy to obtain nearly 2k
entity types. In experiments on all three datasets we show substantial gains
from hierarchy-aware training.
Learning-based Natural Geometric Matching with Homography Prior
Authors: Yifang Xu, Tianli Liao, Jing Chen
Date: 2018-07-13 00:00:00
Tasks: Geometric Matching

Geometric matching is a key step in computer vision tasks. Previous
learning-based methods for geometric matching concentrate more on improving
alignment quality, while we argue the importance of naturalness issue
simultaneously. To deal with this, firstly, Pearson correlation is applied to
handle large intra-class variations of features in feature matching stage.
Then, we parametrize homography transformation with 9 parameters in full
connected layer of our network, to better characterize large viewpoint
variations compared with affine transformation. Furthermore, a novel loss
function with Gaussian weights guarantees the model accuracy and efficiency in
training procedure. Finally, we provide two choices for different purposes in
geometric matching. When compositing homography with affine transformation, the
alignment accuracy improves and all lines are preserved, which results in a
more natural transformed image. When compositing homography with non-rigid
thin-plate-spline transformation, the alignment accuracy further improves.
Experimental results on Proposal Flow dataset show that our method outperforms
state-of-the-art methods, both in terms of alignment accuracy and naturalness.
Tune: A Research Platform for Distributed Model Selection and Training
Authors: Richard Liaw, Eric Liang, Robert Nishihara, Philipp Moritz, Joseph E. Gonzalez, Ion Stoica
Date: 2018-07-13 00:00:00
Tasks: Hyperparameter Optimization, Model Selection

Modern machine learning algorithms are increasingly computationally
demanding, requiring specialized hardware and distributed computation to
achieve high performance in a reasonable time frame. Many hyperparameter search
algorithms have been proposed for improving the efficiency of model selection,
however their adaptation to the distributed compute environment is often
ad-hoc. We propose Tune, a unified framework for model selection and training
that provides a narrow-waist interface between training scripts and search
algorithms. We show that this interface meets the requirements for a broad
range of hyperparameter search algorithms, allows straightforward scaling of
search to large clusters, and simplifies algorithm implementation. We
demonstrate the implementation of several state-of-the-art hyperparameter
search algorithms in Tune. Tune is available at
http://ray.readthedocs.io/en/latest/tune.html.
Newton-Krylov PDE-constrained LDDMM in the space of band-limited vector fields
Authors: Monica Hernandez
Date: 2018-07-13 00:00:00
Tasks: Anatomy

PDE-constrained Large Deformation Diffeomorphic Metric Mapping is a
particularly interesting framework of physically meaningful diffeomorphic
registration methods. Newton-Krylov optimization has shown an excellent
numerical accuracy and an extraordinarily fast convergence rate in this
framework. However, the most significant limitation of PDE-constrained LDDMM is
the huge computational complexity, that hinders the extensive use in
Computational Anatomy applications. In this work, we propose two
PDE-constrained LDDMM methods parameterized in the space of band-limited vector
fields and we evaluate their performance with respect to the most related state
of the art methods. The parameterization in the space of band-limited vector
fields dramatically alleviates the computational burden avoiding the
computation of the high-frequency components of the velocity fields that would
be suppressed by the action of the low-pass filters involved in the computation
of the gradient and the Hessian-vector products. Besides, the proposed methods
have shown an improved accuracy with respect to the benchmark methods.
Global optimization test problems based on random field composition
Authors: Ramses Sala, Niccolò Baldanzini, Marco Pierini
Date: 2018-07-13 00:00:00
Tasks: global-optimization, Metaheuristic Optimization

The development and identification of effective optimization algorithms for
non-convex real-world problems is a challenge in global optimization. Because
theoretical performance analysis is difficult, and problems based on models of
real-world systems are often computationally expensive, several artificial
performance test problems and test function generators have been proposed for
empirical comparative assessment and analysis of metaheuristic optimization
algorithms. These test problems however often lack the complex function
structures and forthcoming difficulties that can appear in real-world problems.
This communication presents a method to systematically build test problems with
various types and degrees of difficulty. By weighted composition of
parameterized random fields, challenging test functions with tunable function
features such as, variance contribution distribution, interaction order, and
nonlinearity can be constructed. The method is described, and its applicability
to optimization performance analysis is described by means of a few basic
examples. The method aims to set a step forward in the systematic generation of
global optimization test problems, which could lead to a better understanding
of the performance of optimization algorithms on problem types with particular
characteristics. On request an introductive MATLAB implementation of a test
function generator based on the presented method is available.
Learning Graph Representations by Dendrograms
Authors: Thomas Bonald, Bertrand Charpentier
Date: 2018-07-13 00:00:00
Tasks: Clustering, Graph Clustering

