pwc-archive/papers-with-abstracts huggingface dataset:
Improving Entity Linking by Modeling Latent Relations between Mentions
Authors: Phong Le, Ivan Titov
Date: 2018-04-27 00:00:00
Proceeding: ACL 2018 7
Tasks: Entity Linking

Entity linking involves aligning textual mentions of named entities to their
corresponding entries in a knowledge base. Entity linking systems often exploit
relations between textual mentions in a document (e.g., coreference) to decide
if the linking decisions are compatible. Unlike previous approaches, which
relied on supervised systems or heuristics to predict these relations, we treat
relations as latent variables in our neural entity-linking model. We induce the
relations without any supervision while optimizing the entity-linking system in
an end-to-end fashion. Our multi-relational model achieves the best reported
scores on the standard benchmark (AIDA-CoNLL) and substantially outperforms its
relation-agnostic version. Its training also converges much faster, suggesting
that the injected structural bias helps to explain regularities in the training
data.
Divide-and-Conquer Reinforcement Learning
Authors: Dibya Ghosh, Avi Singh, Aravind Rajeswaran, Vikash Kumar, Sergey Levine
Date: 2017-11-27 00:00:00
Proceeding: ICLR 2018 1
Tasks: Deep Reinforcement Learning, Policy Gradient Methods, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Standard model-free deep reinforcement learning (RL) algorithms sample a new
initial state for each trial, allowing them to optimize policies that can
perform well even in highly stochastic environments. However, problems that
exhibit considerable initial state variation typically produce high-variance
gradient estimates for model-free RL, making direct policy or value function
optimization challenging. In this paper, we develop a novel algorithm that
instead partitions the initial state space into "slices", and optimizes an
ensemble of policies, each on a different slice. The ensemble is gradually
unified into a single policy that can succeed on the whole state space. This
approach, which we term divide-and-conquer RL, is able to solve complex tasks
where conventional deep RL methods are ineffective. Our results show that
divide-and-conquer RL greatly outperforms conventional policy gradient methods
on challenging grasping, manipulation, and locomotion tasks, and exceeds the
performance of a variety of prior methods. Videos of policies learned by our
algorithm can be viewed at http://bit.ly/dnc-rl
The power of deeper networks for expressing natural functions
Authors: David Rolnick, Max Tegmark
Date: 2017-05-16 00:00:00
Proceeding: ICLR 2018 1

It is well-known that neural networks are universal approximators, but that
deeper networks tend in practice to be more powerful than shallower ones. We
shed light on this by proving that the total number of neurons $m$ required to
approximate natural classes of multivariate polynomials of $n$ variables grows
only linearly with $n$ for deep neural networks, but grows exponentially when
merely a single hidden layer is allowed. We also provide evidence that when the
number of hidden layers is increased from $1$ to $k$, the neuron requirement
grows exponentially not with $n$ but with $n^{1/k}$, suggesting that the
minimum number of layers required for practical expressibility grows only
logarithmically with $n$.
An improvement of the convergence proof of the ADAM-Optimizer
Authors: Sebastian Bock, Josef Goppold, Martin Weiß
Date: 2018-04-27 00:00:00

A common way to train neural networks is the Backpropagation. This algorithm
includes a gradient descent method, which needs an adaptive step size. In the
area of neural networks, the ADAM-Optimizer is one of the most popular adaptive
step size methods. It was invented in \cite{Kingma.2015} by Kingma and Ba. The
$5865$ citations in only three years shows additionally the importance of the
given paper. We discovered that the given convergence proof of the optimizer
contains some mistakes, so that the proof will be wrong. In this paper we give
an improvement to the convergence proof of the ADAM-Optimizer.
The Logical Essentials of Bayesian Reasoning
Authors: Bart Jacobs, Fabio Zanasi
Date: 2018-04-03 00:00:00

