pwc-archive/papers-with-abstracts huggingface dataset:
Polylingual Wordnet
Authors: Mihael Arcan, John McCrae, Paul Buitelaar
Date: 2019-03-04 00:00:00
Tasks: Machine Translation, Translation, Word Sense Disambiguation

Princeton WordNet is one of the most important resources for natural language
processing, but is only available for English. While it has been translated
using the expand approach to many other languages, this is an expensive manual
process. Therefore it would be beneficial to have a high-quality automatic
translation approach that would support NLP techniques, which rely on WordNet
in new languages. The translation of wordnets is fundamentally complex because
of the need to translate all senses of a word including low frequency senses,
which is very challenging for current machine translation approaches. For this
reason we leverage existing translations of WordNet in other languages to
identify contextual information for wordnet senses from a large set of generic
parallel corpora. We evaluate our approach using 10 translated wordnets for
European languages. Our experiment shows a significant improvement over
translation without any contextual information. Furthermore, we evaluate how
the choice of pivot languages affects performance of multilingual word sense
disambiguation.
Multivariate extensions of isotonic regression and total variation denoising via entire monotonicity and Hardy-Krause variation
Authors: Billy Fang, Adityanand Guntuboyina, Bodhisattva Sen
Date: 2019-03-04 00:00:00
Tasks: Denoising, regression

We consider the problem of nonparametric regression when the covariate is $d$-dimensional, where $d \geq 1$. In this paper we introduce and study two nonparametric least squares estimators (LSEs) in this setting---the entirely monotonic LSE and the constrained Hardy-Krause variation LSE. We show that these two LSEs are natural generalizations of univariate isotonic regression and univariate total variation denoising, respectively, to multiple dimensions. We discuss the characterization and computation of these two LSEs obtained from $n$ data points. We provide a detailed study of their risk properties under the squared error loss and fixed uniform lattice design. We show that the finite sample risk of these LSEs is always bounded from above by $n^{-2/3}$ modulo logarithmic factors depending on $d$; thus these nonparametric LSEs avoid the curse of dimensionality to some extent. We also prove nearly matching minimax lower bounds. Further, we illustrate that these LSEs are particularly useful in fitting rectangular piecewise constant functions. Specifically, we show that the risk of the entirely monotonic LSE is almost parametric (at most $1/n$ up to logarithmic factors) when the true function is well-approximable by a rectangular piecewise constant entirely monotone function with not too many constant pieces. A similar result is also shown to hold for the constrained Hardy-Krause variation LSE for a simple subclass of rectangular piecewise constant functions. We believe that the proposed LSEs yield a novel approach to estimating multivariate functions using convex optimization that avoid the curse of dimensionality to some extent.
An Adversarial Super-Resolution Remedy for Radar Design Trade-offs
Authors: Karim Armanious, Sherif Abdulatif, Fady Aziz, Urs Schneider, Bin Yang
Date: 2019-03-04 00:00:00
Tasks: Autonomous Driving, Super-Resolution

Radar is of vital importance in many fields, such as autonomous driving, safety and surveillance applications. However, it suffers from stringent constraints on its design parametrization leading to multiple trade-offs. For example, the bandwidth in FMCW radars is inversely proportional with both the maximum unambiguous range and range resolution. In this work, we introduce a new method for circumventing radar design trade-offs. We propose the use of recent advances in computer vision, more specifically generative adversarial networks (GANs), to enhance low-resolution radar acquisitions into higher resolution counterparts while maintaining the advantages of the low-resolution parametrization. The capability of the proposed method was evaluated on the velocity resolution and range-azimuth trade-offs in micro-Doppler signatures and FMCW uniform linear array (ULA) radars, respectively.
Sim-to-Real Transfer for Biped Locomotion
Authors: Wenhao Yu, Visak CV Kumar, Greg Turk, C. Karen Liu
Date: 2019-03-04 00:00:00
Tasks: Bayesian Optimization, Friction

