pwc-archive/papers-with-abstracts huggingface dataset:
Consequences and Factors of Stylistic Differences in Human-Robot Dialogue
Authors: Stephanie M. Lukin, Kimberly A. Pollard, Claire Bonial, Matthew Marge, Cassidy Henry, Ron Arstein, David Traum, Clare R. Voss
Date: 2018-07-21 00:00:00
Proceeding: WS 2018 7

This paper identifies stylistic differences in instruction-giving observed in
a corpus of human-robot dialogue. Differences in verbosity and structure (i.e.,
single-intent vs. multi-intent instructions) arose naturally without
restrictions or prior guidance on how users should speak with the robot.
Different styles were found to produce different rates of miscommunication, and
correlations were found between style differences and individual user
variation, trust, and interaction experience with the robot. Understanding
potential consequences and factors that influence style can inform design of
dialogue systems that are robust to natural variation from human users.
ScoutBot: A Dialogue System for Collaborative Navigation
Authors: Stephanie M. Lukin, Felix Gervits, Cory J. Hayes, Anton Leuski, Pooja Moolchandani, John G. Rogers III, Carlos Sanchez Amaro, Matthew Marge, Clare R. Voss, David Traum
Date: 2018-07-21 00:00:00
Proceeding: ACL 2018 7

ScoutBot is a dialogue interface to physical and simulated robots that
supports collaborative exploration of environments. The demonstration will
allow users to issue unconstrained spoken language commands to ScoutBot.
ScoutBot will prompt for clarification if the user's instruction needs
additional input. It is trained on human-robot dialogue collected from
Wizard-of-Oz experiments, where robot responses were initiated by a human
wizard in previous interactions. The demonstration will show a simulated ground
robot (Clearpath Jackal) in a simulated environment supported by ROS (Robot
Operating System).
S3D: Single Shot multi-Span Detector via Fully 3D Convolutional Networks
Authors: Da Zhang, Xiyang Dai, Xin Wang, Yuan-Fang Wang
Date: 2018-07-21 00:00:00
Tasks: Action Detection, Activity Detection

In this paper, we present a novel Single Shot multi-Span Detector for
temporal activity detection in long, untrimmed videos using a simple end-to-end
fully three-dimensional convolutional (Conv3D) network. Our architecture, named
S3D, encodes the entire video stream and discretizes the output space of
temporal activity spans into a set of default spans over different temporal
locations and scales. At prediction time, S3D predicts scores for the presence
of activity categories in each default span and produces temporal adjustments
relative to the span location to predict the precise activity duration. Unlike
many state-of-the-art systems that require a separate proposal and
classification stage, our S3D is intrinsically simple and dedicatedly designed
for single-shot, end-to-end temporal activity detection. When evaluating on
THUMOS'14 detection benchmark, S3D achieves state-of-the-art performance and is
very efficient and can operate at 1271 FPS.
Safe Option-Critic: Learning Safety in the Option-Critic Architecture
Authors: Arushi Jain, Khimya Khetarpal, Doina Precup
Date: 2018-07-21 00:00:00
Tasks: Atari Games, Hierarchical Reinforcement Learning, Reinforcement Learning