This chapter offers an accessible introduction to the channel-based approach
to Bayesian probability theory. This framework rests on algebraic and logical
foundations, inspired by the methodologies of programming language semantics.
It offers a uniform, structured and expressive language for describing Bayesian
phenomena in terms of familiar programming concepts, like channel, predicate
transformation and state transformation. The introduction also covers inference
in Bayesian networks, which will be modelled by a suitable calculus of string
diagrams.
Deep Multi-Modal Classification of Intraductal Papillary Mucinous Neoplasms (IPMN) with Canonical Correlation Analysis
Authors: Sarfaraz Hussein, Pujan Kandel, Juan E. Corral, Candice W. Bolan, Michael B. Wallace, Ulas Bagci
Date: 2017-10-26 00:00:00
Tasks: General Classification, Multi-modal Classification, Prognosis

Pancreatic cancer has the poorest prognosis among all cancer types.
Intraductal Papillary Mucinous Neoplasms (IPMNs) are radiographically
identifiable precursors to pancreatic cancer; hence, early detection and
precise risk assessment of IPMN are vital. In this work, we propose a
Convolutional Neural Network (CNN) based computer aided diagnosis (CAD) system
to perform IPMN diagnosis and risk assessment by utilizing multi-modal MRI. In
our proposed approach, we use minimum and maximum intensity projections to ease
the annotation variations among different slices and type of MRIs. Then, we
present a CNN to obtain deep feature representation corresponding to each MRI
modality (T1-weighted and T2-weighted). At the final step, we employ canonical
correlation analysis (CCA) to perform a fusion operation at the feature level,
leading to discriminative canonical correlation features. Extracted features
are used for classification. Our results indicate significant improvements over
other potential approaches to solve this important problem. The proposed
approach doesn't require explicit sample balancing in cases of imbalance
between positive and negative examples. To the best of our knowledge, our study
is the first to automatically diagnose IPMN using multi-modal MRI.
Decoupled Parallel Backpropagation with Convergence Guarantee
Authors: Zhouyuan Huo, Bin Gu, Qian Yang, Heng Huang
Date: 2018-04-27 00:00:00
Proceeding: ICML 2018 7

Backpropagation algorithm is indispensable for the training of feedforward
neural networks. It requires propagating error gradients sequentially from the
output layer all the way back to the input layer. The backward locking in
backpropagation algorithm constrains us from updating network layers in
parallel and fully leveraging the computing resources. Recently, several
algorithms have been proposed for breaking the backward locking. However, their
performances degrade seriously when networks are deep. In this paper, we
propose decoupled parallel backpropagation algorithm for deep learning
optimization with convergence guarantee. Firstly, we decouple the
backpropagation algorithm using delayed gradients, and show that the backward
locking is removed when we split the networks into multiple modules. Then, we
utilize decoupled parallel backpropagation in two stochastic methods and prove
that our method guarantees convergence to critical points for the non-convex
problem. Finally, we perform experiments for training deep convolutional neural
networks on benchmark datasets. The experimental results not only confirm our
theoretical analysis, but also demonstrate that the proposed method can achieve
significant speedup without loss of accuracy.
Auto-Detection of Safety Issues in Baby Products
Authors: Graham Bleaney, Matthew Kuzyk, Julian Man, Hossein Mayanloo, H. R. Tizhoosh
Date: 2018-04-27 00:00:00
Tasks: Dimensionality Reduction, regression

Every year, thousands of people receive consumer product related injuries.
Research indicates that online customer reviews can be processed to
autonomously identify product safety issues. Early identification of safety
issues can lead to earlier recalls, and thus fewer injuries and deaths. A
dataset of product reviews from Amazon.com was compiled, along with
\emph{SaferProducts.gov} complaints and recall descriptions from the Consumer
Product Safety Commission (CPSC) and European Commission Rapid Alert system. A
system was built to clean the collected text and to extract relevant features.
Dimensionality reduction was performed by computing feature relevance through a
Random Forest and discarding features with low information gain. Various
classifiers were analyzed, including Logistic Regression, SVMs,
Na{\"i}ve-Bayes, Random Forests, and an Ensemble classifier. Experimentation
with various features and classifier combinations resulted in a logistic
regression model with 66\% precision in the top 50 reviews surfaced. This
classifier outperforms all benchmarks set by related literature and consumer
product safety professionals.
Convergence and Concentration of Empirical Measures under Wasserstein Distance in Unbounded Functional Spaces
Authors: Jing Lei
Date: 2018-04-27 00:00:00
Tasks: Gaussian Processes