We present a new approach for transfer of dynamic robot control policies such as biped locomotion from simulation to real hardware. Key to our approach is to perform system identification of the model parameters {\mu} of the hardware (e.g. friction, center-of-mass) in two distinct stages, before policy learning (pre-sysID) and after policy learning (post-sysID). Pre-sysID begins by collecting trajectories from the physical hardware based on a set of generic motion sequences. Because the trajectories may not be related to the task of interest, presysID does not attempt to accurately identify the true value of {\mu}, but only to approximate the range of {\mu} to guide the policy learning. Next, a Projected Universal Policy (PUP) is created by simultaneously training a network that projects {\mu} to a low-dimensional latent variable {\eta} and a family of policies that are conditioned on {\eta}. The second round of system identification (post-sysID) is then carried out by deploying the PUP on the robot hardware using task-relevant trajectories. We use Bayesian Optimization to determine the values for {\eta} that optimizes the performance of PUP on the real hardware. We have used this approach to create three successful biped locomotion controllers (walk forward, walk backwards, walk sideways) on the Darwin OP2 robot.
Joint Perception and Control as Inference with an Object-based Implementation
Authors: Minne Li, Zheng Tian, Pranav Nashikkar, Ian Davies, Ying Wen, Jun Wang
Date: 2019-03-04 00:00:00
Tasks: Bayesian Inference, Decision Making, Model-based Reinforcement Learning, Object

Existing model-based reinforcement learning methods often study perception modeling and decision making separately. We introduce joint Perception and Control as Inference (PCI), a general framework to combine perception and control for partially observable environments through Bayesian inference. Based on the fact that object-level inductive biases are critical in human perceptual learning and reasoning, we propose Object-based Perception Control (OPC), an instantiation of PCI which manages to facilitate control using automatic discovered object-based representations. We develop an unsupervised end-to-end solution and analyze the convergence of the perception model update. Experiments in a high-dimensional pixel environment demonstrate the learning effectiveness of our object-based perception control approach. Specifically, we show that OPC achieves good perceptual grouping quality and outperforms several strong baselines in accumulated rewards.
α-Rank: Multi-Agent Evaluation by Evolution
Authors: Shayegan Omidshafiei, Christos Papadimitriou, Georgios Piliouras, Karl Tuyls, Mark Rowland, Jean-Baptiste Lespiau, Wojciech M. Czarnecki, Marc Lanctot, Julien Perolat, Remi Munos
Date: 2019-03-04 00:00:00
Tasks: Mathematical Proofs, MuJoCo

We introduce {\alpha}-Rank, a principled evolutionary dynamics methodology,
for the evaluation and ranking of agents in large-scale multi-agent
interactions, grounded in a novel dynamical game-theoretic solution concept
called Markov-Conley chains (MCCs). The approach leverages continuous-time and
discrete-time evolutionary dynamical systems applied to empirical games, and
scales tractably in the number of agents, in the type of interactions (beyond
dyadic), and the type of empirical games (symmetric and asymmetric). Current
models are fundamentally limited in one or more of these dimensions, and are
not guaranteed to converge to the desired game-theoretic solution concept
(typically the Nash equilibrium). {\alpha}-Rank automatically provides a
ranking over the set of agents under evaluation and provides insights into
their strengths, weaknesses, and long-term dynamics in terms of basins of
attraction and sink components. This is a direct consequence of our new model's
direct correspondence to the dynamical MCC solution concept when its
ranking-intensity parameter, {\alpha}, is chosen to be large, which exactly
forms the basis of {\alpha}-Rank. In contrast to the Nash equilibrium, which is
a static solution concept based solely on fixed points, MCCs are a dynamical
solution concept based on the Markov chain formalism, Conley's Fundamental
Theorem of Dynamical Systems, and the core ingredients of dynamical systems:
fixed points, recurrent sets, periodic orbits, and limit cycles. Our
{\alpha}-Rank method runs in polynomial time with respect to the total number
of pure strategy profiles, whereas computing a Nash equilibrium for a
general-sum game is known to be intractable. We introduce mathematical proofs
that reveal the formal underpinnings of the {\alpha}-Rank methodology. We
illustrate the method in canonical games and in AlphaGo, AlphaZero, MuJoCo
Soccer, and Poker.
Microscopic Traffic Simulation by Cooperative Multi-agent Deep Reinforcement Learning
Authors: Giulio Bacchiani, Daniele Molinari, Marco Patander
Date: 2019-03-04 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Expert human drivers perform actions relying on traffic laws and their
previous experience. While traffic laws are easily embedded into an artificial
brain, modeling human complex behaviors which come from past experience is a
more challenging task. One of these behaviors is the capability of
communicating intentions and negotiating the right of way through driving
actions, as when a driver is entering a crowded roundabout and observes other
cars movements to guess the best time to merge in. In addition, each driver has
its own unique driving style, which is conditioned by both its personal
characteristics, such as age and quality of sight, and external factors, such
as being late or in a bad mood. For these reasons, the interaction between
different drivers is not trivial to simulate in a realistic manner. In this
paper, this problem is addressed by developing a microscopic simulator using a
Deep Reinforcement Learning Algorithm based on a combination of visual frames,
representing the perception around the vehicle, and a vector of numerical
parameters. In particular, the algorithm called Asynchronous Advantage
Actor-Critic has been extended to a multi-agent scenario in which every agent
needs to learn to interact with other similar agents. Moreover, the model
includes a novel architecture such that the driving style of each vehicle is
adjustable by tuning some of its input parameters, permitting to simulate
drivers with different levels of aggressiveness and desired cruising speeds.
Reduced Focal Loss: 1st Place Solution to xView object detection in Satellite Imagery
Authors: Nikolay Sergievskiy, Alexander Ponamarev
Date: 2019-03-04 00:00:00
Tasks: object-detection, Object Detection