Designing hierarchical reinforcement learning algorithms that exhibit safe behaviour is not only vital for practical applications but also, facilitates a better understanding of an agent's decisions. We tackle this problem in the options framework, a particular way to specify temporally abstract actions which allow an agent to use sub-policies with start and end conditions. We consider a behaviour as safe that avoids regions of state-space with high uncertainty in the outcomes of actions. We propose an optimization objective that learns safe options by encouraging the agent to visit states with higher behavioural consistency. The proposed objective results in a trade-off between maximizing the standard expected return and minimizing the effect of model uncertainty in the return. We propose a policy gradient algorithm to optimize the constrained objective function. We examine the quantitative and qualitative behaviour of the proposed approach in a tabular grid-world, continuous-state puddle-world, and three games from the Arcade Learning Environment: Ms.Pacman, Amidar, and Q*Bert. Our approach achieves a reduction in the variance of return, boosts performance in environments with intrinsic variability in the reward structure, and compares favorably both with primitive actions as well as with risk-neutral options.
Learning Heuristics for Quantified Boolean Formulas through Deep Reinforcement Learning
Authors: Gil Lederman, Markus N. Rabe, Edward A. Lee, Sanjit A. Seshia
Date: 2018-07-20 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We demonstrate how to learn efficient heuristics for automated reasoning algorithms for quantified Boolean formulas through deep reinforcement learning. We focus on a backtracking search algorithm, which can already solve formulas of impressive size - up to hundreds of thousands of variables. The main challenge is to find a representation of these formulas that lends itself to making predictions in a scalable way. For a family of challenging problems, we learned a heuristic that solves significantly more formulas compared to the existing handwritten heuristics.
Baidu Apollo EM Motion Planner
Authors: Haoyang Fan, Fan Zhu, Changchun Liu, Liangliang Zhang, Li Zhuang, Dong Li, Weicheng Zhu, Jiangtao Hu, Hongye Li, Qi Kong
Date: 2018-07-20 00:00:00
Tasks: Autonomous Driving, Motion Planning

In this manuscript, we introduce a real-time motion planning system based on
the Baidu Apollo (open source) autonomous driving platform. The developed
system aims to address the industrial level-4 motion planning problem while
considering safety, comfort and scalability. The system covers multilane and
single-lane autonomous driving in a hierarchical manner: (1) The top layer of
the system is a multilane strategy that handles lane-change scenarios by
comparing lane-level trajectories computed in parallel. (2) Inside the
lane-level trajectory generator, it iteratively solves path and speed
optimization based on a Frenet frame. (3) For path and speed optimization, a
combination of dynamic programming and spline-based quadratic programming is
proposed to construct a scalable and easy-to-tune framework to handle traffic
rules, obstacle decisions and smoothness simultaneously. The planner is
scalable to both highway and lower-speed city driving scenarios. We also
demonstrate the algorithm through scenario illustrations and on-road test
results.
  The system described in this manuscript has been deployed to dozens of Baidu
Apollo autonomous driving vehicles since Apollo v1.5 was announced in September
2017. As of May 16th, 2018, the system has been tested under 3,380 hours and
approximately 68,000 kilometers (42,253 miles) of closed-loop autonomous
driving under various urban scenarios.
  The algorithm described in this manuscript is available at
https://github.com/ApolloAuto/apollo/tree/master/modules/planning.
A Fast, Principled Working Set Algorithm for Exploiting Piecewise Linear Structure in Convex Problems
Authors: Tyler B. Johnson, Carlos Guestrin
Date: 2018-07-20 00:00:00

By reducing optimization to a sequence of smaller subproblems, working set
algorithms achieve fast convergence times for many machine learning problems.
Despite such performance, working set implementations often resort to
heuristics to determine subproblem size, makeup, and stopping criteria. We
propose BlitzWS, a working set algorithm with useful theoretical guarantees.
Our theory relates subproblem size and stopping criteria to the amount of
progress during each iteration. This result motivates strategies for optimizing
algorithmic parameters and discarding irrelevant components as BlitzWS
progresses toward a solution. BlitzWS applies to many convex problems,
including training L1-regularized models and support vector machines. We
showcase this versatility with empirical comparisons, which demonstrate BlitzWS
is indeed a fast algorithm.
Explaining Image Classifiers by Counterfactual Generation
Authors: Chun-Hao Chang, Elliot Creager, Anna Goldenberg, David Duvenaud
Date: 2018-07-20 00:00:00
Proceeding: ICLR 2019 5
Tasks: counterfactual, Image Classification

When an image classifier makes a prediction, which parts of the image are
relevant and why? We can rephrase this question to ask: which parts of the
image, if they were not seen by the classifier, would most change its decision?
Producing an answer requires marginalizing over images that could have been
seen but weren't. We can sample plausible image in-fills by conditioning a
generative model on the rest of the image. We then optimize to find the image
regions that most change the classifier's decision after in-fill. Our approach
contrasts with ad-hoc in-filling approaches, such as blurring or injecting
noise, which generate inputs far from the data distribution, and ignore
informative relationships between different parts of the image. Our method
produces more compact and relevant saliency maps, with fewer artifacts compared
to previous methods.
signProx: One-Bit Proximal Algorithm for Nonconvex Stochastic Optimization
Authors: Xiaojian Xu, Ulugbek S. Kamilov
Date: 2018-07-20 00:00:00
Tasks: Stochastic Optimization

