pwc-archive/papers-with-abstracts huggingface dataset:
Hierarchical Approaches for Reinforcement Learning in Parameterized Action Space
Authors: Ermo Wei, Drew Wicke, Sean Luke
Date: 2018-10-23 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We explore Deep Reinforcement Learning in a parameterized action space.
Specifically, we investigate how to achieve sample-efficient end-to-end
training in these tasks. We propose a new compact architecture for the tasks
where the parameter policy is conditioned on the output of the discrete action
policy. We also propose two new methods based on the state-of-the-art
algorithms Trust Region Policy Optimization (TRPO) and Stochastic Value
Gradient (SVG) to train such an architecture. We demonstrate that these methods
outperform the state of the art method, Parameterized Action DDPG, on test
domains.
One Bit Matters: Understanding Adversarial Examples as the Abuse of Redundancy
Authors: Jingkang Wang, Ruoxi Jia, Gerald Friedland, Bo Li, Costas Spanos
Date: 2018-10-23 00:00:00
Tasks: Decision Making

Despite the great success achieved in machine learning (ML), adversarial
examples have caused concerns with regards to its trustworthiness: A small
perturbation of an input results in an arbitrary failure of an otherwise
seemingly well-trained ML model. While studies are being conducted to discover
the intrinsic properties of adversarial examples, such as their transferability
and universality, there is insufficient theoretic analysis to help understand
the phenomenon in a way that can influence the design process of ML
experiments. In this paper, we deduce an information-theoretic model which
explains adversarial attacks as the abuse of feature redundancies in ML
algorithms. We prove that feature redundancy is a necessary condition for the
existence of adversarial examples. Our model helps to explain some major
questions raised in many anecdotal studies on adversarial examples. Our theory
is backed up by empirical measurements of the information content of benign and
adversarial examples on both image and text datasets. Our measurements show
that typical adversarial examples introduce just enough redundancy to overflow
the decision making of an ML model trained on corresponding benign examples. We
conclude with actionable recommendations to improve the robustness of machine
learners against adversarial examples.
What can AI do for me: Evaluating Machine Learning Interpretations in Cooperative Play
Authors: Shi Feng, Jordan Boyd-Graber
Date: 2018-10-23 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Question Answering

Machine learning is an important tool for decision making, but its ethical and responsible application requires rigorous vetting of its interpretability and utility: an understudied problem, particularly for natural language processing models. We propose an evaluation of interpretation on a real task with real human users, where the effectiveness of interpretation is measured by how much it improves human performance. We design a grounded, realistic human-computer cooperative setting using a question answering task, Quizbowl. We recruit both trivia experts and novices to play this game with computer as their teammate, who communicates its prediction via three different interpretations. We also provide design guidance for natural language processing human-in-the-loop settings.
Point-cloud-based place recognition using CNN feature extraction
Authors: Ting Sun, Ming Liu, Haoyang Ye, Dit-yan Yeung
Date: 2018-10-23 00:00:00

This paper proposes a novel point-cloud-based place recognition system that
adopts a deep learning approach for feature extraction. By using a
convolutional neural network pre-trained on color images to extract features
from a range image without fine-tuning on extra range images, significant
improvement has been observed when compared to using hand-crafted features. The
resulting system is illumination invariant, rotation invariant and robust
against moving objects that are unrelated to the place identity. Apart from the
system itself, we also bring to the community a new place recognition dataset
containing both point cloud and grayscale images covering a full $360^\circ$
environmental view. In addition, the dataset is organized in such a way that it
facilitates experimental validation with respect to rotation invariance or
robustness against unrelated moving objects separately.
A Neural Compositional Paradigm for Image Captioning
Authors: Bo Dai, Sanja Fidler, Dahua Lin
Date: 2018-10-23 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Diversity, Image Captioning

