pwc-archive/papers-with-abstracts huggingface dataset:
Characterizing classification datasets: a study of meta-features for meta-learning
Authors: Adriano Rivolli, Luís P. F. Garcia, Carlos Soares, Joaquin Vanschoren, André C. P. L. F. de Carvalho
Date: 2018-08-30 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Meta-Learning

Meta-learning is increasingly used to support the recommendation of machine learning algorithms and their configurations. Such recommendations are made based on meta-data, consisting of performance evaluations of algorithms on prior datasets, as well as characterizations of these datasets. These characterizations, also called meta-features, describe properties of the data which are predictive for the performance of machine learning algorithms trained on them. Unfortunately, despite being used in a large number of studies, meta-features are not uniformly described, organized and computed, making many empirical studies irreproducible and hard to compare. This paper aims to deal with this by systematizing and standardizing data characterization measures for classification datasets used in meta-learning. Moreover, it presents MFE, a new tool for extracting meta-features from datasets and identifying more subtle reproducibility issues in the literature, proposing guidelines for data characterization that strengthen reproducible empirical research in meta-learning.
Modeling Empathy and Distress in Reaction to News Stories
Authors: Sven Buechel, Anneke Buffone, Barry Slaff, Lyle Ungar, João Sedoc
Date: 2018-08-30 00:00:00
Proceeding: EMNLP 2018 10

Computational detection and understanding of empathy is an important factor
in advancing human-computer interaction. Yet to date, text-based empathy
prediction has the following major limitations: It underestimates the
psychological complexity of the phenomenon, adheres to a weak notion of ground
truth where empathic states are ascribed by third parties, and lacks a shared
corpus. In contrast, this contribution presents the first publicly available
gold standard for empathy prediction. It is constructed using a novel
annotation methodology which reliably captures empathy assessments by the
writer of a statement using multi-item scales. This is also the first
computational work distinguishing between multiple forms of empathy, empathic
concern, and personal distress, as recognized throughout psychology. Finally,
we present experimental results for three different predictive models, of which
a CNN performs the best.
A Unified Analysis of Stochastic Momentum Methods for Deep Learning
Authors: Yan Yan, Tianbao Yang, Zhe Li, Qihang Lin, Yi Yang
Date: 2018-08-30 00:00:00
Tasks: Deep Learning

Stochastic momentum methods have been widely adopted in training deep neural
networks. However, their theoretical analysis of convergence of the training
objective and the generalization error for prediction is still under-explored.
This paper aims to bridge the gap between practice and theory by analyzing the
stochastic gradient (SG) method, and the stochastic momentum methods including
two famous variants, i.e., the stochastic heavy-ball (SHB) method and the
stochastic variant of Nesterov's accelerated gradient (SNAG) method. We propose
a framework that unifies the three variants. We then derive the convergence
rates of the norm of gradient for the non-convex optimization problem, and
analyze the generalization performance through the uniform stability approach.
Particularly, the convergence analysis of the training objective exhibits that
SHB and SNAG have no advantage over SG. However, the stability analysis shows
that the momentum term can improve the stability of the learned model and hence
improve the generalization performance. These theoretical insights verify the
common wisdom and are also corroborated by our empirical analysis on deep
learning.
Learning End-to-end Autonomous Driving using Guided Auxiliary Supervision
Authors: Ashish Mehta, Adithya Subramanian, Anbumani Subramanian
Date: 2018-08-30 00:00:00
Tasks: Autonomous Driving, Multi-Task Learning

