pwc-archive/papers-with-abstracts huggingface dataset:
From Social to Individuals: a Parsimonious Path of Multi-level Models for Crowdsourced Preference Aggregation
Authors: Qianqian Xu, Jiechao Xiong, Xiaochun Cao, Qingming Huang, Yuan YAO
Date: 2018-03-08 00:00:00

In crowdsourced preference aggregation, it is often assumed that all the
annotators are subject to a common preference or social utility function which
generates their comparison behaviors in experiments. However, in reality
annotators are subject to variations due to multi-criteria, abnormal, or a
mixture of such behaviors. In this paper, we propose a parsimonious
mixed-effects model, which takes into account both the fixed effect that the
majority of annotators follows a common linear utility model, and the random
effect that some annotators might deviate from the common significantly and
exhibit strongly personalized preferences. The key algorithm in this paper
establishes a dynamic path from the social utility to individual variations,
with different levels of sparsity on personalization. The algorithm is based on
the Linearized Bregman Iterations, which leads to easy parallel implementations
to meet the need of large-scale data analysis. In this unified framework, three
kinds of random utility models are presented, including the basic linear model
with L2 loss, Bradley-Terry model, and Thurstone-Mosteller model. The validity
of these multi-level models are supported by experiments with both simulated
and real-world datasets, which shows that the parsimonious multi-level models
exhibit improvements in both interpretability and predictive precision compared
with traditional HodgeRank.
IEOPF: An Active Contour Model for Image Segmentation with Inhomogeneities Estimated by Orthogonal Primary Functions
Authors: Chaolu Feng
Date: 2017-12-05 00:00:00
Tasks: ARC, Clustering, Image Segmentation, Semantic Segmentation

Image segmentation is still an open problem especially when intensities of
the interested objects are overlapped due to the presence of intensity
inhomogeneity (also known as bias field). To segment images with intensity
inhomogeneities, a bias correction embedded level set model is proposed where
Inhomogeneities are Estimated by Orthogonal Primary Functions (IEOPF). In the
proposed model, the smoothly varying bias is estimated by a linear combination
of a given set of orthogonal primary functions. An inhomogeneous intensity
clustering energy is then defined and membership functions of the clusters
described by the level set function are introduced to rewrite the energy as a
data term of the proposed model. Similar to popular level set methods, a
regularization term and an arc length term are also included to regularize and
smooth the level set function, respectively. The proposed model is then
extended to multichannel and multiphase patterns to segment colourful images
and images with multiple objects, respectively. It has been extensively tested
on both synthetic and real images that are widely used in the literature and
public BrainWeb and IBSR datasets. Experimental results and comparison with
state-of-the-art methods demonstrate that advantages of the proposed model in
terms of bias correction and segmentation accuracy.
Multi-objective evolution for 3D RTS Micro
Authors: Sushil J. Louis, Siming Liu
Date: 2018-03-08 00:00:00
Tasks: Real-Time Strategy Games, Starcraft

We attack the problem of controlling teams of autonomous units during
skirmishes in real-time strategy games. Earlier work had shown promise in
evolving control algorithm parameters that lead to high performance team
behaviors similar to those favored by good human players in real-time strategy
games like Starcraft. This algorithm specifically encoded parameterized kiting
and fleeing behaviors and the genetic algorithm evolved these parameter values.
In this paper we investigate using influence maps and potential fields alone to
compactly represent and control real-time team behavior for entities that can
maneuver in three dimensions. A two-objective fitness function that maximizes
damage done and minimizes damage taken guides our multi-objective evolutionary
algorithm. Preliminary results indicate that evolving friend and enemy unit
potential field parameters for distance, weapon characteristics, and entity
health suffice to produce complex, high performing, three-dimensional, team
tactics.
Autoencoder Node Saliency: Selecting Relevant Latent Representations
Authors: Ya Ju Fan
Date: 2017-11-21 00:00:00

