pwc-archive/papers-with-abstracts huggingface dataset:
Lexicographically Ordered Multi-Objective Clustering
Authors: Sainyam Galhotra, Sandhya Saisubramanian, Shlomo Zilberstein
Date: 2019-03-02 00:00:00
Tasks: Clustering

We introduce a rich model for multi-objective clustering with lexicographic
ordering over objectives and a slack. The slack denotes the allowed
multiplicative deviation from the optimal objective value of the higher
priority objective to facilitate improvement in lower-priority objectives. We
then propose an algorithm called Zeus to solve this class of problems, which is
characterized by a makeshift function. The makeshift fine tunes the clusters
formed by the processed objectives so as to improve the clustering with respect
to the unprocessed objectives, given the slack. We present makeshift for
solving three different classes of objectives and analyze their solution
guarantees. Finally, we empirically demonstrate the effectiveness of our
approach on three applications using real-world data.
A Formal Framework for Robot Construction Problems: A Hybrid Planning Approach
Authors: Faseeh Ahmad, Esra Erdem, Volkan Patoglu
Date: 2019-03-02 00:00:00

We study robot construction problems where multiple autonomous robots
rearrange stacks of prefabricated blocks to build stable structures. These
problems are challenging due to ramifications of actions, true concurrency, and
requirements of supportedness of blocks by other blocks and stability of the
structure at all times. We propose a formal hybrid planning framework to solve
a wide range of robot construction problems, based on Answer Set Programming.
This framework not only decides for a stable final configuration of the
structure, but also computes the order of manipulation tasks for multiple
autonomous robots to build the structure from an initial configuration, while
simultaneously ensuring the stability, supportedness and other desired
properties of the partial construction at each step of the plan. We prove the
soundness and completeness of our formal method with respect to these
properties. We introduce a set of challenging robot construction benchmark
instances, including bridge building and stack overhanging scenarios, discuss
the usefulness of our framework over these instances, and demonstrate the
applicability of our method using a bimanual Baxter robot.
Automating Predictive Modeling Process using Reinforcement Learning
Authors: Udayan Khurana, Horst Samulowitz
Date: 2019-03-02 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty, Imputation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

Building a good predictive model requires an array of activities such as data
imputation, feature transformations, estimator selection, hyper-parameter
search and ensemble construction. Given the large, complex and heterogenous
space of options, off-the-shelf optimization methods are infeasible for
realistic response times. In practice, much of the predictive modeling process
is conducted by experienced data scientists, who selectively make use of
available tools. Over time, they develop an understanding of the behavior of
operators, and perform serial decision making under uncertainty, colloquially
referred to as educated guesswork. With an unprecedented demand for application
of supervised machine learning, there is a call for solutions that
automatically search for a good combination of parameters across these tasks to
minimize the modeling error. We introduce a novel system called APRL
(Autonomous Predictive modeler via Reinforcement Learning), that uses past
experience through reinforcement learning to optimize such sequential decision
making from within a set of diverse actions under a time constraint on a
previously unseen predictive learning problem. APRL actions are taken to
optimize the performance of a final ensemble. This is in contrast to other
systems, which maximize individual model accuracy first and create ensembles as
a disconnected post-processing step. As a result, APRL is able to reduce up to
71\% of classification error on average over a wide variety of problems.
Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research
Authors: Joel Z. Leibo, Edward Hughes, Marc Lanctot, Thore Graepel
Date: 2019-03-02 00:00:00

Evolution has produced a multi-scale mosaic of interacting adaptive units.
Innovations arise when perturbations push parts of the system away from stable
equilibria into new regimes where previously well-adapted solutions no longer
work. Here we explore the hypothesis that multi-agent systems sometimes display
intrinsic dynamics arising from competition and cooperation that provide a
naturally emergent curriculum, which we term an autocurriculum. The solution of
one social task often begets new social tasks, continually generating novel
challenges, and thereby promoting innovation. Under certain conditions these
challenges may become increasingly complex over time, demanding that agents
accumulate ever more innovations.
Speech Recognition with no speech or with noisy speech
Authors: Gautam Krishna, Co Tran, Jianguo Yu, Ahmed H. Tewfik
Date: 2019-03-02 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), EEG, Electroencephalogram (EEG), speech-recognition, Speech Recognition