Mainstream captioning models often follow a sequential structure to generate
captions, leading to issues such as introduction of irrelevant semantics, lack
of diversity in the generated captions, and inadequate generalization
performance. In this paper, we present an alternative paradigm for image
captioning, which factorizes the captioning procedure into two stages: (1)
extracting an explicit semantic representation from the given image; and (2)
constructing the caption based on a recursive compositional procedure in a
bottom-up manner. Compared to conventional ones, our paradigm better preserves
the semantic content through an explicit factorization of semantics and syntax.
By using the compositional generation procedure, caption construction follows a
recursive structure, which naturally fits the properties of human language.
Moreover, the proposed compositional procedure requires less data to train,
generalizes better, and yields more diverse captions.
Deep Neural Ranking for Crowdsourced Geopolitical Event Forecasting
Authors: Giuseppe Nebbione, Derek Doran, Srikanth Nadella, Brandon Minnery
Date: 2018-10-23 00:00:00

There are many examples of 'wisdom of the crowd' effects in which the large
number of participants imparts confidence in the collective judgment of the
crowd. But how do we form an aggregated judgment when the size of the crowd is
limited? Whose judgments do we include, and whose do we accord the most weight?
This paper considers this problem in the context of geopolitical event
forecasting, where volunteer analysts are queried to give their expertise,
confidence, and predictions about the outcome of an event. We develop a
forecast aggregation model that integrates topical information about a
question, meta-data about a pair of forecasters, and their predictions in a
deep siamese neural network that decides which forecasters' predictions are
more likely to be close to the correct response. A ranking of the forecasters
is induced from a tournament of pair-wise forecaster comparisons, with the
ranking used to create an aggregate forecast. Preliminary results find the
aggregate prediction of the best forecasters ranked by our deep siamese network
model consistently beats typical aggregation techniques by Brier score.
Sparse DNNs with Improved Adversarial Robustness
Authors: Yiwen Guo, Chao Zhang, Chang-Shui Zhang, Yurong Chen
Date: 2018-10-23 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Adversarial Robustness, General Classification

Deep neural networks (DNNs) are computationally/memory-intensive and vulnerable to adversarial attacks, making them prohibitive in some real-world applications. By converting dense models into sparse ones, pruning appears to be a promising solution to reducing the computation/memory cost. This paper studies classification models, especially DNN-based ones, to demonstrate that there exists intrinsic relationships between their sparsity and adversarial robustness. Our analyses reveal, both theoretically and empirically, that nonlinear DNN-based classifiers behave differently under $l_2$ attacks from some linear ones. We further demonstrate that an appropriately higher model sparsity implies better robustness of nonlinear DNNs, whereas over-sparsified models can be more difficult to resist adversarial examples.
How to Read Paintings: Semantic Art Understanding with Multi-Modal Retrieval
Authors: Noa Garcia, George Vogiatzis
Date: 2018-10-23 00:00:00
Tasks: Art Analysis, Retrieval

Automatic art analysis has been mostly focused on classifying artworks into
different artistic styles. However, understanding an artistic representation
involves more complex processes, such as identifying the elements in the scene
or recognizing author influences. We present SemArt, a multi-modal dataset for
semantic art understanding. SemArt is a collection of fine-art painting images
in which each image is associated to a number of attributes and a textual
artistic comment, such as those that appear in art catalogues or museum
collections. To evaluate semantic art understanding, we envisage the Text2Art
challenge, a multi-modal retrieval task where relevant paintings are retrieved
according to an artistic text, and vice versa. We also propose several models
for encoding visual and textual artistic representations into a common semantic
space. Our best approach is able to find the correct image within the top 10
ranked images in the 45.5% of the test samples. Moreover, our models show
remarkable levels of art understanding when compared against human evaluation.
Neural Transition-based Syntactic Linearization
Authors: Linfeng Song, Yue Zhang, Daniel Gildea
Date: 2018-10-23 00:00:00
Proceeding: WS 2018 11
Tasks: Language Modeling, Language Modelling, Sentence

