pwc-archive/papers-with-abstracts huggingface dataset:
Extracting Ontological Knowledge from Textual Descriptions
Authors: Kevin Alex Mathews, P. Sreenivasa Kumar
Date: 2017-09-25 00:00:00
Tasks: Sentence

Authoring of OWL-DL ontologies is intellectually challenging and to make this
process simpler, many systems accept natural language text as input. A
text-based ontology authoring approach can be successful only when it is
combined with an effective method for extracting ontological axioms from text.
Extracting axioms from unrestricted English input is a substantially
challenging task due to the richness of the language. Controlled natural
languages (CNLs) have been proposed in this context and these tend to be highly
restrictive. In this paper, we propose a new CNL called TEDEI (TExtual
DEscription Identifier) whose grammar is inspired by the different ways OWL-DL
constructs are expressed in English. We built a system that transforms TEDEI
sentences into corresponding OWL-DL axioms. Now, ambiguity due to different
possible lexicalizations of sentences and semantic ambiguity present in
sentences are challenges in this context. We find that the best way to handle
these challenges is to construct axioms corresponding to alternative
formalizations of the sentence so that the end-user can make an appropriate
choice. The output is compared against human-authored axioms and in substantial
number of cases, human-authored axiom is indeed one of the alternatives given
by the system. The proposed system substantially enhances the types of sentence
structures that can be used for ontology authoring.
Resilient Learning-Based Control for Synchronization of Passive Multi-Agent Systems under Attack
Authors: Arash Rahnama, Panos J. Antsaklis
Date: 2017-09-28 00:00:00
Tasks: Adversarial Attack

In this paper, we show synchronization for a group of output passive agents
that communicate with each other according to an underlying communication graph
to achieve a common goal. We propose a distributed event-triggered control
framework that will guarantee synchronization and considerably decrease the
required communication load on the band-limited network. We define a general
Byzantine attack on the event-triggered multi-agent network system and
characterize its negative effects on synchronization. The Byzantine agents are
capable of intelligently falsifying their data and manipulating the underlying
communication graph by altering their respective control feedback weights. We
introduce a decentralized detection framework and analyze its steady-state and
transient performances. We propose a way of identifying individual Byzantine
neighbors and a learning-based method of estimating the attack parameters.
Lastly, we propose learning-based control approaches to mitigate the negative
effects of the adversarial attack.
Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations
Authors: Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, Giulia Vezzani, John Schulman, Emanuel Todorov, Sergey Levine
Date: 2017-09-28 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Dexterous multi-fingered hands are extremely versatile and provide a generic
way to perform a multitude of tasks in human-centric environments. However,
effectively controlling them remains challenging due to their high
dimensionality and large number of potential contacts. Deep reinforcement
learning (DRL) provides a model-agnostic approach to control complex dynamical
systems, but has not been shown to scale to high-dimensional dexterous
manipulation. Furthermore, deployment of DRL on physical systems remains
challenging due to sample inefficiency. Consequently, the success of DRL in
robotics has thus far been limited to simpler manipulators and tasks. In this
work, we show that model-free DRL can effectively scale up to complex
manipulation tasks with a high-dimensional 24-DoF hand, and solve them from
scratch in simulated experiments. Furthermore, with the use of a small number
of human demonstrations, the sample complexity can be significantly reduced,
which enables learning with sample sizes equivalent to a few hours of robot
experience. The use of demonstrations result in policies that exhibit very
natural movements and, surprisingly, are also substantially more robust.
Bayesian Multi Plate High Throughput Screening of Compounds
Authors: Ivo D. Shterev, David B. Dunson, Cliburn Chan, Gregory D. Sempowski
Date: 2017-09-28 00:00:00
Tasks: Drug Discovery, Specificity, Vocal Bursts Intensity Prediction

