pwc-archive/papers-with-abstracts huggingface dataset:
Coordinating Collaborative Chat in Massive Open Online Courses
Authors: Gaurav Singh Tomar, Sreecharan Sankaranarayanan, Xu Wang, Carolyn Penstein Rosé
Date: 2017-04-18 00:00:00

An earlier study of a collaborative chat intervention in a Massive Open
Online Course (MOOC) identified negative effects on attrition stemming from a
requirement for students to be matched with exactly one partner prior to
beginning the activity. That study raised questions about how to orchestrate a
collaborative chat intervention in a MOOC context in order to provide the
benefit of synchronous social engagement without the coordination difficulties.
In this paper we present a careful analysis of an intervention designed to
overcome coordination difficulties by welcoming students into the chat on a
rolling basis as they arrive rather than requiring them to be matched with a
partner before beginning. The results suggest the most positive impact when
experiencing a chat with exactly one partner rather than more or less. A
qualitative analysis of the chat data reveals differential experiences between
these configurations that suggests a potential explanation for the effect and
raises questions for future research.
Beating Atari with Natural Language Guided Reinforcement Learning
Authors: Russell Kaplan, Christopher Sauer, Alexander Sosa
Date: 2017-04-18 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, Montezuma's Revenge, OpenAI Gym, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We introduce the first deep reinforcement learning agent that learns to beat
Atari games with the aid of natural language instructions. The agent uses a
multimodal embedding between environment observations and natural language to
self-monitor progress through a list of English instructions, granting itself
reward for completing instructions in addition to increasing the game score.
Our agent significantly outperforms Deep Q-Networks (DQNs), Asynchronous
Advantage Actor-Critic (A3C) agents, and the best agents posted to OpenAI Gym
on what is often considered the hardest Atari 2600 environment: Montezuma's
Revenge.
Diversity Networks: Neural Network Compression Using Determinantal Point Processes
Authors: Zelda Mariet, Suvrit Sra
Date: 2015-11-16 00:00:00
Tasks: Diversity, Neural Network Compression, Point Processes

We introduce Divnet, a flexible technique for learning networks with diverse
neurons. Divnet models neuronal diversity by placing a Determinantal Point
Process (DPP) over neurons in a given layer. It uses this DPP to select a
subset of diverse neurons and subsequently fuses the redundant neurons into the
selected ones. Compared with previous approaches, Divnet offers a more
principled, flexible technique for capturing neuronal diversity and thus
implicitly enforcing regularization. This enables effective auto-tuning of
network architecture and leads to smaller network sizes without hurting
performance. Moreover, through its focus on diversity and neuron fusing, Divnet
remains compatible with other procedures that seek to reduce memory footprints
of networks. We present experimental results to corroborate our claims: for
pruning neural networks, Divnet is seen to be notably superior to competing
approaches.
Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art
Authors: Joel Janai, Fatma Güney, Aseem Behl, Andreas Geiger
Date: 2017-04-18 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Benchmarking, Motion Estimation, Scene Understanding, Survey

Recent years have witnessed enormous progress in AI-related fields such as computer vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it becomes increasingly difficult to stay up-to-date or enter the field as a beginner. While several survey papers on particular sub-problems have appeared, no comprehensive survey on problems, datasets, and methods in computer vision for autonomous vehicles has been published. This book attempts to narrow this gap by providing a survey on the state-of-the-art datasets and techniques. Our survey includes both the historically most relevant literature as well as the current state of the art on several specific topics, including recognition, reconstruction, motion estimation, tracking, scene understanding, and end-to-end learning for autonomous driving. Towards this goal, we analyze the performance of the state of the art on several challenging benchmarking datasets, including KITTI, MOT, and Cityscapes. Besides, we discuss open problems and current research challenges. To ease accessibility and accommodate missing references, we also provide a website that allows navigating topics as well as methods and provides additional information.
25 Tweets to Know You: A New Model to Predict Personality with Social Media
Authors: Pierre-Hadrien Arnoux, Anbang Xu, Neil Boyette, Jalal Mahmud, Rama Akkiraju, Vibha Sinha
Date: 2017-04-18 00:00:00
Tasks: Gaussian Processes, regression