Learning to drive faithfully in highly stochastic urban settings remains an
open problem. To that end, we propose a Multi-task Learning from Demonstration
(MT-LfD) framework which uses supervised auxiliary task prediction to guide the
main task of predicting the driving commands. Our framework involves an
end-to-end trainable network for imitating the expert demonstrator's driving
commands. The network intermediately predicts visual affordances and action
primitives through direct supervision which provide the aforementioned
auxiliary supervised guidance. We demonstrate that such joint learning and
supervised guidance facilitates hierarchical task decomposition, assisting the
agent to learn faster, achieve better driving performance and increases
transparency of the otherwise black-box end-to-end network. We run our
experiments to validate the MT-LfD framework in CARLA, an open-source urban
driving simulator. We introduce multiple non-player agents in CARLA and induce
temporal noise in them for realistic stochasticity.
Deep Chronnectome Learning via Full Bidirectional Long Short-Term Memory Networks for MCI Diagnosis
Authors: Weizheng Yan, Han Zhang, Jing Sui, Dinggang Shen
Date: 2018-08-30 00:00:00
Tasks: Clustering, Diagnostic, Functional Connectivity, General Classification, Time Series Analysis

Brain functional connectivity (FC) extracted from resting-state fMRI
(RS-fMRI) has become a popular approach for disease diagnosis, where
discriminating subjects with mild cognitive impairment (MCI) from normal
controls (NC) is still one of the most challenging problems. Dynamic functional
connectivity (dFC), consisting of time-varying spatiotemporal dynamics, may
characterize "chronnectome" diagnostic information for improving MCI
classification. However, most of the current dFC studies are based on detecting
discrete major brain status via spatial clustering, which ignores rich
spatiotemporal dynamics contained in such chronnectome. We propose Deep
Chronnectome Learning for exhaustively mining the comprehensive information,
especially the hidden higher-level features, i.e., the dFC time series that may
add critical diagnostic power for MCI classification. To this end, we devise a
new Fully-connected Bidirectional Long Short-Term Memory Network (Full-BiLSTM)
to effectively learn the periodic brain status changes using both past and
future information for each brief time segment and then fuse them to form the
final output. We have applied our method to a rigorously built large-scale
multi-site database (i.e., with 164 data from NCs and 330 from MCIs, which can
be further augmented by 25 folds). Our method outperforms other
state-of-the-art approaches with an accuracy of 73.6% under solid
cross-validations. We also made extensive comparisons among multiple variants
of LSTM models. The results suggest high feasibility of our method with
promising value also for other brain disorder diagnoses.
Robot_gym: accelerated robot training through simulation in the cloud with ROS and Gazebo
Authors: Víctor Mayoral Vilches, Alejandro Hernández Cordero, Asier Bilbao Calvo, Irati Zamalloa Ugarte, Risto Kojcev
Date: 2018-08-30 00:00:00

Rather than programming, training allows robots to achieve behaviors that
generalize better and are capable to respond to real-world needs. However, such
training requires a big amount of experimentation which is not always feasible
for a physical robot. In this work, we present robot_gym, a framework to
accelerate robot training through simulation in the cloud that makes use of
roboticists' tools, simplifying the development and deployment processes on
real robots. We unveil that, for simple tasks, simple 3DoF robots require more
than 140 attempts to learn. For more complex, 6DoF robots, the number of
attempts increases to more than 900 for the same task. We demonstrate that our
framework, for simple tasks, accelerates the robot training time by more than
33% while maintaining similar levels of accuracy and repeatability.
Gaussian Mixture Generative Adversarial Networks for Diverse Datasets, and the Unsupervised Clustering of Images
Authors: Matan Ben-Yosef, Daphna Weinshall
Date: 2018-08-30 00:00:00
Tasks: Clustering, Diversity