We provide upper bounds of the expected Wasserstein distance between a probability measure and its empirical version, generalizing recent results for finite dimensional Euclidean spaces and bounded functional spaces. Such a generalization can cover Euclidean spaces with large dimensionality, with the optimal dependence on the dimensionality. Our method also covers the important case of Gaussian processes in separable Hilbert spaces, with rate-optimal upper bounds for functional data distributions whose coordinates decay geometrically or polynomially. Moreover, our bounds of the expected value can be combined with mean-concentration results to yield improved exponential tail probability bounds for the Wasserstein error of empirical measures under Bernstein-type or log Sobolev-type conditions.
Persistent Monitoring of Stochastic Spatio-temporal Phenomena with a Small Team of Robots
Authors: Sahil Garg, Nora Ayanian
Date: 2018-04-27 00:00:00

This paper presents a solution for persistent monitoring of real-world
stochastic phenomena, where the underlying covariance structure changes sharply
across time, using a small number of mobile robot sensors. We propose an
adaptive solution for the problem where stochastic real-world dynamics are
modeled as a Gaussian Process (GP). The belief on the underlying covariance
structure is learned from recently observed dynamics as a Gaussian Mixture (GM)
in the low-dimensional hyper-parameters space of the GP and adapted across time
using Sequential Monte Carlo methods. Each robot samples a belief point from
the GM and locally optimizes a set of informative regions by greedy
maximization of the submodular entropy function. The key contributions of this
paper are threefold: adapting the belief on the covariance using Markov Chain
Monte Carlo (MCMC) sampling such that particles survive even under sharp
covariance changes across time; exploiting the belief to transform the problem
of entropy maximization into a decentralized one; and developing an
approximation algorithm to maximize entropy on a set of informative regions in
the continuous space. We illustrate the application of the proposed solution
through extensive simulations using an artificial dataset and multiple real
datasets from fixed sensor deployments, and compare it to three competing
state-of-the-art approaches.
A matrix-free approach to parallel and memory-efficient deformable image registration
Authors: Lars König, Jan Rühaak, Alexander Derksen, Jan Lellmann
Date: 2018-04-27 00:00:00
Tasks: CPU, GPU, Image Registration

We present a novel computational approach to fast and memory-efficient
deformable image registration. In the variational registration model, the
computation of the objective function derivatives is the computationally most
expensive operation, both in terms of runtime and memory requirements. In order
to target this bottleneck, we analyze the matrix structure of gradient and
Hessian computations for the case of the normalized gradient fields distance
measure and curvature regularization. Based on this analysis, we derive
equivalent matrix-free closed-form expressions for derivative computations,
eliminating the need for storing intermediate results and the costs of sparse
matrix arithmetic. This has further benefits: (1) matrix computations can be
fully parallelized, (2) memory complexity for derivative computation is reduced
from linear to constant, and (3) overall computation times are substantially
reduced.
  In comparison with an optimized matrix-based reference implementation, the
CPU implementation achieves speedup factors between 3.1 and 9.7, and we are
able to handle substantially higher resolutions. Using a GPU implementation, we
achieve an additional speedup factor of up to 9.2.
  Furthermore, we evaluated the approach on real-world medical datasets. On ten
publicly available lung CT images from the DIR-Lab 4DCT dataset, we achieve the
best mean landmark error of 0.93 mm compared to other submissions on the
DIR-Lab website, with an average runtime of only 9.23 s. Complete non-rigid
registration of full-size 3D thorax-abdomen CT volumes from oncological
follow-up is achieved in 12.6 s. The experimental results show that the
proposed matrix-free algorithm enables the use of variational registration
models also in applications which were previously impractical due to memory or
runtime restrictions.
Learning Non-Stationary Space-Time Models for Environmental Monitoring
Authors: Sahil Garg, Amarjeet Singh, Fabio Ramos
Date: 2018-04-27 00:00:00