The task of linearization is to find a grammatical order given a set of
words. Traditional models use statistical methods. Syntactic linearization
systems, which generate a sentence along with its syntactic tree, have shown
state-of-the-art performance. Recent work shows that a multi-layer LSTM
language model outperforms competitive statistical syntactic linearization
systems without using syntax. In this paper, we study neural syntactic
linearization, building a transition-based syntactic linearizer leveraging a
feed-forward neural network, observing significantly better results compared to
LSTM language models on this task.
Explainable artificial intelligence (XAI), the goodness criteria and the grasp-ability test
Authors: Tae Wam Kim
Date: 2018-10-22 00:00:00
Tasks: Explainable artificial intelligence, Explainable Artificial Intelligence (XAI)

This paper introduces the "grasp-ability test" as a "goodness" criteria by
which to compare which explanation is more or less meaningful than others for
users to understand the automated algorithmic data processing.
Biomedical Document Clustering and Visualization based on the Concepts of Diseases
Authors: Setu Shah, Xiao Luo
Date: 2018-10-22 00:00:00
Tasks: Clustering

Document clustering is a text mining technique used to provide better
document search and browsing in digital libraries or online corpora. A lot of
research has been done on biomedical document clustering that is based on using
existing ontology. But, associations and co-occurrences of the medical concepts
are not well represented by using ontology. In this research, a vector
representation of concepts of diseases and similarity measurement between
concepts are proposed. They identify the closest concepts of diseases in the
context of a corpus. Each document is represented by using the vector space
model. A weight scheme is proposed to consider both local content and
associations between concepts. A Self-Organizing Map is used as document
clustering algorithm. The vector projection and visualization features of SOM
enable visualization and analysis of the clusters distributions and
relationships on the two dimensional space. The experimental results show that
the proposed document clustering framework generates meaningful clusters and
facilitate visualization of the clusters based on the concepts of diseases.
MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare
Authors: Edward Choi, Cao Xiao, Walter F. Stewart, Jimeng Sun
Date: 2018-10-22 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Disease Prediction, Prediction

Deep learning models exhibit state-of-the-art performance for many predictive
healthcare tasks using electronic health records (EHR) data, but these models
typically require training data volume that exceeds the capacity of most
healthcare systems. External resources such as medical ontologies are used to
bridge the data volume constraint, but this approach is often not directly
applicable or useful because of inconsistencies with terminology. To solve the
data insufficiency challenge, we leverage the inherent multilevel structure of
EHR data and, in particular, the encoded relationships among medical codes. We
propose Multilevel Medical Embedding (MiME) which learns the multilevel
embedding of EHR data while jointly performing auxiliary prediction tasks that
rely on this inherent EHR structure without the need for external labels. We
conducted two prediction tasks, heart failure prediction and sequential disease
prediction, where MiME outperformed baseline methods in diverse evaluation
settings. In particular, MiME consistently outperformed all baselines when
predicting heart failure on datasets of different volumes, especially
demonstrating the greatest performance improvement (15% relative gain in PR-AUC
over the best baseline) on the smallest dataset, demonstrating its ability to
effectively model the multilevel structure of EHR data.
Applying Deep Learning To Airbnb Search
Authors: Malay Haldar, Mustafa Abdool, Prashant Ramanathan, Tao Xu, Shulin Yang, Huizhong Duan, Qing Zhang, Nick Barrow-Williams, Bradley C. Turnbull, Brendan M. Collins, Thomas Legrand
Date: 2018-10-22 00:00:00
Tasks: Deep Learning

The application to search ranking is one of the biggest machine learning
success stories at Airbnb. Much of the initial gains were driven by a gradient
boosted decision tree model. The gains, however, plateaued over time. This
paper discusses the work done in applying neural networks in an attempt to
break out of that plateau. We present our perspective not with the intention of
pushing the frontier of new modeling techniques. Instead, ours is a story of
the elements we found useful in applying neural networks to a real life
product. Deep learning was steep learning for us. To other teams embarking on
similar journeys, we hope an account of our struggles and triumphs will provide
some useful pointers. Bon voyage!
The Lives of Bots
Authors: R. Stuart Geiger
Date: 2018-10-22 00:00:00