High throughput screening of compounds (chemicals) is an essential part of
drug discovery [7], involving thousands to millions of compounds, with the
purpose of identifying candidate hits. Most statistical tools, including the
industry standard B-score method, work on individual compound plates and do not
exploit cross-plate correlation or statistical strength among plates. We
present a new statistical framework for high throughput screening of compounds
based on Bayesian nonparametric modeling. The proposed approach is able to
identify candidate hits from multiple plates simultaneously, sharing
statistical strength among plates and providing more robust estimates of
compound activity. It can flexibly accommodate arbitrary distributions of
compound activities and is applicable to any plate geometry. The algorithm
provides a principled statistical approach for hit identification and false
discovery rate control. Experiments demonstrate significant improvements in hit
identification sensitivity and specificity over the B-score method, which is
highly sensitive to threshold choice. The framework is implemented as an
efficient R extension package BHTSpack and is suitable for large scale data
sets.
Sparse Hierarchical Regression with Polynomials
Authors: Dimitris Bertsimas, Bart Van Parys
Date: 2017-09-28 00:00:00
Tasks: regression

We present a novel method for exact hierarchical sparse polynomial
regression. Our regressor is that degree $r$ polynomial which depends on at
most $k$ inputs, counting at most $\ell$ monomial terms, which minimizes the
sum of the squares of its prediction errors. The previous hierarchical sparse
specification aligns well with modern big data settings where many inputs are
not relevant for prediction purposes and the functional complexity of the
regressor needs to be controlled as to avoid overfitting. We present a two-step
approach to this hierarchical sparse regression problem. First, we discard
irrelevant inputs using an extremely fast input ranking heuristic. Secondly, we
take advantage of modern cutting plane methods for integer optimization to
solve our resulting reduced hierarchical $(k, \ell)$-sparse problem exactly.
The ability of our method to identify all $k$ relevant inputs and all $\ell$
monomial terms is shown empirically to experience a phase transition.
Crucially, the same transition also presents itself in our ability to reject
all irrelevant features and monomials as well. In the regime where our method
is statistically powerful, its computational complexity is interestingly on par
with Lasso based heuristics. The presented work fills a void in terms of a lack
of powerful disciplined nonlinear sparse regression methods in high-dimensional
settings. Our method is shown empirically to scale to regression problems with
$n\approx 10,000$ observations for input dimension $p\approx 1,000$.
Sparse High-Dimensional Regression: Exact Scalable Algorithms and Phase Transitions
Authors: Dimitris Bertsimas, Bart Van Parys
Date: 2017-09-28 00:00:00
Tasks: regression, Vocal Bursts Intensity Prediction

We present a novel binary convex reformulation of the sparse regression
problem that constitutes a new duality perspective. We devise a new cutting
plane method and provide evidence that it can solve to provable optimality the
sparse regression problem for sample sizes n and number of regressors p in the
100,000s, that is two orders of magnitude better than the current state of the
art, in seconds. The ability to solve the problem for very high dimensions
allows us to observe new phase transition phenomena. Contrary to traditional
complexity theory which suggests that the difficulty of a problem increases
with problem size, the sparse regression problem has the property that as the
number of samples $n$ increases the problem becomes easier in that the solution
recovers 100% of the true signal, and our approach solves the problem extremely
fast (in fact faster than Lasso), while for small number of samples n, our
approach takes a larger amount of time to solve the problem, but importantly
the optimal solution provides a statistically more relevant regressor. We argue
that our exact sparse regression approach presents a superior alternative over
heuristic methods available at present.
Meta-SGD: Learning to Learn Quickly for Few-Shot Learning
Authors: Zhenguo Li, Fengwei Zhou, Fei Chen, Hang Li
Date: 2017-07-31 00:00:00
Tasks: Few-Shot Learning, Meta-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Few-shot learning is challenging for learning algorithms that learn each task
in isolation and from scratch. In contrast, meta-learning learns from many
related tasks a meta-learner that can learn a new task more accurately and
faster with fewer examples, where the choice of meta-learners is crucial. In
this paper, we develop Meta-SGD, an SGD-like, easily trainable meta-learner
that can initialize and adapt any differentiable learner in just one step, on
both supervised learning and reinforcement learning. Compared to the popular
meta-learner LSTM, Meta-SGD is conceptually simpler, easier to implement, and
can be learned more efficiently. Compared to the latest meta-learner MAML,
Meta-SGD has a much higher capacity by learning to learn not just the learner
initialization, but also the learner update direction and learning rate, all in
a single meta-learning process. Meta-SGD shows highly competitive performance
for few-shot learning on regression, classification, and reinforcement
learning.
Premise Selection for Theorem Proving by Deep Graph Embedding
Authors: Mingzhe Wang, Yihe Tang, Jian Wang, Jia Deng
Date: 2017-09-28 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Automated Theorem Proving, General Classification, Graph Embedding