Hierarchical graph clustering is a common technique to reveal the multi-scale
structure of complex networks. We propose a novel metric for assessing the
quality of a hierarchical clustering. This metric reflects the ability to
reconstruct the graph from the dendrogram, which encodes the hierarchy. The
optimal representation of the graph defines a class of reducible linkages
leading to regular dendrograms by greedy agglomerative clustering.
CascadeCNN: Pushing the Performance Limits of Quantisation in Convolutional Neural Networks
Authors: Alexandros Kouris, Stylianos I. Venieris, Christos-Savvas Bouganis
Date: 2018-07-13 00:00:00

This work presents CascadeCNN, an automated toolflow that pushes the
quantisation limits of any given CNN model, aiming to perform high-throughput
inference. A two-stage architecture tailored for any given CNN-FPGA pair is
generated, consisting of a low- and high-precision unit in a cascade. A
confidence evaluation unit is employed to identify misclassified cases from the
excessively low-precision unit and forward them to the high-precision unit for
re-processing. Experiments demonstrate that the proposed toolflow can achieve a
performance boost up to 55% for VGG-16 and 48% for AlexNet over the baseline
design for the same resource budget and accuracy, without the need of
retraining the model or accessing the training data.
A comprehensive study of sparse representation techniques for offline signature verification
Authors: Elias N. Zois, Dimitrios Tsourounis, Ilias Theodorakopoulos, Anastasios Kesidis, George Economou
Date: 2018-07-13 00:00:00

In this work, a feature extraction method for offline signature verification
is presented that harnesses the power of sparse representation in order to
deliver state-of-the-art verification performance in several signature datasets
like CEDAR, MCYT-75, GPDS and UTSIG. Beyond the accuracy improvements, several
major parameters associated with sparse representation; such as selected
configuration, dictionary size, sparsity level and positivity priors are
investigated. Besides, it is evinced that 2nd order statistics of the sparse
codes is a powerful pooling function for the formation of the global signature
descriptor. Also, a thorough evaluation of the effects of preprocessing is
introduced by an automated algorithm in order to select the optimum thinning
level. Finally, a segmentation strategy which employs a special form of spatial
pyramid tailored to the problem of sparse representation is presented along
with the enhancing of the produced descriptor on meaningful areas of the
signature as emerged from the BRISK key-point detection mechanism. The obtained
state-of-the-art results on the most challenging signature datasets provide a
strong indication towards the benefits of learned features, even in writer
dependent (WD) scenarios with a unique model for each writer and only a few
available reference samples of him/her.
Exploring Hierarchy-Aware Inverse Reinforcement Learning
Authors: Chris Cundy, Daniel Filan
Date: 2018-07-13 00:00:00
Tasks: BIRL, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We introduce a new generative model for human planning under the Bayesian
Inverse Reinforcement Learning (BIRL) framework which takes into account the
fact that humans often plan using hierarchical strategies. We describe the
Bayesian Inverse Hierarchical RL (BIHRL) algorithm for inferring the values of
hierarchical planners, and use an illustrative toy model to show that BIHRL
retains accuracy where standard BIRL fails. Furthermore, BIHRL is able to
accurately predict the goals of `Wikispeedia' game players, with inclusion of
hierarchical structure in the model resulting in a large boost in accuracy. We
show that BIHRL is able to significantly outperform BIRL even when we only have
a weak prior on the hierarchical structure of the plans available to the agent,
and discuss the significant challenges that remain for scaling up this
framework to more realistic settings.
On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length
Authors: Stanisław Jastrzębski, Zachary Kenton, Nicolas Ballas, Asja Fischer, Yoshua Bengio, Amos Storkey
Date: 2018-07-13 00:00:00
Proceeding: ICLR 2019 5
Tasks: Relation

Stochastic Gradient Descent (SGD) based training of neural networks with a large learning rate or a small batch-size typically ends in well-generalizing, flat regions of the weight space, as indicated by small eigenvalues of the Hessian of the training loss. However, the curvature along the SGD trajectory is poorly understood. An empirical investigation shows that initially SGD visits increasingly sharp regions, reaching a maximum sharpness determined by both the learning rate and the batch-size of SGD. When studying the SGD dynamics in relation to the sharpest directions in this initial phase, we find that the SGD step is large compared to the curvature and commonly fails to minimize the loss along the sharpest directions. Furthermore, using a reduced learning rate along these directions can improve training speed while leading to both sharper and better generalizing solutions compared to vanilla SGD. In summary, our analysis of the dynamics of SGD in the subspace of the sharpest directions shows that they influence the regions that SGD steers to (where larger learning rate or smaller batch size result in wider regions visited), the overall training speed, and the generalization ability of the final model.
Are generative deep models for novelty detection truly better?
Authors: Vít Škvára, Tomáš Pevný, Václav Šmídl
Date: 2018-07-13 00:00:00
Tasks: Anomaly Detection, Novelty Detection