The performance of automatic speech recognition systems(ASR) degrades in the
presence of noisy speech. This paper demonstrates that using
electroencephalography (EEG) can help automatic speech recognition systems
overcome performance loss in the presence of noise. The paper also shows that
distillation training of automatic speech recognition systems using EEG
features will increase their performance. Finally, we demonstrate the ability
to recognize words from EEG with no speech signal on a limited English
vocabulary with high accuracy.
A Regularized Approach to Sparse Optimal Policy in Reinforcement Learning
Authors: Xiang Li, Wenhao Yang, Zhihua Zhang
Date: 2019-03-02 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose and study a general framework for regularized Markov decision processes (MDPs) where the goal is to find an optimal policy that maximizes the expected discounted total reward plus a policy regularization term. The extant entropy-regularized MDPs can be cast into our framework. Moreover, under our framework, many regularization terms can bring multi-modality and sparsity, which are potentially useful in reinforcement learning. In particular, we present sufficient and necessary conditions that induce a sparse optimal policy. We also conduct a full mathematical analysis of the proposed regularized MDPs, including the optimality condition, performance error, and sparseness control. We provide a generic method to devise regularization forms and propose off-policy actor critic algorithms in complex environment settings. We empirically analyze the numerical properties of optimal policies and compare the performance of different sparse regularization forms in discrete and continuous environments.
Predicting and interpreting embeddings for out of vocabulary words in downstream tasks
Authors: Nicolas Garneau, Jean-Samuel Leboeuf, Luc Lamontagne
Date: 2019-03-02 00:00:00
Proceeding: WS 2018 11

We propose a novel way to handle out of vocabulary (OOV) words in downstream
natural language processing (NLP) tasks. We implement a network that predicts
useful embeddings for OOV words based on their morphology and on the context in
which they appear. Our model also incorporates an attention mechanism
indicating the focus allocated to the left context words, the right context
words or the word's characters, hence making the prediction more interpretable.
The model is a ``drop-in'' module that is jointly trained with the downstream
task's neural network, thus producing embeddings specialized for the task at
hand. When the task is mostly syntactical, we observe that our model aims most
of its attention on surface form characters. On the other hand, for tasks more
semantical, the network allocates more attention to the surrounding words. In
all our tests, the module helps the network to achieve better performances in
comparison to the use of simple random embeddings.
FRI -- Feature Relevance Intervals for Interpretable and Interactive Data Exploration
Authors: Lukas Pfannschmidt, Christina Göpfert, Ursula Neumann, Dominik Heider, Barbara Hammer
Date: 2019-03-02 00:00:00
Tasks: feature selection, General Classification, regression

Most existing feature selection methods are insufficient for analytic purposes as soon as high dimensional data or redundant sensor signals are dealt with since features can be selected due to spurious effects or correlations rather than causal effects. To support the finding of causal features in biomedical experiments, we hereby present FRI, an open source Python library that can be used to identify all-relevant variables in linear classification and (ordinal) regression problems. Using the recently proposed feature relevance method, FRI is able to provide the base for further general experimentation or in specific can facilitate the search for alternative biomarkers. It can be used in an interactive context, by providing model manipulation and visualization methods, or in a batch process as a filter method.
Virtual Representations for Iterative IoT Deployment
Authors: Sebastian R. Bader, Maria Maleshkova
Date: 2019-03-02 00:00:00