This paper describes our approach to the DIUx xView 2018 Detection Challenge
[1]. This challenge focuses on a new satellite imagery dataset. The dataset
contains 60 object classes that are highly imbalanced. Due to the imbalanced
nature of the dataset, the training process becomes significantly more
challenging. To address this problem, we introduce a novel Reduced Focal Loss
function, which brought us 1st place in the DIUx xView 2018 Detection
Challenge.
Hybrid Actor-Critic Reinforcement Learning in Parameterized Action Space
Authors: Zhou Fan, Rui Su, Wei-Nan Zhang, Yong Yu
Date: 2019-03-04 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper we propose a hybrid architecture of actor-critic algorithms for reinforcement learning in parameterized action space, which consists of multiple parallel sub-actor networks to decompose the structured action space into simpler action spaces along with a critic network to guide the training of all sub-actor networks. While this paper is mainly focused on parameterized action space, the proposed architecture, which we call hybrid actor-critic, can be extended for more general action spaces which has a hierarchical structure. We present an instance of the hybrid actor-critic architecture based on proximal policy optimization (PPO), which we refer to as hybrid proximal policy optimization (H-PPO). Our experiments test H-PPO on a collection of tasks with parameterized action space, where H-PPO demonstrates superior performance over previous methods of parameterized action reinforcement learning.
The StreetLearn Environment and Dataset
Authors: Piotr Mirowski, Andras Banki-Horvath, Keith Anderson, Denis Teplyashin, Karl Moritz Hermann, Mateusz Malinowski, Matthew Koichi Grimes, Karen Simonyan, Koray Kavukcuoglu, Andrew Zisserman, Raia Hadsell
Date: 2019-03-04 00:00:00
Tasks: Cross-View Geo-Localisation, Decision Making, Reinforcement Learning

Navigation is a rich and well-grounded problem domain that drives progress in
many different areas of research: perception, planning, memory, exploration,
and optimisation in particular. Historically these challenges have been
separately considered and solutions built that rely on stationary datasets -
for example, recorded trajectories through an environment. These datasets
cannot be used for decision-making and reinforcement learning, however, and in
general the perspective of navigation as an interactive learning task, where
the actions and behaviours of a learning agent are learned simultaneously with
the perception and planning, is relatively unsupported. Thus, existing
navigation benchmarks generally rely on static datasets (Geiger et al., 2013;
Kendall et al., 2015) or simulators (Beattie et al., 2016; Shah et al., 2018).
To support and validate research in end-to-end navigation, we present
StreetLearn: an interactive, first-person, partially-observed visual
environment that uses Google Street View for its photographic content and broad
coverage, and give performance baselines for a challenging goal-driven
navigation task. The environment code, baseline agent code, and the dataset are
available at http://streetlearn.cc
Joint segmentation and classification of retinal arteries/veins from fundus images
Authors: Fantin Girard, Conrad Kavalec, Farida Cheriet
Date: 2019-03-04 00:00:00
Tasks: General Classification, Segmentation, Sensitivity, Specificity