Predicting personality is essential for social applications supporting
human-centered activities, yet prior modeling methods with users written text
require too much input data to be realistically used in the context of social
media. In this work, we aim to drastically reduce the data requirement for
personality modeling and develop a model that is applicable to most users on
Twitter. Our model integrates Word Embedding features with Gaussian Processes
regression. Based on the evaluation of over 1.3K users on Twitter, we find that
our model achieves comparable or better accuracy than state of the art
techniques with 8 times fewer data.
Voxelwise nonlinear regression toolbox for neuroimage analysis: Application to aging and neurodegenerative disease modeling
Authors: Santi Puch, Asier Aduriz, Adrià Casamitjana, Veronica Vilaplana, Paula Petrone, Grégory Operto, Raffaele Cacciaglia, Stavros Skouras, Carles Falcon, José Luis Molinuevo, Juan Domingo Gispert
Date: 2016-12-02 00:00:00
Tasks: regression

This paper describes a new neuroimaging analysis toolbox that allows for the
modeling of nonlinear effects at the voxel level, overcoming limitations of
methods based on linear models like the GLM. We illustrate its features using a
relevant example in which distinct nonlinear trajectories of Alzheimer's
disease related brain atrophy patterns were found across the full biological
spectrum of the disease. The open-source toolbox presented in this paper is
available at https://github.com/imatge-upc/VNeAT.
A Convenient Category for Higher-Order Probability Theory
Authors: Chris Heunen, Ohad Kammar, Sam Staton, Hongseok Yang
Date: 2017-01-10 00:00:00
Tasks: Form, Probabilistic Programming

Higher-order probabilistic programming languages allow programmers to write sophisticated models in machine learning and statistics in a succinct and structured way, but step outside the standard measure-theoretic formalization of probability theory. Programs may use both higher-order functions and continuous distributions, or even define a probability distribution on functions. But standard probability theory does not handle higher-order functions well: the category of measurable spaces is not cartesian closed. Here we introduce quasi-Borel spaces. We show that these spaces: form a new formalization of probability theory replacing measurable spaces; form a cartesian closed category and so support higher-order functions; form a well-pointed category and so support good proof principles for equational reasoning; and support continuous probability distributions. We demonstrate the use of quasi-Borel spaces for higher-order functions and probability by: showing that a well-known construction of probability theory involving random functions gains a cleaner expression; and generalizing de Finetti's theorem, that is a crucial theorem in probability theory, to quasi-Borel spaces.
Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning
Authors: Tien-Ju Yang, Yu-Hsin Chen, Vivienne Sze
Date: 2016-11-16 00:00:00
Proceeding: CVPR 2017 7

Deep convolutional neural networks (CNNs) are indispensable to
state-of-the-art computer vision algorithms. However, they are still rarely
deployed on battery-powered mobile devices, such as smartphones and wearable
gadgets, where vision algorithms can enable many revolutionary real-world
applications. The key limiting factor is the high energy consumption of CNN
processing due to its high computational complexity. While there are many
previous efforts that try to reduce the CNN model size or amount of
computation, we find that they do not necessarily result in lower energy
consumption, and therefore do not serve as a good metric for energy cost
estimation.
  To close the gap between CNN design and energy consumption optimization, we
propose an energy-aware pruning algorithm for CNNs that directly uses energy
consumption estimation of a CNN to guide the pruning process. The energy
estimation methodology uses parameters extrapolated from actual hardware
measurements that target realistic battery-powered system setups. The proposed
layer-by-layer pruning algorithm also prunes more aggressively than previously
proposed pruning methods by minimizing the error in output feature maps instead
of filter weights. For each layer, the weights are first pruned and then
locally fine-tuned with a closed-form least-square solution to quickly restore
the accuracy. After all layers are pruned, the entire network is further
globally fine-tuned using back-propagation. With the proposed pruning method,
the energy consumption of AlexNet and GoogLeNet are reduced by 3.7x and 1.6x,
respectively, with less than 1% top-5 accuracy loss. Finally, we show that
pruning the AlexNet with a reduced number of target classes can greatly
decrease the number of weights but the energy reduction is limited.
  Energy modeling tool and energy-aware pruned models available at
http://eyeriss.mit.edu/energy.html
Investigating Recurrence and Eligibility Traces in Deep Q-Networks
Authors: Jean Harb, Doina Precup
Date: 2017-04-18 00:00:00
Tasks: Atari Games, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Eligibility traces in reinforcement learning are used as a bias-variance
trade-off and can often speed up training time by propagating knowledge back
over time-steps in a single update. We investigate the use of eligibility
traces in combination with recurrent networks in the Atari domain. We
illustrate the benefits of both recurrent nets and eligibility traces in some
Atari games, and highlight also the importance of the optimization used in the
training.
Generalized Ideals and Co-Granular Rough Sets
Authors: A. Mani
Date: 2017-04-18 00:00:00