A central vision of the Internet of Things is the representation of the
physical world in a consistent virtual environment. Especially in the context
of smart factories the connection of the different, heterogeneous production
modules through a digital shop floor promises faster conversion rates,
data-driven maintenance or automated machine configurations for use cases,
which have not been known at design time. Nevertheless, these scenarios demand
IoT representations of all participating machines and components, which
requires high installation efforts and hardware adjustments.
  We propose an incremental process for bringing the shop floor closer to the
IoT vision. Currently the majority of systems, components or parts are not yet
connected with the internet and might not even provide the possibility to be
technically equipped with sensors. However, those could be essential parts for
a realistic digital shop floor representation. We, therefore, propose Virtual
Representations, which are capable of independently calculating a physical
object's condition by dynamically collecting and interpreting already available
data through RESTful Web APIs. The internal logic of such Virtual
Representations are further adjustable at runtime, since changes to its
respective physical object, its environment or updates to the resource itself
should not cause any downtime.
Efficient Reinforcement Learning for StarCraft by Abstract Forward Models and Transfer Learning
Authors: Ruo-Ze Liu, Haifeng Guo, Xiaozhong Ji, Yang Yu, Zhen-Jia Pang, Zitai Xiao, Yuzhou Wu, Tong Lu
Date: 2019-03-02 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Starcraft, Starcraft II, Transfer Learning

Injecting human knowledge is an effective way to accelerate reinforcement learning (RL). However, these methods are underexplored. This paper presents our discovery that an abstract forward model (thought-game (TG)) combined with transfer learning (TL) is an effective way. We take StarCraft II as our study environment. With the help of a designed TG, the agent can learn a 99% win-rate on a 64x64 map against the Level-7 built-in AI, using only 1.08 hours in a single commercial machine. We also show that the TG method is not as restrictive as it was thought to be. It can work with roughly designed TGs, and can also be useful when the environment changes. Comparing with previous model-based RL, we show TG is more effective. We also present a TG hypothesis that gives the influence of different fidelity levels of TG. For real games that have unequal state and action spaces, we proposed a novel XfrNet of which usefulness is validated while achieving a 90% win-rate against the cheating Level-10 AI. We argue that the TG method might shed light on further studies of efficient RL with human knowledge.
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics Network
Authors: Xihan Li, Jia Zhang, Jiang Bian, Yunhai Tong, Tie-Yan Liu
Date: 2019-03-02 00:00:00
Tasks: Combinatorial Optimization, Management, Multi-agent Reinforcement Learning, Reinforcement Learning, Reinforcement Learning (RL)

Resource balancing within complex transportation networks is one of the most
important problems in real logistics domain. Traditional solutions on these
problems leverage combinatorial optimization with demand and supply
forecasting. However, the high complexity of transportation routes, severe
uncertainty of future demand and supply, together with non-convex business
constraints make it extremely challenging in the traditional resource
management field. In this paper, we propose a novel sophisticated multi-agent
reinforcement learning approach to address these challenges. In particular,
inspired by the externalities especially the interactions among resource
agents, we introduce an innovative cooperative mechanism for state and reward
design resulting in more effective and efficient transportation. Extensive
experiments on a simulated ocean transportation service demonstrate that our
new approach can stimulate cooperation among agents and lead to much better
performance. Compared with traditional solutions based on combinatorial
optimization, our approach can give rise to a significant improvement in terms
of both performance and stability.
Constant-Delay Enumeration for Nondeterministic Document Spanners
Authors: Antoine Amarilli, Pierre Bourhis, Stefan Mengel, Matthias Niewerth
Date: 2018-07-24 00:00:00