Stochastic gradient descent (SGD) is one of the most widely used optimization
methods for parallel and distributed processing of large datasets. One of the
key limitations of distributed SGD is the need to regularly communicate the
gradients between different computation nodes. To reduce this communication
bottleneck, recent work has considered a one-bit variant of SGD, where only the
sign of each gradient element is used in optimization. In this paper, we extend
this idea by proposing a stochastic variant of the proximal-gradient method
that also uses one-bit per update element. We prove the theoretical convergence
of the method for non-convex optimization under a set of explicit assumptions.
Our results indicate that the compressed method can match the convergence rate
of the uncompressed one, making the proposed method potentially appealing for
distributed processing of large datasets.
Ensemble of Deep Learned Features for Melanoma Classification
Authors: Loris Nanni, Alessandra Lumini, Stefano Ghidoni
Date: 2018-07-20 00:00:00
Tasks: Classification, General Classification

The aim of this work is to propose an ensemble of descriptors for Melanoma
Classification, whose performance has been evaluated on validation and test
datasets of the melanoma challenge 2018. The system proposed here achieves a
strong discriminative power thanks to the combination of multiple descriptors.
The proposed system represents a very simple yet effective way of boosting the
performance of trained CNNs by composing multiple CNNs into an ensemble and
combining scores by sum rule. Several types of ensembles are considered, with
different CNN architectures along with different learning parameter sets.
Moreover CNN are used as feature extractors: an input image is processed by a
trained CNN and the response of a particular layer (usually the classification
layer, but also internal layers can be employed) is treated as a descriptor for
the image and used for training a set of Support Vector Machines (SVM).
Abstractive and Extractive Text Summarization using Document Context Vector and Recurrent Neural Networks
Authors: Chandra Khatri, Gyanit Singh, Nish Parikh
Date: 2018-07-20 00:00:00
Tasks: Document Summarization, Extractive Summarization, Extractive Text Summarization, Text Summarization

Sequence to sequence (Seq2Seq) learning has recently been used for
abstractive and extractive summarization. In current study, Seq2Seq models have
been used for eBay product description summarization. We propose a novel
Document-Context based Seq2Seq models using RNNs for abstractive and extractive
summarizations. Intuitively, this is similar to humans reading the title,
abstract or any other contextual information before reading the document. This
gives humans a high-level idea of what the document is about. We use this idea
and propose that Seq2Seq models should be started with contextual information
at the first time-step of the input to obtain better summaries. In this manner,
the output summaries are more document centric, than being generic, overcoming
one of the major hurdles of using generative models. We generate
document-context from user-behavior and seller provided information. We train
and evaluate our models on human-extracted-golden-summaries. The
document-contextual Seq2Seq models outperform standard Seq2Seq models.
Moreover, generating human extracted summaries is prohibitively expensive to
scale, we therefore propose a semi-supervised technique for extracting
approximate summaries and using it for training Seq2Seq models at scale.
Semi-supervised models are evaluated against human extracted summaries and are
found to be of similar efficacy. We provide side by side comparison for
abstractive and extractive summarizers (contextual and non-contextual) on same
evaluation dataset. Overall, we provide methodologies to use and evaluate the
proposed techniques for large document summarization. Furthermore, we found
these techniques to be highly effective, which is not the case with existing
techniques.
Convolutional Neural Networks Analyzed via Inverse Problem Theory and Sparse Representations
Authors: Cem Tarhan, Gozde Bozdagi Akar
Date: 2018-07-20 00:00:00
Tasks: Deblurring, Denoising