Generative Adversarial Networks (GANs) have been shown to produce
realistically looking synthetic images with remarkable success, yet their
performance seems less impressive when the training set is highly diverse. In
order to provide a better fit to the target data distribution when the dataset
includes many different classes, we propose a variant of the basic GAN model,
called Gaussian Mixture GAN (GM-GAN), where the probability distribution over
the latent space is a mixture of Gaussians. We also propose a supervised
variant which is capable of conditional sample synthesis. In order to evaluate
the model's performance, we propose a new scoring method which separately takes
into account two (typically conflicting) measures - diversity vs. quality of
the generated data. Through a series of empirical experiments, using both
synthetic and real-world datasets, we quantitatively show that GM-GANs
outperform baselines, both when evaluated using the commonly used Inception
Score, and when evaluated using our own alternative scoring method. In
addition, we qualitatively demonstrate how the \textit{unsupervised} variant of
GM-GAN tends to map latent vectors sampled from different Gaussians in the
latent space to samples of different classes in the data space. We show how
this phenomenon can be exploited for the task of unsupervised clustering, and
provide quantitative evaluation showing the superiority of our method for the
unsupervised clustering of image datasets. Finally, we demonstrate a feature
which further sets our model apart from other GAN models: the option to control
the quality-diversity trade-off by altering, post-training, the probability
distribution of the latent space. This allows one to sample higher quality and
lower diversity samples, or vice versa, according to one's needs.
IEA: Inner Ensemble Average within a convolutional neural network
Authors: Abduallah Mohamed, Xinrui Hua, Xianda Zhou, Christian Claudel
Date: 2018-08-30 00:00:00
Proceeding: ICLR 2019 5
Tasks: Benchmarking, Ensemble Learning

Ensemble learning is a method of combining multiple trained models to improve model accuracy. We propose the usage of such methods, specifically ensemble average, inside Convolutional Neural Network (CNN) architectures by replacing the single convolutional layers with Inner Average Ensembles (IEA) of multiple convolutional layers. Empirical results on different benchmarking datasets show that CNN models using IEA outperform those with regular convolutional layers. A visual and a similarity score analysis of the features generated from IEA explains why it boosts the model performance.
A Coordinate-Free Construction of Scalable Natural Gradient
Authors: Kevin Luk, Roger Grosse
Date: 2018-08-30 00:00:00

Most neural networks are trained using first-order optimization methods,
which are sensitive to the parameterization of the model. Natural gradient
descent is invariant to smooth reparameterizations because it is defined in a
coordinate-free way, but tractable approximations are typically defined in
terms of coordinate systems, and hence may lose the invariance properties. We
analyze the invariance properties of the Kronecker-Factored Approximate
Curvature (K-FAC) algorithm by constructing the algorithm in a coordinate-free
way. We explicitly construct a Riemannian metric under which the natural
gradient matches the K-FAC update; invariance to affine transformations of the
activations follows immediately. We extend our framework to analyze the
invariance properties of K-FAC applied to convolutional networks and recurrent
neural networks, as well as metrics other than the usual Fisher metric.
Generalize Symbolic Knowledge With Neural Rule Engine
Authors: Shen Li, Hengru Xu, Zhengdong Lu
Date: 2018-08-30 00:00:00

As neural networks have dominated the state-of-the-art results in a wide range of NLP tasks, it attracts considerable attention to improve the performance of neural models by integrating symbolic knowledge. Different from existing works, this paper investigates the combination of these two powerful paradigms from the knowledge-driven side. We propose Neural Rule Engine (NRE), which can learn knowledge explicitly from logic rules and then generalize them implicitly with neural networks. NRE is implemented with neural module networks in which each module represents an action of a logic rule. The experiments show that NRE could greatly improve the generalization abilities of logic rules with a significant increase in recall. Meanwhile, the precision is still maintained at a high level.
PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptors
Authors: Haowen Deng, Tolga Birdal, Slobodan Ilic
Date: 2018-08-30 00:00:00
Proceeding: ECCV 2018 9
Tasks: Point Cloud Registration

We present PPF-FoldNet for unsupervised learning of 3D local descriptors on
pure point cloud geometry. Based on the folding-based auto-encoding of well
known point pair features, PPF-FoldNet offers many desirable properties: it
necessitates neither supervision, nor a sensitive local reference frame,
benefits from point-set sparsity, is end-to-end, fast, and can extract powerful
rotation invariant descriptors. Thanks to a novel feature visualization, its
evolution can be monitored to provide interpretable insights. Our extensive
experiments demonstrate that despite having six degree-of-freedom invariance
and lack of training labels, our network achieves state of the art results in
standard benchmark datasets and outperforms its competitors when rotations and
varying point densities are present. PPF-FoldNet achieves $9\%$ higher recall
on standard benchmarks, $23\%$ higher recall when rotations are introduced into
the same datasets and finally, a margin of $>35\%$ is attained when point
density is significantly decreased.
Backdoor Embedding in Convolutional Neural Network Models via Invisible Perturbation
Authors: Cong Liao, Haoti Zhong, Anna Squicciarini, Sencun Zhu, David Miller
Date: 2018-08-30 00:00:00
Tasks: Data Poisoning, Deep Learning, General Classification, image-classification, Image Classification

