pwc-archive/papers-with-abstracts huggingface dataset:
Machine Learning for Data-Driven Movement Generation: a Review of the State of the Art
Authors: Omid Alemi, Philippe Pasquier
Date: 2019-03-20 00:00:00
Tasks: BIG-bench Machine Learning, Survey

The rise of non-linear and interactive media such as video games has
increased the need for automatic movement animation generation. In this survey,
we review and analyze different aspects of building automatic movement
generation systems using machine learning techniques and motion capture data.
We cover topics such as high-level movement characterization, training data,
features representation, machine learning models, and evaluation methods. We
conclude by presenting a discussion of the reviewed literature and outlining
the research gaps and remaining challenges for future work.
Distributed Maximization of Submodular plus Diversity Functions for Multi-label Feature Selection on Huge Datasets
Authors: Mehrdad Ghadiri, Mark Schmidt
Date: 2019-03-20 00:00:00
Tasks: Data Summarization, Diversity, feature selection, Recommendation Systems

There are many problems in machine learning and data mining which are
equivalent to selecting a non-redundant, high "quality" set of objects.
Recommender systems, feature selection, and data summarization are among many
applications of this. In this paper, we consider this problem as an
optimization problem that seeks to maximize the sum of a sum-sum diversity
function and a non-negative monotone submodular function. The diversity
function addresses the redundancy, and the submodular function controls the
predictive quality. We consider the problem in big data settings (in other
words, distributed and streaming settings) where the data cannot be stored on a
single machine or the process time is too high for a single machine. We show
that a greedy algorithm achieves a constant factor approximation of the optimal
solution in these settings. Moreover, we formulate the multi-label feature
selection problem as such an optimization problem. This formulation combined
with our algorithm leads to the first distributed multi-label feature selection
method. We compare the performance of this method with centralized multi-label
feature selection methods in the literature, and we show that its performance
is comparable or in some cases is even better than current centralized
multi-label feature selection methods.
Photon-Flooded Single-Photon 3D Cameras
Authors: Anant Gupta, Atul Ingle, Andreas Velten, Mohit Gupta
Date: 2019-03-20 00:00:00
Proceeding: CVPR 2019 6

Single photon avalanche diodes (SPADs) are starting to play a pivotal role in
the development of photon-efficient, long-range LiDAR systems. However, due to
non-linearities in their image formation model, a high photon flux (e.g., due
to strong sunlight) leads to distortion of the incident temporal waveform, and
potentially, large depth errors. Operating SPADs in low flux regimes can
mitigate these distortions, but, often requires attenuating the signal and
thus, results in low signal-to-noise ratio. In this paper, we address the
following basic question: what is the optimal photon flux that a SPAD-based
LiDAR should be operated in? We derive a closed form expression for the optimal
flux, which is quasi-depth-invariant, and depends on the ambient light
strength. The optimal flux is lower than what a SPAD typically measures in real
world scenarios, but surprisingly, considerably higher than what is
conventionally suggested for avoiding distortions. We propose a simple,
adaptive approach for achieving the optimal flux by attenuating incident flux
based on an estimate of ambient light strength. Using extensive simulations and
a hardware prototype, we show that the optimal flux criterion holds for several
depth estimators, under a wide range of illumination conditions.
On the Robustness of Deep K-Nearest Neighbors
Authors: Chawin Sitawarin, David Wagner
Date: 2019-03-20 00:00:00

Despite a large amount of attention on adversarial examples, very few works
have demonstrated an effective defense against this threat. We examine Deep
k-Nearest Neighbor (DkNN), a proposed defense that combines k-Nearest Neighbor
(kNN) and deep learning to improve the model's robustness to adversarial
examples. It is challenging to evaluate the robustness of this scheme due to a
lack of efficient algorithm for attacking kNN classifiers with large k and
high-dimensional data. We propose a heuristic attack that allows us to use
gradient descent to find adversarial examples for kNN classifiers, and then
apply it to attack the DkNN defense as well. Results suggest that our attack is
moderately stronger than any naive attack on kNN and significantly outperforms
other attacks on DkNN.
On Sampling Random Features From Empirical Leverage Scores: Implementation and Theoretical Guarantees
Authors: Shahin Shahrampour, Soheil Kolouri
Date: 2019-03-20 00:00:00