Objective Automatic artery/vein (A/V) segmentation from fundus images is
required to track blood vessel changes occurring with many pathologies
including retinopathy and cardiovascular pathologies. One of the clinical
measures that quantifies vessel changes is the arterio-venous ratio (AVR) which
represents the ratio between artery and vein diameters. This measure
significantly depends on the accuracy of vessel segmentation and classification
into arteries and veins. This paper proposes a fast, novel method for semantic
A/V segmentation combining deep learning and graph propagation.
  Methods A convolutional neural network (CNN) is proposed to jointly segment
and classify vessels into arteries and veins. The initial CNN labeling is
propagated through a graph representation of the retinal vasculature, whose
nodes are defined as the vessel branches and edges are weighted by the cost of
linking pairs of branches. To efficiently propagate the labels, the graph is
simplified into its minimum spanning tree.
  Results The method achieves an accuracy of 94.8% for vessels segmentation.
The A/V classification achieves a specificity of 92.9% with a sensitivity of
93.7% on the CT-DRIVE database compared to the state-of-the-art-specificity and
sensitivity, both of 91.7%.
  Conclusion The results show that our method outperforms the leading previous
works on a public dataset for A/V classification and is by far the fastest.
  Significance The proposed global AVR calculated on the whole fundus image
using our automatic A/V segmentation method can better track vessel changes
associated to diabetic retinopathy than the standard local AVR calculated only
around the optic disc.
Time Series Source Separation using Dynamic Mode Decomposition
Authors: Arvind Prasadan, Raj Rao Nadakuditi
Date: 2019-03-04 00:00:00
Tasks: blind source separation, Change Point Detection, Time Series, Time Series Analysis

The Dynamic Mode Decomposition (DMD) extracted dynamic modes are the non-orthogonal eigenvectors of the matrix that best approximates the one-step temporal evolution of the multivariate samples. In the context of dynamical system analysis, the extracted dynamic modes are a generalization of global stability modes. We apply DMD to a data matrix whose rows are linearly independent, additive mixtures of latent time series. We show that when the latent time series are uncorrelated at a lag of one time-step then, in the large sample limit, the recovered dynamic modes will approximate, up to a column-wise normalization, the columns of the mixing matrix. Thus, DMD is a time series blind source separation algorithm in disguise, but is different from closely related second order algorithms such as the Second-Order Blind Identification (SOBI) method and the Algorithm for Multiple Unknown Signals Extraction (AMUSE). All can unmix mixed stationary, ergodic Gaussian time series in a way that kurtosis-based Independent Components Analysis (ICA) fundamentally cannot. We use our insights on single lag DMD to develop a higher-lag extension, analyze the finite sample performance with and without randomly missing data, and identify settings where the higher lag variant can outperform the conventional single lag variant. We validate our results with numerical simulations, and highlight how DMD can be used in change point detection.
Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks
Authors: Ningyu Zhang, Shumin Deng, Zhanlin Sun, Guanying Wang, Xi Chen, Wei zhang, Huajun Chen
Date: 2019-03-04 00:00:00
Proceeding: NAACL 2019 6
Tasks: Knowledge Graph Embeddings, Relation, Relation Extraction

We propose a distance supervised relation extraction approach for
long-tailed, imbalanced data which is prevalent in real-world settings. Here,
the challenge is to learn accurate "few-shot" models for classes existing at
the tail of the class distribution, for which little data is available.
Inspired by the rich semantic correlations between classes at the long tail and
those at the head, we take advantage of the knowledge from data-rich classes at
the head of the distribution to boost the performance of the data-poor classes
at the tail. First, we propose to leverage implicit relational knowledge among
class labels from knowledge graph embeddings and learn explicit relational
knowledge using graph convolution networks. Second, we integrate that
relational knowledge into relation extraction model by coarse-to-fine
knowledge-aware attention mechanism. We demonstrate our results for a
large-scale benchmark dataset which show that our approach significantly
outperforms other baselines, especially for long-tail relations.
Generalizing Graph Convolutional Neural Networks with Edge-Variant Recursions on Graphs
Authors: Elvin Isufi, Fernando Gama, Alejandro Ribeiro
Date: 2019-03-04 00:00:00
Tasks: General Classification