Deep learning models have consistently outperformed traditional machine
learning models in various classification tasks, including image
classification. As such, they have become increasingly prevalent in many real
world applications including those where security is of great concern. Such
popularity, however, may attract attackers to exploit the vulnerabilities of
the deployed deep learning models and launch attacks against security-sensitive
applications. In this paper, we focus on a specific type of data poisoning
attack, which we refer to as a {\em backdoor injection attack}. The main goal
of the adversary performing such attack is to generate and inject a backdoor
into a deep learning model that can be triggered to recognize certain embedded
patterns with a target label of the attacker's choice. Additionally, a backdoor
injection attack should occur in a stealthy manner, without undermining the
efficacy of the victim model. Specifically, we propose two approaches for
generating a backdoor that is hardly perceptible yet effective in poisoning the
model. We consider two attack settings, with backdoor injection carried out
either before model training or during model updating. We carry out extensive
experimental evaluations under various assumptions on the adversary model, and
demonstrate that such attacks can be effective and achieve a high attack
success rate (above $90\%$) at a small cost of model accuracy loss (below
$1\%$) with a small injection rate (around $1\%$), even under the weakest
assumption wherein the adversary has no knowledge either of the original
training data or the classifier model.
Acquiring Annotated Data with Cross-lingual Explicitation for Implicit Discourse Relation Classification
Authors: Wei Shi, Frances Yung, Vera Demberg
Date: 2018-08-30 00:00:00
Proceeding: WS 2019 6
Tasks: 16k, Discourse Parsing, General Classification, Implicit Discourse Relation Classification, Open-Ended Question Answering, Relation, Relation Classification, Translation

Implicit discourse relation classification is one of the most challenging and
important tasks in discourse parsing, due to the lack of connective as strong
linguistic cues. A principle bottleneck to further improvement is the shortage
of training data (ca.~16k instances in the PDTB). Shi et al. (2017) proposed to
acquire additional data by exploiting connectives in translation: human
translators mark discourse relations which are implicit in the source language
explicitly in the translation. Using back-translations of such explicitated
connectives improves discourse relation parsing performance. This paper
addresses the open question of whether the choice of the translation language
matters, and whether multiple translations into different languages can be
effectively used to improve the quality of the additional data.
Multi-Source Syntactic Neural Machine Translation
Authors: Anna Currey, Kenneth Heafield
Date: 2018-08-30 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Machine Translation, Sentence, Translation

We introduce a novel multi-source technique for incorporating source syntax
into neural machine translation using linearized parses. This is achieved by
employing separate encoders for the sequential and parsed versions of the same
source sentence; the resulting representations are then combined using a
hierarchical attention mechanism. The proposed model improves over both seq2seq
and parsed baselines by over 1 BLEU on the WMT17 English-German task. Further
analysis shows that our multi-source syntactic model is able to translate
successfully without any parsed input, unlike standard parsed methods. In
addition, performance does not deteriorate as much on long sentences as for the
baselines.
Comparative Studies of Detecting Abusive Language on Twitter
Authors: Younghun Lee, Seunghyun Yoon, Kyomin Jung
Date: 2018-08-30 00:00:00
Proceeding: WS 2018 10
Tasks: Abuse Detection, Abusive Language, Clustering, Hate Speech Detection, Sentiment Analysis, Twitter Sentiment Analysis