Random features provide a practical framework for large-scale kernel
approximation and supervised learning. It has been shown that data-dependent
sampling of random features using leverage scores can significantly reduce the
number of features required to achieve optimal learning bounds. Leverage scores
introduce an optimized distribution for features based on an
infinite-dimensional integral operator (depending on input distribution), which
is impractical to sample from. Focusing on empirical leverage scores in this
paper, we establish an out-of-sample performance bound, revealing an
interesting trade-off between the approximated kernel and the eigenvalue decay
of another kernel in the domain of random features defined based on data
distribution. Our experiments verify that the empirical algorithm consistently
outperforms vanilla Monte Carlo sampling, and with a minor modification the
method is even competitive to supervised data-dependent kernel learning,
without using the output (label) information.
A Learning Framework for Distribution-Based Game-Theoretic Solution Concepts
Authors: Tushant Jha, Yair Zick
Date: 2019-03-20 00:00:00

The past few years have seen several works on learning economic solutions from data; these include optimal auction design, function optimization, stable payoffs in cooperative games and more. In this work, we provide a unified learning-theoretic methodology for modeling such problems, and establish tools for determining whether a given economic solution concept can be learned from data. Our learning theoretic framework generalizes a notion of function space dimension -- the graph dimension -- adapting it to the solution concept learning domain. We identify sufficient conditions for the PAC learnability of solution concepts, and show that results in existing works can be immediately derived using our methodology. Finally, we apply our methods in other economic domains, yielding a novel notion of PAC competitive equilibrium and PAC Condorcet winners.
Prospection: Interpretable Plans From Language By Predicting the Future
Authors: Chris Paxton, Yonatan Bisk, Jesse Thomason, Arunkumar Byravan, Dieter Fox
Date: 2019-03-20 00:00:00

High-level human instructions often correspond to behaviors with multiple
implicit steps. In order for robots to be useful in the real world, they must
be able to to reason over both motions and intermediate goals implied by human
instructions. In this work, we propose a framework for learning representations
that convert from a natural-language command to a sequence of intermediate
goals for execution on a robot. A key feature of this framework is prospection,
training an agent not just to correctly execute the prescribed command, but to
predict a horizon of consequences of an action before taking it. We demonstrate
the fidelity of plans generated by our framework when interpreting real,
crowd-sourced natural language commands for a robot in simulated scenes.
On Extracting Data from Tables that are Encoded using HTML
Authors: Juan C. Roldán, Patricia Jiménez, Rafael Corchuelo
Date: 2019-03-20 00:00:00

Tables are a common means to display data in human-friendly formats. Many
authors have worked on proposals to extract those data back since this has many
interesting applications. In this article, we summarise and compare many of the
proposals to extract data from tables that are encoded using HTML and have been
published between $2000$ and $2018$. We first present a vocabulary that
homogenises the terminology used in this field; next, we use it to summarise
the proposals; finally, we compare them side by side. Our analysis highlights
several challenges to which no proposal provides a conclusive solution and a
few more that have not been addressed sufficiently; simply put, no proposal
provides a complete solution to the problem, which seems to suggest that this
research field shall keep active in the near future. We have also realised that
there is no consensus regarding the datasets and the methods used to evaluate
the proposals, which hampers comparing the experimental results.
Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening
Authors: Nan Wu, Jason Phang, Jungkyu Park, Yiqiu Shen, Zhe Huang, Masha Zorin, Stanisław Jastrzębski, Thibault Févry, Joe Katsnelson, Eric Kim, Stacey Wolfson, Ujas Parikh, Sushma Gaddam, Leng Leng Young Lin, Kara Ho, Joshua D. Weinstein, Beatriu Reig, Yiming Gao, Hildegard Toth, Kristine Pysarenko, Alana Lewin, Jiyon Lee, Krystal Airola, Eralda Mema, Stephanie Chung, Esther Hwang, Naziya Samreen, S. Gene Kim, Laura Heacock, Linda Moy, Kyunghyun Cho, Krzysztof J. Geras
Date: 2019-03-20 00:00:00
Tasks: Attribute, Breast Cancer Detection, Medical Diagnosis