The autoencoder is an artificial neural network model that learns hidden
representations of unlabeled data. With a linear transfer function it is
similar to the principal component analysis (PCA). While both methods use
weight vectors for linear transformations, the autoencoder does not come with
any indication similar to the eigenvalues in PCA that are paired with the
eigenvectors. We propose a novel supervised node saliency (SNS) method that
ranks the hidden nodes by comparing class distributions of latent
representations against a fixed reference distribution. The latent
representations of a hidden node can be described using a one-dimensional
histogram. We apply normalized entropy difference (NED) to measure the
"interestingness" of the histograms, and conclude a property for NED values to
identify a good classifying node. By applying our methods to real data sets, we
demonstrate the ability of SNS to explain what the trained autoencoders have
learned.
The Advantage of Doubling: A Deep Reinforcement Learning Approach to Studying the Double Team in the NBA
Authors: Jiaxuan Wang, Ian Fox, Jonathan Skaza, Nick Linck, Satinder Singh, Jenna Wiens
Date: 2018-03-08 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning

During the 2017 NBA playoffs, Celtics coach Brad Stevens was faced with a
difficult decision when defending against the Cavaliers: "Do you double and
risk giving up easy shots, or stay at home and do the best you can?" It's a
tough call, but finding a good defensive strategy that effectively incorporates
doubling can make all the difference in the NBA. In this paper, we analyze
double teaming in the NBA, quantifying the trade-off between risk and reward.
Using player trajectory data pertaining to over 643,000 possessions, we
identified when the ball handler was double teamed. Given these data and the
corresponding outcome (i.e., was the defense successful), we used deep
reinforcement learning to estimate the quality of the defensive actions. We
present qualitative and quantitative results summarizing our learned defensive
strategy for defending. We show that our policy value estimates are predictive
of points per possession and win percentage. Overall, the proposed framework
represents a step toward a more comprehensive understanding of defensive
strategies in the NBA.
Distributed Computation of Wasserstein Barycenters over Networks
Authors: César A. Uribe, Darina Dvinskikh, Pavel Dvurechensky, Alexander Gasnikov, Angelia Nedić
Date: 2018-03-08 00:00:00

We propose a new \cu{class-optimal} algorithm for the distributed computation
of Wasserstein Barycenters over networks. Assuming that each node in a graph
has a probability distribution, we prove that every node can reach the
barycenter of all distributions held in the network by using local interactions
compliant with the topology of the graph. We provide an estimate for the
minimum number of communication rounds required for the proposed method to
achieve arbitrary relative precision both in the optimality of the solution and
the consensus among all agents for undirected fixed networks.
Visual Explanations From Deep 3D Convolutional Neural Networks for Alzheimer's Disease Classification
Authors: Chengliang Yang, Anand Rangarajan, Sanjay Ranka
Date: 2018-03-07 00:00:00
Tasks: Classification, General Classification, Image Segmentation, Semantic Segmentation, Sensitivity