Many deep models have been recently proposed for anomaly detection. This
paper presents comparison of selected generative deep models and classical
anomaly detection methods on an extensive number of non--image benchmark
datasets. We provide statistical comparison of the selected models, in many
configurations, architectures and hyperparamaters. We arrive to conclusion that
performance of the generative models is determined by the process of selection
of their hyperparameters. Specifically, performance of the deep generative
models deteriorates with decreasing amount of anomalous samples used in
hyperparameter selection. In practical scenarios of anomaly detection, none of
the deep generative models systematically outperforms the kNN.
Multi-task dialog act and sentiment recognition on Mastodon
Authors: Christophe Cerisara, Somayeh Jafaritazehjani, Adedayo Oluokun, Hoa Le
Date: 2018-07-13 00:00:00
Proceeding: COLING 2018 8
Tasks: Transfer Learning

Because of license restrictions, it often becomes impossible to strictly
reproduce most research results on Twitter data already a few months after the
creation of the corpus. This situation worsened gradually as time passes and
tweets become inaccessible. This is a critical issue for reproducible and
accountable research on social media. We partly solve this challenge by
annotating a new Twitter-like corpus from an alternative large social medium
with licenses that are compatible with reproducible experiments: Mastodon. We
manually annotate both dialogues and sentiments on this corpus, and train a
multi-task hierarchical recurrent network on joint sentiment and dialog act
recognition. We experimentally demonstrate that transfer learning may be
efficiently achieved between both tasks, and further analyze some specific
correlations between sentiments and dialogues on social media. Both the
annotated corpus and deep network are released with an open-source license.
A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification
Authors: Zeyang Lei, Yujiu Yang, Min Yang, Yi Liu
Date: 2018-07-13 00:00:00
Proceeding: ACL 2018 7
Tasks: Classification, General Classification, Negation, Sentiment Analysis, Sentiment Classification

Deep learning approaches for sentiment classification do not fully exploit
sentiment linguistic knowledge. In this paper, we propose a
Multi-sentiment-resource Enhanced Attention Network (MEAN) to alleviate the
problem by integrating three kinds of sentiment linguistic knowledge (e.g.,
sentiment lexicon, negation words, intensity words) into the deep neural
network via attention mechanisms. By using various types of sentiment
resources, MEAN utilizes sentiment-relevant information from different
representation subspaces, which makes it more effective to capture the overall
semantics of the sentiment, negation and intensity words for sentiment
prediction. The experimental results demonstrate that MEAN has robust
superiority over strong competitors.
Zoom-Net: Mining Deep Feature Interactions for Visual Relationship Recognition
Authors: Guojun Yin, Lu Sheng, Bin Liu, Nenghai Yu, Xiaogang Wang, Jing Shao, Chen Change Loy
Date: 2018-07-13 00:00:00
Proceeding: ECCV 2018 9
Tasks: Object

Recognizing visual relationships <subject-predicate-object> among any pair of
localized objects is pivotal for image understanding. Previous studies have
shown remarkable progress in exploiting linguistic priors or external textual
information to improve the performance. In this work, we investigate an
orthogonal perspective based on feature interactions. We show that by
encouraging deep message propagation and interactions between local object
features and global predicate features, one can achieve compelling performance
in recognizing complex relationships without using any linguistic priors. To
this end, we present two new pooling cells to encourage feature interactions:
(i) Contrastive ROI Pooling Cell, which has a unique deROI pooling that
inversely pools local object features to the corresponding area of global
predicate features. (ii) Pyramid ROI Pooling Cell, which broadcasts global
predicate features to reinforce local object features.The two cells constitute
a Spatiality-Context-Appearance Module (SCA-M), which can be further stacked
consecutively to form our final Zoom-Net.We further shed light on how one could
resolve ambiguous and noisy object and predicate annotations by
Intra-Hierarchical trees (IH-tree). Extensive experiments conducted on Visual
Genome dataset demonstrate the effectiveness of our feature-oriented approach
compared to state-of-the-art methods (Acc@1 11.42% from 8.16%) that depend on
explicit modeling of linguistic interactions. We further show that SCA-M can be
incorporated seamlessly into existing approaches to improve the performance by
a large margin. The source code will be released on
https://github.com/gjyin91/ZoomNet.
Hybrid CTC-Attention based End-to-End Speech Recognition using Subword Units
Authors: Zhangyu Xiao, Zhijian Ou, Wei Chu, Hui Lin
Date: 2018-07-13 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, speech-recognition, Speech Recognition

In this paper, we present an end-to-end automatic speech recognition system,
which successfully employs subword units in a hybrid CTC-Attention based
system. The subword units are obtained by the byte-pair encoding (BPE)
compression algorithm. Compared to using words as modeling units, using
characters or subword units does not suffer from the out-of-vocabulary (OOV)
problem. Furthermore, using subword units further offers a capability in
modeling longer context than using characters. We evaluate different systems
over the LibriSpeech 1000h dataset. The subword-based hybrid CTC-Attention
system obtains 6.8% word error rate (WER) on the test_clean subset without any
dictionary or external language model. This represents a significant
improvement (a 12.8% WER relative reduction) over the character-based hybrid
CTC-Attention system.