We present a deep convolutional neural network for breast cancer screening
exam classification, trained and evaluated on over 200,000 exams (over
1,000,000 images). Our network achieves an AUC of 0.895 in predicting whether
there is a cancer in the breast, when tested on the screening population. We
attribute the high accuracy of our model to a two-stage training procedure,
which allows us to use a very high-capacity patch-level network to learn from
pixel-level labels alongside a network learning from macroscopic breast-level
labels. To validate our model, we conducted a reader study with 14 readers,
each reading 720 screening mammogram exams, and find our model to be as
accurate as experienced radiologists when presented with the same data.
Finally, we show that a hybrid model, averaging probability of malignancy
predicted by a radiologist with a prediction of our neural network, is more
accurate than either of the two separately. To better understand our results,
we conduct a thorough analysis of our network's performance on different
subpopulations of the screening population, model design, training procedure,
errors, and properties of its internal representations.
GANs for Semi-Supervised Opinion Spam Detection
Authors: Gray Stanton, Athirai A. Irissappane
Date: 2019-03-19 00:00:00
Tasks: General Classification, Generative Adversarial Network, Spam detection, text-classification, Text Classification

Online reviews have become a vital source of information in purchasing a service (product). Opinion spammers manipulate reviews, affecting the overall perception of the service. A key challenge in detecting opinion spam is obtaining ground truth. Though there exists a large set of reviews online, only a few of them have been labeled spam or non-spam. In this paper, we propose spamGAN, a generative adversarial network which relies on limited set of labeled data as well as unlabeled data for opinion spam detection. spamGAN improves the state-of-the-art GAN based techniques for text classification. Experiments on TripAdvisor dataset show that spamGAN outperforms existing spam detection techniques when limited labeled data is used. Apart from detecting spam reviews, spamGAN can also generate reviews with reasonable perplexity.
Simple, Fast, Accurate Intent Classification and Slot Labeling for Goal-Oriented Dialogue Systems
Authors: Arshit Gupta, John Hewitt, Katrin Kirchhoff
Date: 2019-03-19 00:00:00
Proceeding: WS 2019 9
Tasks: General Classification, Goal-Oriented Dialogue Systems, intent-classification, Intent Classification, Spoken Language Understanding

With the advent of conversational assistants, like Amazon Alexa, Google Now, etc., dialogue systems are gaining a lot of traction, especially in industrial setting. These systems typically consist of Spoken Language understanding component which, in turn, consists of two tasks - Intent Classification (IC) and Slot Labeling (SL). Generally, these two tasks are modeled together jointly to achieve best performance. However, this joint modeling adds to model obfuscation. In this work, we first design framework for a modularization of joint IC-SL task to enhance architecture transparency. Then, we explore a number of self-attention, convolutional, and recurrent models, contributing a large-scale analysis of modeling paradigms for IC+SL across two datasets. Finally, using this framework, we propose a class of 'label-recurrent' models that otherwise non-recurrent, with a 10-dimensional representation of the label history, and show that our proposed systems are easy to interpret, highly accurate (achieving over 30% error reduction in SL over the state-of-the-art on the Snips dataset), as well as fast, at 2x the inference and 2/3 to 1/2 the training time of comparable recurrent models, thus giving an edge in critical real-world systems.
A Quantum Annealing-Based Approach to Extreme Clustering
Authors: Tim Jaschek, Marko Bucyk, Jaspreet S. Oberoi
Date: 2019-03-19 00:00:00
Tasks: Clustering