The context-dependent nature of online aggression makes annotating large
collections of data extremely difficult. Previously studied datasets in abusive
language detection have been insufficient in size to efficiently train deep
learning models. Recently, Hate and Abusive Speech on Twitter, a dataset much
greater in size and reliability, has been released. However, this dataset has
not been comprehensively studied to its potential. In this paper, we conduct
the first comparative study of various learning models on Hate and Abusive
Speech on Twitter, and discuss the possibility of using additional features and
context data for improvements. Experimental results show that bidirectional GRU
networks trained on word-level features, with Latent Topic Clustering modules,
is the most accurate model scoring 0.805 F1.
Learning to adapt: a meta-learning approach for speaker adaptation
Authors: Ondřej Klejch, Joachim Fainberg, Peter Bell
Date: 2018-08-30 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Meta-Learning, speech-recognition, Speech Recognition

The performance of automatic speech recognition systems can be improved by
adapting an acoustic model to compensate for the mismatch between training and
testing conditions, for example by adapting to unseen speakers. The success of
speaker adaptation methods relies on selecting weights that are suitable for
adaptation and using good adaptation schedules to update these weights in order
not to overfit to the adaptation data. In this paper we investigate a
principled way of adapting all the weights of the acoustic model using a
meta-learning. We show that the meta-learner can learn to perform supervised
and unsupervised speaker adaptation and that it outperforms a strong baseline
adapting LHUC parameters when adapting a DNN AM with 1.5M parameters. We also
report initial experiments on adapting TDNN AMs, where the meta-learner
achieves comparable performance with LHUC.
Automated Scene Flow Data Generation for Training and Verification
Authors: Oliver Wasenmüller, René Schuster, Didier Stricker, Karl Leiss, Jürger Pfister, Oleksandra Ganus, Julian Tatsch, Artem Savkin, Nikolas Brasch
Date: 2018-08-30 00:00:00
Tasks: Position

Scene flow describes the 3D position as well as the 3D motion of each pixel
in an image. Such algorithms are the basis for many state-of-the-art autonomous
or automated driving functions. For verification and training large amounts of
ground truth data is required, which is not available for real data. In this
paper, we demonstrate a technology to create synthetic data with dense and
precise scene flow ground truth.
Pronoun Translation in English-French Machine Translation: An Analysis of Error Types
Authors: Christian Hardmeier, Liane Guillou
Date: 2018-08-30 00:00:00
Tasks: Machine Translation, NMT, Sentence, Translation

Pronouns are a long-standing challenge in machine translation. We present a
study of the performance of a range of rule-based, statistical and neural MT
systems on pronoun translation based on an extensive manual evaluation using
the PROTEST test suite, which enables a fine-grained analysis of different
pronoun types and sheds light on the difficulties of the task. We find that the
rule-based approaches in our corpus perform poorly as a result of
oversimplification, whereas SMT and early NMT systems exhibit significant
shortcomings due to a lack of awareness of the functional and referential
properties of pronouns. A recent Transformer-based NMT system with
cross-sentence context shows very promising results on non-anaphoric pronouns
and intra-sentential anaphora, but there is still considerable room for
improvement in examples with cross-sentence dependencies.
Towards a Better Metric for Evaluating Question Generation Systems
Authors: Preksha Nema, Mitesh M. Khapra
Date: 2018-08-30 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Knowledge Graphs, Question Generation, Question-Generation

There has always been criticism for using $n$-gram based similarity metrics,
such as BLEU, NIST, etc, for evaluating the performance of NLG systems.
However, these metrics continue to remain popular and are recently being used
for evaluating the performance of systems which automatically generate
questions from documents, knowledge graphs, images, etc. Given the rising
interest in such automatic question generation (AQG) systems, it is important
to objectively examine whether these metrics are suitable for this task. In
particular, it is important to verify whether such metrics used for evaluating
AQG systems focus on answerability of the generated question by preferring
questions which contain all relevant information such as question type
(Wh-types), entities, relations, etc. In this work, we show that current
automatic evaluation metrics based on $n$-gram similarity do not always
correlate well with human judgments about answerability of a question. To
alleviate this problem and as a first step towards better evaluation metrics
for AQG, we introduce a scoring function to capture answerability and show that
when this scoring function is integrated with existing metrics, they correlate
significantly better with human judgments. The scripts and data developed as a
part of this work are made publicly available at
https://github.com/PrekshaNema25/Answerability-Metric