Inverse problems in imaging such as denoising, deblurring, superresolution
(SR) have been addressed for many decades. In recent years, convolutional
neural networks (CNNs) have been widely used for many inverse problem areas.
Although their indisputable success, CNNs are not mathematically validated as
to how and what they learn. In this paper, we prove that during training, CNN
elements solve for inverse problems which are optimum solutions stored as CNN
neuron filters. We discuss the necessity of mutual coherence between CNN layer
elements in order for a network to converge to the optimum solution. We prove
that required mutual coherence can be provided by the usage of residual
learning and skip connections. We have set rules over training sets and depth
of networks for better convergence, i.e. performance.
Knowledge Integration for Disease Characterization: A Breast Cancer Example
Authors: Oshani Seneviratne, Sabbir M. Rashid, Shruthi Chari, James P. McCusker, Kristin P. Bennett, James A. Hendler, Deborah L. McGuinness
Date: 2018-07-20 00:00:00

With the rapid advancements in cancer research, the information that is
useful for characterizing disease, staging tumors, and creating treatment and
survivorship plans has been changing at a pace that creates challenges when
physicians try to remain current. One example involves increasing usage of
biomarkers when characterizing the pathologic prognostic stage of a breast
tumor. We present our semantic technology approach to support cancer
characterization and demonstrate it in our end-to-end prototype system that
collects the newest breast cancer staging criteria from authoritative oncology
manuals to construct an ontology for breast cancer. Using a tool we developed
that utilizes this ontology, physician-facing applications can be used to
quickly stage a new patient to support identifying risks, treatment options,
and monitoring plans based on authoritative and best practice guidelines.
Physicians can also re-stage existing patients or patient populations, allowing
them to find patients whose stage has changed in a given patient cohort. As new
guidelines emerge, using our proposed mechanism, which is grounded by semantic
technologies for ingesting new data from staging manuals, we have created an
enriched cancer staging ontology that integrates relevant data from several
sources with very little human intervention.
Deep Learning
Authors: Nicholas G. Polson, Vadim O. Sokolov
Date: 2018-07-20 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning

Deep learning (DL) is a high dimensional data reduction technique for
constructing high-dimensional predictors in input-output models. DL is a form
of machine learning that uses hierarchical layers of latent features. In this
article, we review the state-of-the-art of deep learning from a modeling and
algorithmic perspective. We provide a list of successful areas of applications
in Artificial Intelligence (AI), Image Processing, Robotics and Automation.
Deep learning is predictive in its nature rather then inferential and can be
viewed as a black-box methodology for high-dimensional function estimation.
Attention Models in Graphs: A Survey
Authors: John Boaz Lee, Ryan A. Rossi, Sungchul Kim, Nesreen K. Ahmed, Eunyee Koh
Date: 2018-07-20 00:00:00
Tasks: Graph Attention, Graph Classification, Graph Mining, Link Prediction, Survey

Graph-structured data arise naturally in many different application domains.
By representing data as graphs, we can capture entities (i.e., nodes) as well
as their relationships (i.e., edges) with each other. Many useful insights can
be derived from graph-structured data as demonstrated by an ever-growing body
of work focused on graph mining. However, in the real-world, graphs can be both
large - with many complex patterns - and noisy which can pose a problem for
effective graph mining. An effective way to deal with this issue is to
incorporate "attention" into graph mining solutions. An attention mechanism
allows a method to focus on task-relevant parts of the graph, helping it to
make better decisions. In this work, we conduct a comprehensive and focused
survey of the literature on the emerging field of graph attention models. We
introduce three intuitive taxonomies to group existing work. These are based on
problem setting (type of input and output), the type of attention mechanism
used, and the task (e.g., graph classification, link prediction, etc.). We
motivate our taxonomies through detailed examples and use each to survey
competing approaches from a unique standpoint. Finally, we highlight several
challenges in the area and discuss promising directions for future work.
Visitors to urban greenspace have higher sentiment and lower negativity on Twitter
Authors: Aaron J. Schwartz, Peter Sheridan Dodds, Jarlath P. M. O'Neil-Dunne, Christopher M. Danforth, Taylor H. Ricketts
Date: 2018-07-20 00:00:00
Tasks: Negation