Clustering, or grouping, dataset elements based on similarity can be used not only to classify a dataset into a few categories, but also to approximate it by a relatively large number of representative elements. In the latter scenario, referred to as extreme clustering, datasets are enormous and the number of representative clusters is large. We have devised a distributed method that can efficiently solve extreme clustering problems using quantum annealing. We prove that this method yields optimal clustering assignments under a separability assumption, and show that the generated clustering assignments are of comparable quality to those of assignments generated by common clustering algorithms, yet can be obtained a full order of magnitude faster.
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables
Authors: Kate Rakelly, Aurick Zhou, Deirdre Quillen, Chelsea Finn, Sergey Levine
Date: 2019-03-19 00:00:00
Proceeding: ICLR Workshop LLD 2019
Tasks: Deep Reinforcement Learning, Efficient Exploration, Meta Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning algorithms require large amounts of experience to
learn an individual task. While in principle meta-reinforcement learning
(meta-RL) algorithms enable agents to learn new skills from small amounts of
experience, several major challenges preclude their practicality. Current
methods rely heavily on on-policy experience, limiting their sample efficiency.
The also lack mechanisms to reason about task uncertainty when adapting to new
tasks, limiting their effectiveness in sparse reward problems. In this paper,
we address these challenges by developing an off-policy meta-RL algorithm that
disentangles task inference and control. In our approach, we perform online
probabilistic filtering of latent task variables to infer how to solve a new
task from small amounts of experience. This probabilistic interpretation
enables posterior sampling for structured and efficient exploration. We
demonstrate how to integrate these task variables with off-policy RL algorithms
to achieve both meta-training and adaptation efficiency. Our method outperforms
prior algorithms in sample efficiency by 20-100X as well as in asymptotic
performance on several meta-RL benchmarks.
When redundancy is useful: A Bayesian approach to 'overinformative' referring expressions
Authors: Judith Degen, Robert D. Hawkins, Caroline Graf, Elisa Kreiss, Noah D. Goodman
Date: 2019-03-19 00:00:00
Tasks: Informativeness, Specificity

Referring is one of the most basic and prevalent uses of language. How do speakers choose from the wealth of referring expressions at their disposal? Rational theories of language use have come under attack for decades for not being able to account for the seemingly irrational overinformativeness ubiquitous in referring expressions. Here we present a novel production model of referring expressions within the Rational Speech Act framework that treats speakers as agents that rationally trade off cost and informativeness of utterances. Crucially, we relax the assumption that informativeness is computed with respect to a deterministic Boolean semantics, in favor of a non-deterministic continuous semantics. This innovation allows us to capture a large number of seemingly disparate phenomena within one unified framework: the basic asymmetry in speakers' propensity to overmodify with color rather than size; the increase in overmodification in complex scenes; the increase in overmodification with atypical features; and the increase in specificity in nominal reference as a function of typicality. These findings cast a new light on the production of referring expressions: rather than being wastefully overinformative, reference is usefully redundant.
How to Make Swarms Open-Ended? Evolving Collective Intelligence Through a Constricted Exploration of Adjacent Possibles
Authors: Olaf Witkowski, Takashi Ikegami
Date: 2019-03-19 00:00:00
Tasks: Diversity

We propose an approach of open-ended evolution via the simulation of swarm
dynamics. In nature, swarms possess remarkable properties, which allow many
organisms, from swarming bacteria to ants and flocking birds, to form
higher-order structures that enhance their behavior as a group. Swarm
simulations highlight three important factors to create novelty and diversity:
(a) communication generates combinatorial cooperative dynamics, (b) concurrency
allows for separation of timescales, and (c) complexity and size increases push
the system towards transitions in innovation. We illustrate these three
components in a model computing the continuous evolution of a swarm of agents.
The results, divided in three distinct applications, show how emergent
structures are capable of filtering information through the bottleneck of their
memory, to produce meaningful novelty and diversity within their simulated
environment.
Characterization of the Handwriting Skills as a Biomarker for Parkinson Disease
Authors: R. Castrillon, A. Acien, J. R. Orozco-Arroyave, A. Morales, J. F. Vargas, R. Vera-Rodrıguez, J. Fierrez, J. Ortega-Garcia, A. Villegas
Date: 2019-03-19 00:00:00