We develop three efficient approaches for generating visual explanations from
3D convolutional neural networks (3D-CNNs) for Alzheimer's disease
classification. One approach conducts sensitivity analysis on hierarchical 3D
image segmentation, and the other two visualize network activations on a
spatial map. Visual checks and a quantitative localization benchmark indicate
that all approaches identify important brain parts for Alzheimer's disease
diagnosis. Comparative analysis show that the sensitivity analysis based
approach has difficulty handling loosely distributed cerebral cortex, and
approaches based on visualization of activations are constrained by the
resolution of the convolutional layer. The complementarity of these methods
improves the understanding of 3D-CNNs in Alzheimer's disease classification
from different perspectives.
Towards Synthesizing Complex Programs from Input-Output Examples
Authors: Xinyun Chen, Chang Liu, Dawn Song
Date: 2017-06-05 00:00:00
Proceeding: ICLR 2018 1
Tasks: Program Synthesis, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In recent years, deep learning techniques have been developed to improve the
performance of program synthesis from input-output examples. Albeit its
significant progress, the programs that can be synthesized by state-of-the-art
approaches are still simple in terms of their complexity. In this work, we move
a significant step forward along this direction by proposing a new class of
challenging tasks in the domain of program synthesis from input-output
examples: learning a context-free parser from pairs of input programs and their
parse trees. We show that this class of tasks are much more challenging than
previously studied tasks, and the test accuracy of existing approaches is
almost 0%.
  We tackle the challenges by developing three novel techniques inspired by
three novel observations, which reveal the key ingredients of using deep
learning to synthesize a complex program. First, the use of a
non-differentiable machine is the key to effectively restrict the search space.
Thus our proposed approach learns a neural program operating a domain-specific
non-differentiable machine. Second, recursion is the key to achieve
generalizability. Thus, we bake-in the notion of recursion in the design of our
non-differentiable machine. Third, reinforcement learning is the key to learn
how to operate the non-differentiable machine, but it is also hard to train the
model effectively with existing reinforcement learning algorithms from a cold
boot. We develop a novel two-phase reinforcement learning-based search
algorithm to overcome this issue. In our evaluation, we show that using our
novel approach, neural parsing programs can be learned to achieve 100% test
accuracy on test inputs that are 500x longer than the training samples.
Translating Questions into Answers using DBPedia n-triples
Authors: Mihael Arcan
Date: 2018-03-07 00:00:00
Tasks: Question Answering

In this paper we present a question answering system using a neural network
to interpret questions learned from the DBpedia repository. We train a
sequence-to-sequence neural network model with n-triples extracted from the
DBpedia Infobox Properties. Since these properties do not represent the natural
language, we further used question-answer dialogues from movie subtitles.
Although the automatic evaluation shows a low overlap of the generated answers
compared to the gold standard set, a manual inspection of the showed promising
outcomes from the experiment for further work.
A Brandom-ian view of Reinforcement Learning towards strong-AI
Authors: Atrisha Sarkar
Date: 2018-03-07 00:00:00
Tasks: Philosophy, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The analytic philosophy of Robert Brandom, based on the ideas of pragmatism,
paints a picture of sapience, through inferentialism. In this paper, we present
a theory, that utilizes essential elements of Brandom's philosophy, towards the
objective of achieving strong-AI. We do this by connecting the constitutive
elements of reinforcement learning and the Game Of Giving and Asking For
Reasons. Further, following Brandom's prescriptive thoughts, we restructure the
popular reinforcement learning algorithm A3C, and show that RL algorithms can
be tuned towards the objective of strong-AI.
Simultaneous Task Allocation and Planning Under Uncertainty
Authors: Fatma Faruq, Bruno Lacerda, Nick Hawes, David Parker
Date: 2018-03-07 00:00:00

We propose novel techniques for task allocation and planning in multi-robot
systems operating in uncertain environments. Task allocation is performed
simultaneously with planning, which provides more detailed information about
individual robot behaviour, but also exploits independence between tasks to do
so efficiently. We use Markov decision processes to model robot behaviour and
linear temporal logic to specify tasks and safety constraints. Building upon
techniques and tools from formal verification, we show how to generate a
sequence of multi-robot policies, iteratively refining them to reallocate tasks
if individual robots fail, and providing probabilistic guarantees on the
performance (and safe operation) of the team of robots under the resulting
policy. We implement our approach and evaluate it on a benchmark multi-robot
example.
An efficient framework for learning sentence representations
Authors: Lajanugen Logeswaran, Honglak Lee
Date: 2018-03-07 00:00:00
Proceeding: ICLR 2018 1
Tasks: General Classification, Representation Learning, Sentence

In this work we propose a simple and efficient framework for learning
sentence representations from unlabelled data. Drawing inspiration from the
distributional hypothesis and recent work on learning sentence representations,
we reformulate the problem of predicting the context in which a sentence
appears as a classification problem. Given a sentence and its context, a
classifier distinguishes context sentences from other contrastive sentences
based on their vector representations. This allows us to efficiently learn
different types of encoding functions, and we show that the model learns
high-quality sentence representations. We demonstrate that our sentence
representations outperform state-of-the-art unsupervised and supervised
representation learning methods on several downstream NLP tasks that involve
understanding sentence semantics while achieving an order of magnitude speedup
in training time.
WNGrad: Learn the Learning Rate in Gradient Descent
Authors: Xiaoxia Wu, Rachel Ward, Léon Bottou
Date: 2018-03-07 00:00:00