We consider the information extraction framework known as document spanners, and study the problem of efficiently computing the results of the extraction from an input document, where the extraction task is described as a sequential variable-set automaton (VA). We pose this problem in the setting of enumeration algorithms, where we can first run a preprocessing phase and must then produce the results with a small delay between any two consecutive results. Our goal is to have an algorithm which is tractable in combined complexity, i.e., in the sizes of the input document and the VA; while ensuring the best possible data complexity bounds in the input document size, i.e., constant delay in the document size. Several recent works at PODS'18 proposed such algorithms but with linear delay in the document size or with an exponential dependency in size of the (generally nondeterministic) input VA. In particular, Florenzano et al. suggest that our desired runtime guarantees cannot be met for general sequential VAs. We refute this and show that, given a nondeterministic sequential VA and an input document, we can enumerate the mappings of the VA on the document with the following bounds: the preprocessing is linear in the document size and polynomial in the size of the VA, and the delay is independent of the document and polynomial in the size of the VA. The resulting algorithm thus achieves tractability in combined complexity and the best possible data complexity bounds. Moreover, it is rather easy to describe, in particular for the restricted case of so-called extended VAs.
neuralRank: Searching and ranking ANN-based model repositories
Authors: Nirmit Desai, Linsong Chu, Raghu K. Ganti, Sebastian Stein, Mudhakar Srivatsa
Date: 2019-03-02 00:00:00
Tasks: Transfer Learning

Widespread applications of deep learning have led to a plethora of
pre-trained neural network models for common tasks. Such models are often
adapted from other models via transfer learning. The models may have varying
training sets, training algorithms, network architectures, and
hyper-parameters. For a given application, what isthe most suitable model in a
model repository? This is a critical question for practical deployments but it
has not received much attention. This paper introduces the novel problem of
searching and ranking models based on suitability relative to a target dataset
and proposes a ranking algorithm called \textit{neuralRank}. The key idea
behind this algorithm is to base model suitability on the discriminating power
of a model, using a novel metric to measure it. With experimental results on
the MNIST, Fashion, and CIFAR10 datasets, we demonstrate that (1) neuralRank is
independent of the domain, the training set, or the network architecture and
(2) that the models ranked highly by neuralRank ranking tend to have higher
model accuracy in practice.
Strong homotopy of digitally continuous functions
Authors: P. Christopher Staecker
Date: 2019-03-02 00:00:00

We introduce a new type of homotopy relation for digitally continuous
functions which we call ``strong homotopy.'' Both digital homotopy and strong
homotopy are natural digitizations of classical topological homotopy: the
difference between them is analogous to the difference between digital
4-adjacency and 8-adjacency in the plane.
  We explore basic properties of strong homotopy, and give some equivalent
characterizations. In particular we show that strong homotopy is related to
``punctuated homotopy,'' in which the function changes by only one point in
each homotopy time step.
  We also show that strongly homotopic maps always have the same induced
homomorphisms in the digital homology theory. This is not generally true for
digitally homotopic maps, though we do show that it is true for any homotopic
selfmaps on the digital cycle $C_n$ with $n\ge 4$.
  We also define and consider strong homotopy equivalence of digital images.
Using some computer assistance, we produce a catalog of all small digital
images up to strong homotopy equivalence. We also briefly consider pointed
strong homotopy equivalence, and give an example of a pointed contractible
image which is not pointed strongly contractible.
Deep Optimization model for Screen Content Image Quality Assessment using Neural Networks
Authors: Xuhao Jiang, Liquan Shen, Guorui Feng, Liangwei Yu, Ping An
Date: 2019-03-02 00:00:00
Tasks: Full reference image quality assessment, Full-Reference Image Quality Assessment, Image Quality Assessment, L2 Regularization, NR-IQA