One of the primary aspects of sustainable development involves accurate
understanding and modeling of environmental phenomena. Many of these phenomena
exhibit variations in both space and time and it is imperative to develop a
deeper understanding of techniques that can model space-time dynamics
accurately. In this paper we propose NOSTILL-GP - NOn-stationary Space TIme
variable Latent Length scale GP, a generic non-stationary, spatio-temporal
Gaussian Process (GP) model. We present several strategies, for efficient
training of our model, necessary for real-world applicability. Extensive
empirical validation is performed using three real-world environmental
monitoring datasets, with diverse dynamics across space and time. Results from
the experiments clearly demonstrate general applicability and effectiveness of
our approach for applications in environmental monitoring.
Deep Reinforcement Learning to Acquire Navigation Skills for Wheel-Legged Robots in Complex Environments
Authors: Xi Chen, Ali Ghadirzadeh, John Folkesson, Patric Jensfelt
Date: 2018-04-27 00:00:00
Tasks: Deep Reinforcement Learning, Navigate, Position, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Robot Navigation

Mobile robot navigation in complex and dynamic environments is a challenging
but important problem. Reinforcement learning approaches fail to solve these
tasks efficiently due to reward sparsities, temporal complexities and
high-dimensionality of sensorimotor spaces which are inherent in such problems.
We present a novel approach to train action policies to acquire navigation
skills for wheel-legged robots using deep reinforcement learning. The policy
maps height-map image observations to motor commands to navigate to a target
position while avoiding obstacles. We propose to acquire the multifaceted
navigation skill by learning and exploiting a number of manageable navigation
behaviors. We also introduce a domain randomization technique to improve the
versatility of the training samples. We demonstrate experimentally a
significant improvement in terms of data-efficiency, success rate, robustness
against irrelevant sensory data, and also the quality of the maneuver skills.
Weaver: Deep Co-Encoding of Questions and Documents for Machine Reading
Authors: Martin Raison, Pierre-Emmanuel Mazaré, Rajarshi Das, Antoine Bordes
Date: 2018-04-27 00:00:00
Tasks: Open-Domain Question Answering, Question Answering, Reading Comprehension

This paper aims at improving how machines can answer questions directly from
text, with the focus of having models that can answer correctly multiple types
of questions and from various types of texts, documents or even from large
collections of them. To that end, we introduce the Weaver model that uses a new
way to relate a question to a textual context by weaving layers of recurrent
networks, with the goal of making as few assumptions as possible as to how the
information from both question and context should be combined to form the
answer. We show empirically on six datasets that Weaver performs well in
multiple conditions. For instance, it produces solid results on the very
popular SQuAD dataset (Rajpurkar et al., 2016), solves almost all bAbI tasks
(Weston et al., 2015) and greatly outperforms state-of-the-art methods for open
domain question answering from text (Chen et al., 2017).
A Hybrid Q-Learning Sine-Cosine-based Strategy for Addressing the Combinatorial Test Suite Minimization Problem
Authors: Kamal Z. Zamli, Fakhrud Din, Bestoun S. Ahmed, Miroslav Bures
Date: 2018-04-27 00:00:00
Tasks: Q-Learning