We propose a deep learning-based approach to the problem of premise
selection: selecting mathematical statements relevant for proving a given
conjecture. We represent a higher-order logic formula as a graph that is
invariant to variable renaming but still fully preserves syntactic and semantic
information. We then embed the graph into a vector via a novel embedding method
that preserves the information of edge ordering. Our approach achieves
state-of-the-art results on the HolStep dataset, improving the classification
accuracy from 83% to 90.3%.
Structured Embedding Models for Grouped Data
Authors: Maja Rudolph, Francisco Ruiz, Susan Athey, David Blei
Date: 2017-09-28 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Word Embeddings

Word embeddings are a powerful approach for analyzing language, and
exponential family embeddings (EFE) extend them to other types of data. Here we
develop structured exponential family embeddings (S-EFE), a method for
discovering embeddings that vary across related groups of data. We study how
the word usage of U.S. Congressional speeches varies across states and party
affiliation, how words are used differently across sections of the ArXiv, and
how the co-purchase patterns of groceries can vary across seasons. Key to the
success of our method is that the groups share statistical information. We
develop two sharing strategies: hierarchical modeling and amortization. We
demonstrate the benefits of this approach in empirical studies of speeches,
abstracts, and shopping baskets. We show how S-EFE enables group-specific
interpretation of word usage, and outperforms EFE in predicting held-out data.
User and Developer Interaction with Editable and Readable Ontologies
Authors: Aisha Blfgeh, Phillip Lord
Date: 2017-09-26 00:00:00
Tasks: Translation

The process of building ontologies is a difficult task that involves
collaboration between ontology developers and domain experts and requires an
ongoing interaction between them. This collaboration is made more difficult,
because they tend to use different tool sets, which can hamper this
interaction. In this paper, we propose to decrease this distance between domain
experts and ontology developers by creating more readable forms of ontologies,
and further to enable editing in normal office environments. Building on a
programmatic ontology development environment, such as Tawny-OWL, we are now
able to generate these readable/editable from the raw ontological source and
its embedded comments. We have this translation to HTML for reading; this
environment provides rich hyperlinking as well as active features such as
hiding the source code in favour of comments. We are now working on translation
to a Word document that also enables editing. Taken together this should
provide a significant new route for collaboration between the ontologist and
domain specialist.
Deep Learning Assisted Heuristic Tree Search for the Container Pre-marshalling Problem
Authors: André Hottung, Shunji Tanaka, Kevin Tierney
Date: 2017-09-28 00:00:00

The container pre-marshalling problem (CPMP) is concerned with the re-ordering of containers in container terminals during off-peak times so that containers can be quickly retrieved when the port is busy. The problem has received significant attention in the literature and is addressed by a large number of exact and heuristic methods. Existing methods for the CPMP heavily rely on problem-specific components (e.g., proven lower bounds) that need to be developed by domain experts with knowledge of optimization techniques and a deep understanding of the problem at hand. With the goal to automate the costly and time-intensive design of heuristics for the CPMP, we propose a new method called Deep Learning Heuristic Tree Search (DLTS). It uses deep neural networks to learn solution strategies and lower bounds customized to the CPMP solely through analyzing existing (near-) optimal solutions to CPMP instances. The networks are then integrated into a tree search procedure to decide which branch to choose next and to prune the search tree. DLTS produces the highest quality heuristic solutions to the CPMP to date with gaps to optimality below 2% on real-world sized instances.
Efficient and Robust Pedestrian Detection using Deep Learning for Human-Aware Navigation
Authors: Andre Mateus, David Ribeiro, Pedro Miraldo, Jacinto C. Nascimento
Date: 2016-07-15 00:00:00
Tasks: Human Detection, Pedestrian Detection