Adjusting the learning rate schedule in stochastic gradient methods is an important unresolved problem which requires tuning in practice. If certain parameters of the loss function such as smoothness or strong convexity constants are known, theoretical learning rate schedules can be applied. However, in practice, such parameters are not known, and the loss function of interest is not convex in any case. The recently proposed batch normalization reparametrization is widely adopted in most neural network architectures today because, among other advantages, it is robust to the choice of Lipschitz constant of the gradient in loss function, allowing one to set a large learning rate without worry. Inspired by batch normalization, we propose a general nonlinear update rule for the learning rate in batch and stochastic gradient descent so that the learning rate can be initialized at a high value, and is subsequently decreased according to gradient observations along the way. The proposed method is shown to achieve robustness to the relationship between the learning rate and the Lipschitz constant, and near-optimal convergence rates in both the batch and stochastic settings ($O(1/T)$ for smooth loss in the batch setting, and $O(1/\sqrt{T})$ for convex loss in the stochastic setting). We also show through numerical evidence that such robustness of the proposed method extends to highly nonconvex and possibly non-smooth loss function in deep learning problems.Our analysis establishes some first theoretical understanding into the observed robustness for batch normalization and weight normalization.
Inference Networks for Sequential Monte Carlo in Graphical Models
Authors: Brooks Paige, Frank Wood
Date: 2016-02-22 00:00:00

We introduce a new approach for amortizing inference in directed graphical
models by learning heuristic approximations to stochastic inverses, designed
specifically for use as proposal distributions in sequential Monte Carlo
methods. We describe a procedure for constructing and learning a structured
neural network which represents an inverse factorization of the graphical
model, resulting in a conditional density estimator that takes as input
particular values of the observed random variables, and returns an
approximation to the distribution of the latent variables. This recognition
model can be learned offline, independent from any particular dataset, prior to
performing inference. The output of these networks can be used as
automatically-learned high-quality proposal distributions to accelerate
sequential Monte Carlo across a diverse range of problem settings.
Satisficing in Time-Sensitive Bandit Learning
Authors: Daniel Russo, Benjamin Van Roy
Date: 2018-03-07 00:00:00
Tasks: Thompson Sampling

Much of the recent literature on bandit learning focuses on algorithms that aim to converge on an optimal action. One shortcoming is that this orientation does not account for time sensitivity, which can play a crucial role when learning an optimal action requires much more information than near-optimal ones. Indeed, popular approaches such as upper-confidence-bound methods and Thompson sampling can fare poorly in such situations. We consider instead learning a satisficing action, which is near-optimal while requiring less information, and propose satisficing Thompson sampling, an algorithm that serves this purpose. We establish a general bound on expected discounted regret and study the application of satisficing Thompson sampling to linear and infinite-armed bandits, demonstrating arbitrarily large benefits over Thompson sampling. We also discuss the relation between the notion of satisficing and the theory of rate distortion, which offers guidance on the selection of satisficing actions.
Value Alignment, Fair Play, and the Rights of Service Robots
Authors: Daniel Estrada
Date: 2018-03-07 00:00:00
Tasks: Ethics