Automated software agents --- or bots --- have long been an important part of
how Wikipedia's volunteer community of editors write, edit, update, monitor,
and moderate content. In this paper, I discuss the complex social and technical
environment in which Wikipedia's bots operate. This paper focuses on the
establishment and role of English Wikipedia's bot policies and the Bot
Approvals Group, a volunteer committee that reviews applications for new bots
and helps resolve conflicts between Wikipedians about automation. In
particular, I examine an early bot controversy over the first bot in Wikipedia
to automatically enforce a social norm about how Wikipedian editors ought to
interact in discussion spaces. As I show, bots enforce many rules in Wikipedia,
but humans produce these bots and negotiate rules around their operation.
Because of the openness of Wikipedia's processes around automation, we can
vividly observe the often-invisible human work involved in such algorithmic
systems --- in stark contrast to most other user-generated content platforms.
Towards Universal Dialogue State Tracking
Authors: Liliang Ren, Kaige Xie, Lu Chen, Kai Yu
Date: 2018-10-22 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Dialogue State Tracking

Dialogue state tracking is the core part of a spoken dialogue system. It
estimates the beliefs of possible user's goals at every dialogue turn. However,
for most current approaches, it's difficult to scale to large dialogue domains.
They have one or more of following limitations: (a) Some models don't work in
the situation where slot values in ontology changes dynamically; (b) The number
of model parameters is proportional to the number of slots; (c) Some models
extract features based on hand-crafted lexicons. To tackle these challenges, we
propose StateNet, a universal dialogue state tracker. It is independent of the
number of values, shares parameters across all slots, and uses pre-trained word
vectors instead of explicit semantic dictionaries. Our experiments on two
datasets show that our approach not only overcomes the limitations, but also
significantly outperforms the performance of state-of-the-art approaches.
Model Selection Techniques -- An Overview
Authors: Jie Ding, Vahid Tarokh, Yuhong Yang
Date: 2018-10-22 00:00:00
Tasks: Epidemiology, model, Model Selection

In the era of big data, analysts usually explore various statistical models
or machine learning methods for observed data in order to facilitate scientific
discoveries or gain predictive power. Whatever data and fitting procedures are
employed, a crucial step is to select the most appropriate model or method from
a set of candidates. Model selection is a key ingredient in data analysis for
reliable and reproducible statistical inference or prediction, and thus central
to scientific studies in fields such as ecology, economics, engineering,
finance, political science, biology, and epidemiology. There has been a long
history of model selection techniques that arise from researches in statistics,
information theory, and signal processing. A considerable number of methods
have been proposed, following different philosophies and exhibiting varying
performances. The purpose of this article is to bring a comprehensive overview
of them, in terms of their motivation, large sample performance, and
applicability. We provide integrated and practically relevant discussions on
theoretical properties of state-of- the-art model selection approaches. We also
share our thoughts on some controversial views on the practice of model
selection.
Two-path 3D CNNs for calibration of system parameters for OCT-based motion compensation
Authors: Nils Gessert, Martin Gromniak, Matthias Schlüter, Alexander Schlaefer
Date: 2018-10-22 00:00:00
Tasks: Motion Compensation