Lattice-theoretic ideals have been used to define and generate non granular
rough approximations over general approximation spaces over the last few years
by few authors. The goal of these studies, in relation based rough sets, have
been to obtain nice properties comparable to those of classical rough
approximations. In this research paper, these ideas are generalized in a severe
way by the present author and associated semantic features are investigated by
her. Granules are used in the construction of approximations in implicit ways
and so a concept of co-granularity is introduced. Knowledge interpretation
associable with the approaches is also investigated. This research will be of
relevance for a number of logico-algebraic approaches to rough sets that
proceed from point-wise definitions of approximations and also for using
alternative approximations in spatial mereological contexts involving actual
contact relations. The antichain based semantics invented in earlier papers by
the present author also applies to the contexts considered.
Approximations from Anywhere and General Rough Sets
Authors: A. Mani
Date: 2017-04-18 00:00:00

Not all approximations arise from information systems. The problem of fitting
approximations, subjected to some rules (and related data), to information
systems in a rough scheme of things is known as the \emph{inverse problem}. The
inverse problem is more general than the duality (or abstract representation)
problems and was introduced by the present author in her earlier papers. From
the practical perspective, a few (as opposed to one) theoretical frameworks may
be suitable for formulating the problem itself. \emph{Granular operator spaces}
have been recently introduced and investigated by the present author in her
recent work in the context of antichain based and dialectical semantics for
general rough sets. The nature of the inverse problem is examined from
number-theoretic and combinatorial perspectives in a higher order variant of
granular operator spaces and some necessary conditions are proved. The results
and the novel approach would be useful in a number of unsupervised and semi
supervised learning contexts and algorithms.
Light Field Blind Motion Deblurring
Authors: Pratul P. Srinivasan, Ren Ng, Ravi Ramamoorthi
Date: 2017-04-18 00:00:00
Proceeding: CVPR 2017 7
Tasks: Deblurring

We study the problem of deblurring light fields of general 3D scenes captured
under 3D camera motion and present both theoretical and practical
contributions. By analyzing the motion-blurred light field in the primal and
Fourier domains, we develop intuition into the effects of camera motion on the
light field, show the advantages of capturing a 4D light field instead of a
conventional 2D image for motion deblurring, and derive simple methods of
motion deblurring in certain cases. We then present an algorithm to blindly
deblur light fields of general scenes without any estimation of scene geometry,
and demonstrate that we can recover both the sharp light field and the 3D
camera motion path of real and synthetically-blurred light fields.
Ranking to Learn: Feature Ranking and Selection via Eigenvector Centrality
Authors: Giorgio Roffo, Simone Melzi
Date: 2017-04-18 00:00:00
Tasks: feature selection, Object Recognition