This paper addresses the problem of Human-Aware Navigation (HAN), using multi
camera sensors to implement a vision-based person tracking system. The main
contributions of this paper are as follows: a novel and efficient Deep Learning
person detection and a standardization of human-aware constraints. In the first
stage of the approach, we propose to cascade the Aggregate Channel Features
(ACF) detector with a deep Convolutional Neural Network (CNN) to achieve fast
and accurate Pedestrian Detection (PD). Regarding the human awareness (that can
be defined as constraints associated with the robot's motion), we use a mixture
of asymmetric Gaussian functions, to define the cost functions associated to
each constraint. Both methods proposed herein are evaluated individually to
measure the impact of each of the components. The final solution (including
both the proposed pedestrian detection and the human-aware constraints) is
tested in a typical domestic indoor scenario, in four distinct experiments. The
results show that the robot is able to cope with human-aware constraints,
defined after common proxemics and social rules.
Query-adaptive Video Summarization via Quality-aware Relevance Estimation
Authors: Arun Balajee Vasudevan, Michael Gygli, Anna Volokitin, Luc van Gool
Date: 2017-05-01 00:00:00
Tasks: Diversity, Video Summarization

Although the problem of automatic video summarization has recently received a
lot of attention, the problem of creating a video summary that also highlights
elements relevant to a search query has been less studied. We address this
problem by posing query-relevant summarization as a video frame subset
selection problem, which lets us optimise for summaries which are
simultaneously diverse, representative of the entire video, and relevant to a
text query. We quantify relevance by measuring the distance between frames and
queries in a common textual-visual semantic embedding space induced by a neural
network. In addition, we extend the model to capture query-independent
properties, such as frame quality. We compare our method against previous state
of the art on textual-visual embeddings for thumbnail selection and show that
our model outperforms them on relevance prediction. Furthermore, we introduce a
new dataset, annotated with diversity and query-specific relevance labels. On
this dataset, we train and test our complete model for video summarization and
show that it outperforms standard baselines such as Maximal Marginal Relevance.
HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis
Authors: Xihui Liu, Haiyu Zhao, Maoqing Tian, Lu Sheng, Jing Shao, Shuai Yi, Junjie Yan, Xiaogang Wang
Date: 2017-09-28 00:00:00
Proceeding: ICCV 2017 10
Tasks: Attribute, Pedestrian Attribute Recognition, Person Re-Identification

Pedestrian analysis plays a vital role in intelligent video surveillance and
is a key component for security-centric computer vision systems. Despite that
the convolutional neural networks are remarkable in learning discriminative
features from images, the learning of comprehensive features of pedestrians for
fine-grained tasks remains an open problem. In this study, we propose a new
attention-based deep neural network, named as HydraPlus-Net (HP-net), that
multi-directionally feeds the multi-level attention maps to different feature
layers. The attentive deep features learned from the proposed HP-net bring
unique advantages: (1) the model is capable of capturing multiple attentions
from low-level to semantic-level, and (2) it explores the multi-scale
selectiveness of attentive features to enrich the final feature representations
for a pedestrian image. We demonstrate the effectiveness and generality of the
proposed HP-net for pedestrian analysis on two tasks, i.e. pedestrian attribute
recognition and person re-identification. Intensive experimental results have
been provided to prove that the HP-net outperforms the state-of-the-art methods
on various datasets.
Comicolorization: Semi-Automatic Manga Colorization
Authors: Chie Furusawa, Kazuyuki Hiroshiba, Keisuke Ogaki, Yuri Odagiri
Date: 2017-06-21 00:00:00
Tasks: Colorization