Automatic motion compensation and adjustment of an intraoperative imaging
modality's field of view is a common problem during interventions. Optical
coherence tomography (OCT) is an imaging modality which is used in
interventions due to its high spatial resolution of few micrometers and its
temporal resolution of potentially several hundred volumes per second. However,
performing motion compensation with OCT is problematic due to its small field
of view which might lead to tracked objects being lost quickly. We propose a
novel deep learning-based approach that directly learns input parameters of
motors that move the scan area for motion compensation from optical coherence
tomography volumes. We design a two-path 3D convolutional neural network (CNN)
architecture that takes two volumes with an object to be tracked as its input
and predicts the necessary motor input parameters to compensate the object's
movement. In this way, we learn the calibration between object movement and
system parameters for motion compensation with arbitrary objects. Thus, we
avoid error-prone hand-eye calibration and handcrafted feature tracking from
classical approaches. We achieve an average correlation coefficient of 0.998
between predicted and ground-truth motor parameters which leads to sub-voxel
accuracy. Furthermore, we show that our deep learning model is real-time
capable for use with the system's high volume acquisition frequency.
A Fully Attention-Based Information Retriever
Authors: Alvaro Henrique Chaim Correia, Jorge Luiz Moreira Silva, Thiago de Castro Martins, Fabio Gagliardi Cozman
Date: 2018-10-22 00:00:00
Tasks: Question Answering

Recurrent neural networks are now the state-of-the-art in natural language
processing because they can build rich contextual representations and process
texts of arbitrary length. However, recent developments on attention mechanisms
have equipped feedforward networks with similar capabilities, hence enabling
faster computations due to the increase in the number of operations that can be
parallelized. We explore this new type of architecture in the domain of
question-answering and propose a novel approach that we call Fully Attention
Based Information Retriever (FABIR). We show that FABIR achieves competitive
results in the Stanford Question Answering Dataset (SQuAD) while having fewer
parameters and being faster at both learning and inference than rival methods.
Bioresorbable Scaffold Visualization in IVOCT Images Using CNNs and Weakly Supervised Localization
Authors: Nils Gessert, Sarah Latus, Youssef S. Abdelwahed, David M. Leistner, Matthias Lutz, Alexander Schlaefer
Date: 2018-10-22 00:00:00
Tasks: Feature Engineering, General Classification

Bioresorbable scaffolds have become a popular choice for treatment of
coronary heart disease, replacing traditional metal stents. Often,
intravascular optical coherence tomography is used to assess potential
malapposition after implantation and for follow-up examinations later on.
Typically, the scaffold is manually reviewed by an expert, analyzing each of
the hundreds of image slices. As this is time consuming, automatic stent
detection and visualization approaches have been proposed, mostly for metal
stent detection based on classic image processing. As bioresorbable scaffolds
are harder to detect, recent approaches have used feature extraction and
machine learning methods for automatic detection. However, these methods
require detailed, pixel-level labels in each image slice and extensive feature
engineering for the particular stent type which might limit the approaches'
generalization capabilities. Therefore, we propose a deep learning-based method
for bioresorbable scaffold visualization using only image-level labels. A
convolutional neural network is trained to predict whether an image slice
contains a metal stent, a bioresorbable scaffold, or no device. Then, we derive
local stent strut information by employing weakly supervised localization using
saliency maps with guided backpropagation. As saliency maps are generally
diffuse and noisy, we propose a novel patch-based method with image shifting
which allows for high resolution stent visualization. Our convolutional neural
network model achieves a classification accuracy of 99.0 % for image-level
stent classification which can be used for both high quality in-slice stent
visualization and 3D rendering of the stent structure.
Perturbation Bounds for Procrustes, Classical Scaling, and Trilateration, with Applications to Manifold Learning
Authors: Ery Arias-Castro, Adel Javanmard, Bruno Pelletier
Date: 2018-10-22 00:00:00
Tasks: Dimensionality Reduction

One of the common tasks in unsupervised learning is dimensionality reduction, where the goal is to find meaningful low-dimensional structures hidden in high-dimensional data. Sometimes referred to as manifold learning, this problem is closely related to the problem of localization, which aims at embedding a weighted graph into a low-dimensional Euclidean space. Several methods have been proposed for localization, and also manifold learning. Nonetheless, the robustness property of most of them is little understood. In this paper, we obtain perturbation bounds for classical scaling and trilateration, which are then applied to derive performance bounds for Isomap, Landmark Isomap, and Maximum Variance Unfolding. A new perturbation bound for procrustes analysis plays a key role.