This paper reviews graph convolutional neural networks (GCNNs) through the
lens of edge-variant graph filters. The edge-variant graph filter is a finite
order, linear, and local recursion that allows each node, in each iteration, to
weigh differently the information of its neighbors. By exploiting this
recursion, we formulate a general framework for GCNNs which considers
state-of-the-art solutions as particular cases. This framework results useful
to i) understand the tradeoff between local detail and the number of parameters
of each solution and ii) provide guidelines for developing a myriad of novel
approaches that can be implemented locally in the vertex domain. One of such
approaches is presented here showing superior performance w.r.t. current
alternatives in graph signal classification problems.
Traditional Machine Learning for Pitch Detection
Authors: Thomas Drugman, Goeric Huybrechts, Viacheslav Klimkov, Alexis Moinet
Date: 2019-03-04 00:00:00
Tasks: Articles, BIG-bench Machine Learning, Clustering, regression

Pitch detection is a fundamental problem in speech processing as F0 is used
in a large number of applications. Recent articles have proposed deep learning
for robust pitch tracking. In this paper, we consider voicing detection as a
classification problem and F0 contour estimation as a regression problem. For
both tasks, acoustic features from multiple domains and traditional machine
learning methods are used. The discrimination power of existing and proposed
features is assessed through mutual information. Multiple supervised and
unsupervised approaches are compared. A significant relative reduction of
voicing errors over the best baseline is obtained: 20% with the best clustering
method (K-means) and 45% with a Multi-Layer Perceptron. For F0 contour
estimation, the benefits of regression techniques are limited though. We
investigate whether those objective gains translate in a parametric synthesis
task. Clear perceptual preferences are observed for the proposed approach over
two widely-used baselines (RAPT and DIO).
Safety Verification and Robustness Analysis of Neural Networks via Quadratic Constraints and Semidefinite Programming
Authors: Mahyar Fazlyab, Manfred Morari, George J. Pappas
Date: 2019-03-04 00:00:00
Tasks: Computational Efficiency

Certifying the safety or robustness of neural networks against input uncertainties and adversarial attacks is an emerging challenge in the area of safe machine learning and control. To provide such a guarantee, one must be able to bound the output of neural networks when their input changes within a bounded set. In this paper, we propose a semidefinite programming (SDP) framework to address this problem for feed-forward neural networks with general activation functions and input uncertainty sets. Our main idea is to abstract various properties of activation functions (e.g., monotonicity, bounded slope, bounded values, and repetition across layers) with the formalism of quadratic constraints. We then analyze the safety properties of the abstracted network via the S-procedure and semidefinite programming. Our framework spans the trade-off between conservatism and computational efficiency and applies to problems beyond safety verification. We evaluate the performance of our approach via numerical problem instances of various sizes.
Relation Extraction Datasets in the Digital Humanities Domain and their Evaluation with Word Embeddings
Authors: Gerhard Wohlgenannt, Ekaterina Chernyak, Dmitry Ilvovsky, Ariadna Barinova, Dmitry Mouromtsev
Date: 2019-03-04 00:00:00
Tasks: Relation, Relation Extraction, Word Embeddings

In this research, we manually create high-quality datasets in the digital
humanities domain for the evaluation of language models, specifically word
embedding models. The first step comprises the creation of unigram and n-gram
datasets for two fantasy novel book series for two task types each, analogy and
doesn't-match. This is followed by the training of models on the two book
series with various popular word embedding model types such as word2vec, GloVe,
fastText, or LexVec. Finally, we evaluate the suitability of word embedding
models for such specific relation extraction tasks in a situation of comparably
small corpus sizes. In the evaluations, we also investigate and analyze
particular aspects such as the impact of corpus term frequencies and task
difficulty on accuracy. The datasets, and the underlying system and word
embedding models are available on github and can be easily extended with new
datasets and tasks, be used to reproduce the presented results, or be
transferred to other domains.