The sine-cosine algorithm (SCA) is a new population-based meta-heuristic
algorithm. In addition to exploiting sine and cosine functions to perform local
and global searches (hence the name sine-cosine), the SCA introduces several
random and adaptive parameters to facilitate the search process. Although it
shows promising results, the search process of the SCA is vulnerable to local
minima/maxima due to the adoption of a fixed switch probability and the bounded
magnitude of the sine and cosine functions (from -1 to 1). In this paper, we
propose a new hybrid Q-learning sine-cosine- based strategy, called the
Q-learning sine-cosine algorithm (QLSCA). Within the QLSCA, we eliminate the
switching probability. Instead, we rely on the Q-learning algorithm (based on
the penalty and reward mechanism) to dynamically identify the best operation
during runtime. Additionally, we integrate two new operations (L\'evy flight
motion and crossover) into the QLSCA to facilitate jumping out of local
minima/maxima and enhance the solution diversity. To assess its performance, we
adopt the QLSCA for the combinatorial test suite minimization problem.
Experimental results reveal that the QLSCA is statistically superior with
regard to test suite size reduction compared to recent state-of-the-art
strategies, including the original SCA, the particle swarm test generator
(PSTG), adaptive particle swarm optimization (APSO) and the cuckoo search
strategy (CS) at the 95% confidence level. However, concerning the comparison
with discrete particle swarm optimization (DPSO), there is no significant
difference in performance at the 95% confidence level. On a positive note, the
QLSCA statistically outperforms the DPSO in certain configurations at the 90%
confidence level.
Mining Automatically Estimated Poses from Video Recordings of Top Athletes
Authors: Rainer Lienhart, Moritz Einfalt, Dan Zecha
Date: 2018-04-24 00:00:00

Human pose detection systems based on state-of-the-art DNNs are on the go to
be extended, adapted and re-trained to fit the application domain of specific
sports. Therefore, plenty of noisy pose data will soon be available from videos
recorded at a regular and frequent basis. This work is among the first to
develop mining algorithms that can mine the expected abundance of noisy and
annotation-free pose data from video recordings in individual sports. Using
swimming as an example of a sport with dominant cyclic motion, we show how to
determine unsupervised time-continuous cycle speeds and temporally striking
poses as well as measure unsupervised cycle stability over time. Additionally,
we use long jump as an example of a sport with a rigid phase-based motion to
present a technique to automatically partition the temporally estimated pose
sequences into their respective phases. This enables the extraction of
performance relevant, pose-based metrics currently used by national
professional sports associations. Experimental results prove the effectiveness
of our mining algorithms, which can also be applied to other cycle-based or
phase-based types of sport.
Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders
Authors: Ananya Harsh Jha, Saket Anand, Maneesh Singh, V. S. R. Veeravasarapu
Date: 2018-04-27 00:00:00
Proceeding: ECCV 2018 9
Tasks: Data Augmentation

Generative models that learn disentangled representations for different
factors of variation in an image can be very useful for targeted data
augmentation. By sampling from the disentangled latent subspace of interest, we
can efficiently generate new data necessary for a particular task. Learning
disentangled representations is a challenging problem, especially when certain
factors of variation are difficult to label. In this paper, we introduce a
novel architecture that disentangles the latent space into two complementary
subspaces by using only weak supervision in form of pairwise similarity labels.
Inspired by the recent success of cycle-consistent adversarial architectures,
we use cycle-consistency in a variational auto-encoder framework. Our
non-adversarial approach is in contrast with the recent works that combine
adversarial training with auto-encoders to disentangle representations. We show
compelling results of disentangled latent subspaces on three datasets and
compare with recent works that leverage adversarial training.
Interaction-Aware Probabilistic Behavior Prediction in Urban Environments
Authors: Jens Schulz, Constantin Hubmann, Julian Löchner, Darius Burschka
Date: 2018-04-27 00:00:00
Tasks: Autonomous Driving, Prediction

Planning for autonomous driving in complex, urban scenarios requires accurate
prediction of the trajectories of surrounding traffic participants. Their
future behavior depends on their route intentions, the road-geometry, traffic
rules and mutual interaction, resulting in interdependencies between their
trajectories. We present a probabilistic prediction framework based on a
dynamic Bayesian network, which represents the state of the complete scene
including all agents and respects the aforementioned dependencies. We propose
Markovian, context-dependent motion models to define the interaction-aware
behavior of drivers. At first, the state of the dynamic Bayesian network is
estimated over time by tracking the single agents via sequential Monte Carlo
inference. Secondly, we perform a probabilistic forward simulation of the
network's estimated belief state to generate the different combinatorial scene
developments. This provides the corresponding trajectories for the set of
possible, future scenes. Our framework can handle various road layouts and
number of traffic participants. We evaluate the approach in online simulations
and real-world scenarios. It is shown that our interaction-aware prediction
outperforms interaction-unaware physics- and map-based approaches.