We developed "Comicolorization", a semi-automatic colorization system for
manga images. Given a monochrome manga and reference images as inputs, our
system generates a plausible color version of the manga. This is the first work
to address the colorization of an entire manga title (a set of manga pages).
Our method colorizes a whole page (not a single panel) semi-automatically, with
the same color for the same character across multiple panels. To colorize the
target character by the color from the reference image, we extract a color
feature from the reference and feed it to the colorization network to help the
colorization. Our approach employs adversarial loss to encourage the effect of
the color features. Optionally, our tool allows users to revise the
colorization result interactively. By feeding the color features to our deep
colorization network, we accomplish colorization of the entire manga using the
desired colors for each panel.
Introduction to the "Industrial Benchmark"
Authors: Daniel Hein, Alexander Hentschel, Volkmar Sterzing, Michel Tokic, Steffen Udluft
Date: 2016-10-12 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

A novel reinforcement learning benchmark, called Industrial Benchmark, is
introduced. The Industrial Benchmark aims at being be realistic in the sense,
that it includes a variety of aspects that we found to be vital in industrial
applications. It is not designed to be an approximation of any real system, but
to pose the same hardness and complexity.
Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approach
Authors: Hu Han, Anil K. Jain, Fang Wang, Shiguang Shan, Xilin Chen
Date: 2017-06-03 00:00:00
Tasks: Attribute, Facial Attribute Classification, MORPH, Multi-Task Learning, Representation Learning, Retrieval

Face attribute estimation has many potential applications in video
surveillance, face retrieval, and social media. While a number of methods have
been proposed for face attribute estimation, most of them did not explicitly
consider the attribute correlation and heterogeneity (e.g., ordinal vs. nominal
and holistic vs. local) during feature representation learning. In this paper,
we present a Deep Multi-Task Learning (DMTL) approach to jointly estimate
multiple heterogeneous attributes from a single face image. In DMTL, we tackle
attribute correlation and heterogeneity with convolutional neural networks
(CNNs) consisting of shared feature learning for all the attributes, and
category-specific feature learning for heterogeneous attributes. We also
introduce an unconstrained face database (LFW+), an extension of public-domain
LFW, with heterogeneous demographic attributes (age, gender, and race) obtained
via crowdsourcing. Experimental results on benchmarks with multiple face
attributes (MORPH II, LFW+, CelebA, LFWA, and FotW) show that the proposed
approach has superior performance compared to state of the art. Finally,
evaluations on a public-domain face database (LAP) with a single attribute show
that the proposed approach has excellent generalization ability.
Efficient Convolutional Neural Network For Audio Event Detection
Authors: Matthias Meyer, Lukas Cavigelli, Lothar Thiele
Date: 2017-09-28 00:00:00
Tasks: Event Detection, General Classification

Wireless distributed systems as used in sensor networks, Internet-of-Things
and cyber-physical systems, impose high requirements on resource efficiency.
Advanced preprocessing and classification of data at the network edge can help
to decrease the communication demand and to reduce the amount of data to be
processed centrally. In the area of distributed acoustic sensing, the
combination of algorithms with a high classification rate and
resource-constraint embedded systems is essential. Unfortunately, algorithms
for acoustic event detection have a high memory and computational demand and
are not suited for execution at the network edge. This paper addresses these
aspects by applying structural optimizations to a convolutional neural network
for audio event detection to reduce the memory requirement by a factor of more
than 500 and the computational effort by a factor of 2.1 while performing 9.2%
better.
Adaptive Learning Rate via Covariance Matrix Based Preconditioning for Deep Neural Networks
Authors: Yasutoshi Ida, Yasuhiro Fujiwara, Sotetsu Iwamura
Date: 2016-05-31 00:00:00
Tasks: Stochastic Optimization

Adaptive learning rate algorithms such as RMSProp are widely used for
training deep neural networks. RMSProp offers efficient training since it uses
first order gradients to approximate Hessian-based preconditioning. However,
since the first order gradients include noise caused by stochastic
optimization, the approximation may be inaccurate. In this paper, we propose a
novel adaptive learning rate algorithm called SDProp. Its key idea is effective
handling of the noise by preconditioning based on covariance matrix. For
various neural networks, our approach is more efficient and effective than
RMSProp and its variant.