In an era where accumulating data is easy and storing it inexpensive, feature
selection plays a central role in helping to reduce the high-dimensionality of
huge amounts of otherwise meaningless data. In this paper, we propose a
graph-based method for feature selection that ranks features by identifying the
most important ones into arbitrary set of cues. Mapping the problem on an
affinity graph-where features are the nodes-the solution is given by assessing
the importance of nodes through some indicators of centrality, in particular,
the Eigen-vector Centrality (EC). The gist of EC is to estimate the importance
of a feature as a function of the importance of its neighbors. Ranking central
nodes individuates candidate features, which turn out to be effective from a
classification point of view, as proved by a thoroughly experimental section.
Our approach has been tested on 7 diverse datasets from recent literature
(e.g., biological data and object recognition, among others), and compared
against filter, embedded and wrappers methods. The results are remarkable in
terms of accuracy, stability and low execution time.
A Study of Deep Learning Robustness Against Computation Failures
Authors: Jean-Charles Vialatte, François Leduc-Primeau
Date: 2017-04-18 00:00:00
Tasks: Deep Learning

For many types of integrated circuits, accepting larger failure rates in
computations can be used to improve energy efficiency. We study the performance
of faulty implementations of certain deep neural networks based on pessimistic
and optimistic models of the effect of hardware faults. After identifying the
impact of hyperparameters such as the number of layers on robustness, we study
the ability of the network to compensate for computational failures through an
increase of the network size. We show that some networks can achieve equivalent
performance under faulty implementations, and quantify the required increase in
computational complexity.
Mining Worse and Better Opinions. Unsupervised and Agnostic Aggregation of Online Reviews
Authors: Michela Fazzolari, Marinella Petrocchi, Alessandro Tommasi, Cesare Zavattari
Date: 2017-04-18 00:00:00
Tasks: Informativeness

In this paper, we propose a novel approach for aggregating online reviews,
according to the opinions they express. Our methodology is unsupervised - due
to the fact that it does not rely on pre-labeled reviews - and it is agnostic -
since it does not make any assumption about the domain or the language of the
review content. We measure the adherence of a review content to the domain
terminology extracted from a review set. First, we demonstrate the
informativeness of the adherence metric with respect to the score associated
with a review. Then, we exploit the metric values to group reviews, according
to the opinions they express. Our experimental campaign has been carried out on
two large datasets collected from Booking and Amazon, respectively.
Interactive Outlining of Pancreatic Cancer Liver Metastases in Ultrasound Images
Authors: Jan Egger, Dieter Schmalstieg, Xiaojun Chen, Wolfram G. Zoller, Alexander Hann
Date: 2017-04-18 00:00:00
Tasks: Interactive Segmentation, Segmentation

Ultrasound (US) is the most commonly used liver imaging modality worldwide.
Due to its low cost, it is increasingly used in the follow-up of cancer
patients with metastases localized in the liver. In this contribution, we
present the results of an interactive segmentation approach for liver
metastases in US acquisitions. A (semi-) automatic segmentation is still very
challenging because of the low image quality and the low contrast between the
metastasis and the surrounding liver tissue. Thus, the state of the art in
clinical practice is still manual measurement and outlining of the metastases
in the US images. We tackle the problem by providing an interactive
segmentation approach providing real-time feedback of the segmentation results.
The approach has been evaluated with typical US acquisitions from the clinical
routine, and the datasets consisted of pancreatic cancer metastases. Even for
difficult cases, satisfying segmentations results could be achieved because of
the interactive real-time behavior of the approach. In total, 40 clinical
images have been evaluated with our method by comparing the results against
manual ground truth segmentations. This evaluation yielded to an average Dice
Score of 85% and an average Hausdorff Distance of 13 pixels.
Probabilistic programs for inferring the goals of autonomous agents
Authors: Marco F. Cusumano-Towner, Alexey Radul, David Wingate, Vikash K. Mansinghka
Date: 2017-04-17 00:00:00