In this paper we evaluate the suitability of handwriting patterns as
potential biomarkers to model Parkinson disease (PD). Although the study of PD
is attracting the interest of many researchers around the world, databases to
evaluate handwriting patterns are scarce and knowledge about patterns
associated to PD is limited and biased to the existing datasets. This paper
introduces a database with a total of 935 handwriting tasks collected from 55
PD patients and 94 healthy controls (45 young and 49 old). Three feature sets
are extracted from the signals: neuromotor, kinematic, and nonlinear dynamic.
Different classifiers are used to discriminate between PD and healthy subjects:
support vector machines, knearest neighbors, and a multilayer perceptron. The
proposed features and classifiers enable to detect PD with accuracies between
81% and 97%. Additionally, new insights are presented on the utility of the
studied features for monitoring and detecting PD.
Cross-task weakly supervised learning from instructional videos
Authors: Dimitri Zhukov, Jean-Baptiste Alayrac, Ramazan Gokberk Cinbis, David Fouhey, Ivan Laptev, Josef Sivic
Date: 2019-03-19 00:00:00
Proceeding: CVPR 2019 6
Tasks: Weakly-supervised Learning

In this paper we investigate learning visual models for the steps of ordinary
tasks using weak supervision via instructional narrations and an ordered list
of steps instead of strong supervision via temporal annotations. At the heart
of our approach is the observation that weakly supervised learning may be
easier if a model shares components while learning different steps: `pour egg'
should be trained jointly with other tasks involving `pour' and `egg'. We
formalize this in a component model for recognizing steps and a weakly
supervised learning framework that can learn this model under temporal
constraints from narration and the list of steps. Past data does not permit
systematic studying of sharing and so we also gather a new dataset, CrossTask,
aimed at assessing cross-task sharing. Our experiments demonstrate that sharing
across tasks improves performance, especially when done at the component level
and that our component model can parse previously unseen tasks by virtue of its
compositionality.
Why Couldn't You do that? Explaining Unsolvability of Classical Planning Problems in the Presence of Plan Advice
Authors: Sarath Sreedharan, Siddharth Srivastava, David Smith, Subbarao Kambhampati
Date: 2019-03-19 00:00:00

Explainable planning is widely accepted as a prerequisite for autonomous
agents to successfully work with humans. While there has been a lot of research
on generating explanations of solutions to planning problems, explaining the
absence of solutions remains an open and under-studied problem, even though
such situations can be the hardest to understand or debug. In this paper, we
show that hierarchical abstractions can be used to efficiently generate reasons
for unsolvability of planning problems. In contrast to related work on
computing certificates of unsolvability, we show that these methods can
generate compact, human-understandable reasons for unsolvability. Empirical
analysis and user studies show the validity of our methods as well as their
computational efficacy on a number of benchmark planning domains.
Aligning Biomedical Metadata with Ontologies Using Clustering and Embeddings
Authors: Rafael S. Gonçalves, Maulik R. Kamdar, Mark A. Musen
Date: 2019-03-19 00:00:00
Tasks: Clustering

The metadata about scientific experiments published in online repositories have been shown to suffer from a high degree of representational heterogeneity---there are often many ways to represent the same type of information, such as a geographical location via its latitude and longitude. To harness the potential that metadata have for discovering scientific data, it is crucial that they be represented in a uniform way that can be queried effectively. One step toward uniformly-represented metadata is to normalize the multiple, distinct field names used in metadata (e.g., lat lon, lat and long) to describe the same type of value. To that end, we present a new method based on clustering and embeddings (i.e., vector representations of words) to align metadata field names with ontology terms. We apply our method to biomedical metadata by generating embeddings for terms in biomedical ontologies from the BioPortal repository. We carried out a comparative study between our method and the NCBO Annotator, which revealed that our method yields more and substantially better alignments between metadata and ontology terms.