With more people living in cities, we are witnessing a decline in exposure to nature. A growing body of research has demonstrated an association between nature contact and improved mood. Here, we used Twitter and the Hedonometer, a world analysis tool, to investigate how sentiment, or the estimated happiness of the words people write, varied before, during, and after visits to San Francisco's urban park system. We found that sentiment was substantially higher during park visits and remained elevated for several hours following the visit. Leveraging differences in vegetative cover across park types, we explored how different types of outdoor public spaces may contribute to subjective well-being. Tweets during visits to Regional Parks, which are greener and have greater vegetative cover, exhibited larger increases in sentiment than tweets during visits to Civic Plazas and Squares. Finally, we analyzed word frequencies to explore several mechanisms theorized to link nature exposure with mental and cognitive benefits. Negation words such as 'no', 'not', and 'don't' decreased in frequency during visits to urban parks. These results can be used by urban planners and public health officials to better target nature contact recommendations for growing urban populations.
Multi-criteria Evolution of Neural Network Topologies: Balancing Experience and Performance in Autonomous Systems
Authors: Sharat Chidambaran, Amir Behjat, Souma Chowdhury
Date: 2018-07-20 00:00:00

Majority of Artificial Neural Network (ANN) implementations in autonomous
systems use a fixed/user-prescribed network topology, leading to sub-optimal
performance and low portability. The existing neuro-evolution of augmenting
topology or NEAT paradigm offers a powerful alternative by allowing the network
topology and the connection weights to be simultaneously optimized through an
evolutionary process. However, most NEAT implementations allow the
consideration of only a single objective. There also persists the question of
how to tractably introduce topological diversification that mitigates
overfitting to training scenarios. To address these gaps, this paper develops a
multi-objective neuro-evolution algorithm. While adopting the basic elements of
NEAT, important modifications are made to the selection, speciation, and
mutation processes. With the backdrop of small-robot path-planning
applications, an experience-gain criterion is derived to encapsulate the amount
of diverse local environment encountered by the system. This criterion
facilitates the evolution of genes that support exploration, thereby seeking to
generalize from a smaller set of mission scenarios than possible with
performance maximization alone. The effectiveness of the single-objective
(optimizing performance) and the multi-objective (optimizing performance and
experience-gain) neuro-evolution approaches are evaluated on two different
small-robot cases, with ANNs obtained by the multi-objective optimization
observed to provide superior performance in unseen scenarios.
Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors
Authors: Andrew Ilyas, Logan Engstrom, Aleksander Madry
Date: 2018-07-20 00:00:00
Proceeding: ICLR 2019 5

We study the problem of generating adversarial examples in a black-box
setting in which only loss-oracle access to a model is available. We introduce
a framework that conceptually unifies much of the existing work on black-box
attacks, and we demonstrate that the current state-of-the-art methods are
optimal in a natural sense. Despite this optimality, we show how to improve
black-box attacks by bringing a new element into the problem: gradient priors.
We give a bandit optimization-based algorithm that allows us to seamlessly
integrate any such priors, and we explicitly identify and incorporate two
examples. The resulting methods use two to four times fewer queries and fail
two to five times less often than the current state-of-the-art.
An Efficient End-to-End Neural Model for Handwritten Text Recognition
Authors: Arindam Chowdhury, Lovekesh Vig
Date: 2018-07-20 00:00:00
Tasks: Decoder, Handwritten Text Recognition

Offline handwritten text recognition from images is an important problem for
enterprises attempting to digitize large volumes of handmarked scanned
documents/reports. Deep recurrent models such as Multi-dimensional LSTMs have
been shown to yield superior performance over traditional Hidden Markov Model
based approaches that suffer from the Markov assumption and therefore lack the
representational power of RNNs. In this paper we introduce a novel approach
that combines a deep convolutional network with a recurrent Encoder-Decoder
network to map an image to a sequence of characters corresponding to the text
present in the image. The entire model is trained end-to-end using Focal Loss,
an improvement over the standard Cross-Entropy loss that addresses the class
imbalance problem, inherent to text recognition. To enhance the decoding
capacity of the model, Beam Search algorithm is employed which searches for the
best sequence out of a set of hypotheses based on a joint distribution of
individual characters. Our model takes as input a downsampled version of the
original image thereby making it both computationally and memory efficient. The
experimental results were benchmarked against two publicly available datasets,
IAM and RIMES. We surpass the state-of-the-art word level accuracy on the
evaluation set of both datasets by 3.5% & 1.1%, respectively.