Intelligent systems sometimes need to infer the probable goals of people,
cars, and robots, based on partial observations of their motion. This paper
introduces a class of probabilistic programs for formulating and solving these
problems. The formulation uses randomized path planning algorithms as the basis
for probabilistic models of the process by which autonomous agents plan to
achieve their goals. Because these path planning algorithms do not have
tractable likelihood functions, new inference algorithms are needed. This paper
proposes two Monte Carlo techniques for these "likelihood-free" models, one of
which can use likelihood estimates from neural networks to accelerate
inference. The paper demonstrates efficacy on three simple examples, each using
under 50 lines of probabilistic code.
Representing Sentences as Low-Rank Subspaces
Authors: Jiaqi Mu, Suma Bhat, Pramod Viswanath
Date: 2017-04-18 00:00:00
Proceeding: ACL 2017 7
Tasks: Semantic Textual Similarity, Sentence

Sentences are important semantic units of natural language. A generic,
distributional representation of sentences that can capture the latent
semantics is beneficial to multiple downstream applications. We observe a
simple geometry of sentences -- the word representations of a given sentence
(on average 10.23 words in all SemEval datasets with a standard deviation 4.84)
roughly lie in a low-rank subspace (roughly, rank 4). Motivated by this
observation, we represent a sentence by the low-rank subspace spanned by its
word vectors. Such an unsupervised representation is empirically validated via
semantic textual similarity tasks on 19 different datasets, where it
outperforms the sophisticated neural network models, including skip-thought
vectors, by 15% on average.
Understanding Negations in Information Processing: Learning from Replicating Human Behavior
Authors: Nicolas Pröllochs, Stefan Feuerriegel, Dirk Neumann
Date: 2017-04-18 00:00:00
Tasks: Recommendation Systems, Reinforcement Learning

Information systems experience an ever-growing volume of unstructured data,
particularly in the form of textual materials. This represents a rich source of
information from which one can create value for people, organizations and
businesses. For instance, recommender systems can benefit from automatically
understanding preferences based on user reviews or social media. However, it is
difficult for computer programs to correctly infer meaning from narrative
content. One major challenge is negations that invert the interpretation of
words and sentences. As a remedy, this paper proposes a novel learning strategy
to detect negations: we apply reinforcement learning to find a policy that
replicates the human perception of negations based on an exogenous response,
such as a user rating for reviews. Our method yields several benefits, as it
eliminates the former need for expensive and subjective manual labeling in an
intermediate stage. Moreover, the inferred policy can be used to derive
statistical inferences and implications regarding how humans process and act on
negations.
Analysis of Computational Science Papers from ICCS 2001-2016 using Topic Modeling and Graph Theory
Authors: Tesfamariam M. Abuhay, Sergey V. Kovalchuk, Klavdiya O. Bochenina, George Kampis, Valeria V. Krzhizhanovskaya, Michael H. Lees
Date: 2017-04-18 00:00:00

This paper presents results of topic modeling and network models of topics
using the International Conference on Computational Science corpus, which
contains domain-specific (computational science) papers over sixteen years (a
total of 5695 papers). We discuss topical structures of International
Conference on Computational Science, how these topics evolve over time in
response to the topicality of various problems, technologies and methods, and
how all these topics relate to one another. This analysis illustrates
multidisciplinary research and collaborations among scientific communities, by
constructing static and dynamic networks from the topic modeling results and
the keywords of authors. The results of this study give insights about the past
and future trends of core discussion topics in computational science. We used
the Non-negative Matrix Factorization topic modeling algorithm to discover
topics and labeled and grouped results hierarchically.
Anomaly detection and motif discovery in symbolic representations of time series
Authors: Fabio Guigou, Pierre Collet, Pierre Parrend
Date: 2017-04-18 00:00:00
Tasks: Anomaly Detection, Time Series, Time Series Analysis

The advent of the Big Data hype and the consistent recollection of event logs
and real-time data from sensors, monitoring software and machine configuration
has generated a huge amount of time-varying data in about every sector of the
industry. Rule-based processing of such data has ceased to be relevant in many
scenarios where anomaly detection and pattern mining have to be entirely
accomplished by the machine. Since the early 2000s, the de-facto standard for
representing time series has been the Symbolic Aggregate approXimation (SAX).In
this document, we present a few algorithms using this representation for
anomaly detection and motif discovery, also known as pattern mining, in such
data. We propose a benchmark of anomaly detection algorithms using data from
Cloud monitoring software.