Ethics and safety research in artificial intelligence is increasingly framed
in terms of "alignment" with human values and interests. I argue that Turing's
call for "fair play for machines" is an early and often overlooked contribution
to the alignment literature. Turing's appeal to fair play suggests a need to
correct human behavior to accommodate our machines, a surprising inversion of
how value alignment is treated today. Reflections on "fair play" motivate a
novel interpretation of Turing's notorious "imitation game" as a condition not
of intelligence but instead of value alignment: a machine demonstrates a
minimal degree of alignment (with the norms of conversation, for instance) when
it can go undetected when interrogated by a human. I carefully distinguish this
interpretation from the Moral Turing Test, which is not motivated by a
principle of fair play, but instead depends on imitation of human moral
behavior. Finally, I consider how the framework of fair play can be used to
situate the debate over robot rights within the alignment literature. I argue
that extending rights to service robots operating in public spaces is "fair" in
precisely the sense that it encourages an alignment of interests between humans
and machines.
The emergent algebraic structure of RNNs and embeddings in NLP
Authors: Sean A. Cantrell
Date: 2018-03-07 00:00:00
Tasks: General Classification, text-classification, Text Classification, Word Embeddings

We examine the algebraic and geometric properties of a uni-directional GRU
and word embeddings trained end-to-end on a text classification task. A
hyperparameter search over word embedding dimension, GRU hidden dimension, and
a linear combination of the GRU outputs is performed. We conclude that words
naturally embed themselves in a Lie group and that RNNs form a nonlinear
representation of the group. Appealing to these results, we propose a novel
class of recurrent-like neural networks and a word embedding scheme.
Sever: A Robust Meta-Algorithm for Stochastic Optimization
Authors: Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Jacob Steinhardt, Alistair Stewart
Date: 2018-03-07 00:00:00
Tasks: Drug Design, Stochastic Optimization

In high dimensions, most machine learning methods are brittle to even a small fraction of structured outliers. To address this, we introduce a new meta-algorithm that can take in a base learner such as least squares or stochastic gradient descent, and harden the learner to be resistant to outliers. Our method, Sever, possesses strong theoretical guarantees yet is also highly scalable -- beyond running the base learner itself, it only requires computing the top singular vector of a certain $n \times d$ matrix. We apply Sever on a drug design dataset and a spam classification dataset, and find that in both cases it has substantially greater robustness than several baselines. On the spam dataset, with $1\%$ corruptions, we achieved $7.4\%$ test error, compared to $13.4\%-20.5\%$ for the baselines, and $3\%$ error on the uncorrupted dataset. Similarly, on the drug design dataset, with $10\%$ corruptions, we achieved $1.42$ mean-squared error test error, compared to $1.51$-$2.33$ for the baselines, and $1.23$ error on the uncorrupted dataset.
Accelerated Methods for Deep Reinforcement Learning
Authors: Adam Stooke, Pieter Abbeel
Date: 2018-03-07 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning (RL) has achieved many recent successes, yet
experiment turn-around time remains a key bottleneck in research and in
practice. We investigate how to optimize existing deep RL algorithms for modern
computers, specifically for a combination of CPUs and GPUs. We confirm that
both policy gradient and Q-value learning algorithms can be adapted to learn
using many parallel simulator instances. We further find it possible to train
using batch sizes considerably larger than are standard, without negatively
affecting sample complexity or final performance. We leverage these facts to
build a unified framework for parallelization that dramatically hastens
experiments in both classes of algorithm. All neural network computations use
GPUs, accelerating both data collection and training. Our results include using
an entire DGX-1 to learn successful strategies in Atari games in mere minutes,
using both synchronous and asynchronous algorithms.
OntoWind: An Improved and Extended Wind Energy Ontology
Authors: Dilek Küçük, Doğan Küçük
Date: 2018-03-07 00:00:00

Ontologies are critical sources of semantic information for many application
domains. Hence, there are ontologies proposed and utilized for domains such as
medicine, chemical engineering, and electrical energy. In this paper, we
present an improved and extended version of a wind energy ontology previously
proposed. First, the ontology is restructured to increase its understandability
and coverage. Secondly, it is enriched with new concepts, crisp/fuzzy
attributes, and instances to increase its usability in semantic applications
regarding wind energy. The ultimate ontology is utilized within a Web-based
semantic portal application for wind energy, in order to showcase its
contribution in a genuine application. Hence, the current study is a
significant to wind and thereby renewable energy informatics, with the
presented publicly-available wind energy ontology and the implemented
proof-of-concept system.