In this paper, we propose a novel quadratic optimized model based on the deep
convolutional neural network (QODCNN) for full-reference and no-reference
screen content image (SCI) quality assessment. Unlike traditional CNN methods
taking all image patches as training data and using average quality pooling,
our model is optimized to obtain a more effective model including three steps.
In the first step, an end-to-end deep CNN is trained to preliminarily predict
the image visual quality, and batch normalized (BN) layers and l2
regularization are employed to improve the speed and performance of network
fitting. For second step, the pretrained model is fine-tuned to achieve better
performance under analysis of the raw training data. An adaptive weighting
method is proposed in the third step to fuse local quality inspired by the
perceptual property of the human visual system (HVS) that the HVS is sensitive
to image patches containing texture and edge information. The novelty of our
algorithm can be concluded as follows: 1) with the consideration of correlation
between local quality and subjective differential mean opinion score (DMOS),
the Euclidean distance is utilized to measure effectiveness of image patches,
and the pretrained model is fine-tuned with more effective training data; 2) an
adaptive pooling approach is employed to fuse patch quality of textual and
pictorial regions, whose feature only extracted from distorted images owns
strong noise robust and effects on both FR and NR IQA; 3) Considering the
characteristics of SCIs, a deep and valid network architecture is designed for
both NR and FR visual quality evaluation of SCIs. Experimental results verify
that our model outperforms both current no-reference and full-reference image
quality assessment methods on the benchmark screen content image quality
assessment database (SIQAD).
Matrix Completion via Nonconvex Regularization: Convergence of the Proximal Gradient Algorithm
Authors: Fei Wen, Rendong Ying, Peilin Liu, Trieu-Kien Truong
Date: 2019-03-02 00:00:00
Tasks: Matrix Completion

Matrix completion has attracted much interest in the past decade in machine
learning and computer vision. For low-rank promotion in matrix completion, the
nuclear norm penalty is convenient due to its convexity but has a bias problem.
Recently, various algorithms using nonconvex penalties have been proposed,
among which the proximal gradient descent (PGD) algorithm is one of the most
efficient and effective. For the nonconvex PGD algorithm, whether it converges
to a local minimizer and its convergence rate are still unclear. This work
provides a nontrivial analysis on the PGD algorithm in the nonconvex case.
Besides the convergence to a stationary point for a generalized nonconvex
penalty, we provide more deep analysis on a popular and important class of
nonconvex penalties which have discontinuous thresholding functions. For such
penalties, we establish the finite rank convergence, convergence to restricted
strictly local minimizer and eventually linear convergence rate of the PGD
algorithm. Meanwhile, convergence to a local minimizer has been proved for the
hard-thresholding penalty. Our result is the first shows that, nonconvex
regularized matrix completion only has restricted strictly local minimizers,
and the PGD algorithm can converge to such minimizers with eventually linear
rate under certain conditions. Illustration of the PGD algorithm via
experiments has also been provided. Code is available at
https://github.com/FWen/nmc.
Fine-Grained Semantic Segmentation of Motion Capture Data using Dilated Temporal Fully-Convolutional Networks
Authors: Noshaba Cheema, Somayeh Hosseini, Janis Sprenger, Erik Herrmann, Han Du, Klaus Fischer, Philipp Slusallek
Date: 2019-03-02 00:00:00
Tasks: Action Segmentation, Image Segmentation, Motion Segmentation, Motion Synthesis, Segmentation, Semantic Segmentation

Human motion capture data has been widely used in data-driven character
animation. In order to generate realistic, natural-looking motions, most
data-driven approaches require considerable efforts of pre-processing,
including motion segmentation and annotation. Existing (semi-) automatic
solutions either require hand-crafted features for motion segmentation or do
not produce the semantic annotations required for motion synthesis and building
large-scale motion databases. In addition, human labeled annotation data
suffers from inter- and intra-labeler inconsistencies by design. We propose a
semi-automatic framework for semantic segmentation of motion capture data based
on supervised machine learning techniques. It first transforms a motion capture
sequence into a ``motion image'' and applies a convolutional neural network for
image segmentation. Dilated temporal convolutions enable the extraction of
temporal information from a large receptive field. Our model outperforms two
state-of-the-art models for action segmentation, as well as a popular network
for sequence modeling. Most of all, our method is very robust under noisy and
inaccurate training labels and thus can handle human errors during the labeling
process.