pwc-archive/papers-with-abstracts huggingface dataset:
Formulation of Deep Reinforcement Learning Architecture Toward Autonomous Driving for On-Ramp Merge
Authors: Pin Wang, Ching-Yao Chan
Date: 2017-09-07 00:00:00
Tasks: Autonomous Driving, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Multiple automakers have in development or in production automated driving
systems (ADS) that offer freeway-pilot functions. This type of ADS is typically
limited to restricted-access freeways only, that is, the transition from manual
to automated modes takes place only after the ramp merging process is completed
manually. One major challenge to extend the automation to ramp merging is that
the automated vehicle needs to incorporate and optimize long-term objectives
(e.g. successful and smooth merge) when near-term actions must be safely
executed. Moreover, the merging process involves interactions with other
vehicles whose behaviors are sometimes hard to predict but may influence the
merging vehicle optimal actions. To tackle such a complicated control problem,
we propose to apply Deep Reinforcement Learning (DRL) techniques for finding an
optimal driving policy by maximizing the long-term reward in an interactive
environment. Specifically, we apply a Long Short-Term Memory (LSTM)
architecture to model the interactive environment, from which an internal state
containing historical driving information is conveyed to a Deep Q-Network
(DQN). The DQN is used to approximate the Q-function, which takes the internal
state as input and generates Q-values as output for action selection. With this
DRL architecture, the historical impact of interactive environment on the
long-term reward can be captured and taken into account for deciding the
optimal control policy. The proposed architecture has the potential to be
extended and applied to other autonomous driving scenarios such as driving
through a complex intersection or changing lanes under varying traffic flow
conditions.
Gotta Learn Fast: A New Benchmark for Generalization in RL
Authors: Alex Nichol, Vicki Pfau, Christopher Hesse, Oleg Klimov, John Schulman
Date: 2018-04-10 00:00:00
Tasks: Few-Shot Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

In this report, we present a new reinforcement learning (RL) benchmark based
on the Sonic the Hedgehog (TM) video game franchise. This benchmark is intended
to measure the performance of transfer learning and few-shot learning
algorithms in the RL domain. We also present and evaluate some baseline
algorithms on the new benchmark.
Ontology based Scene Creation for the Development of Automated Vehicles
Authors: Gerrit Bagschik, Till Menzel, Markus Maurer
Date: 2017-03-29 00:00:00

The introduction of automated vehicles without permanent human supervision
demands a functional system description, including functional system boundaries
and a comprehensive safety analysis. These inputs to the technical development
can be identified and analyzed by a scenario-based approach. Furthermore, to
establish an economical test and release process, a large number of scenarios
must be identified to obtain meaningful test results. Experts are doing well to
identify scenarios that are difficult to handle or unlikely to happen. However,
experts are unlikely to identify all scenarios possible based on the knowledge
they have on hand. Expert knowledge modeled for computer aided processing may
help for the purpose of providing a wide range of scenarios. This contribution
reviews ontologies as knowledge-based systems in the field of automated
vehicles, and proposes a generation of traffic scenes in natural language as a
basis for a scenario creation.
Discovering Style Trends through Deep Visually Aware Latent Item Embeddings
Authors: Murium Iqbal, Adair Kovac, Kamelia Aryafar
Date: 2018-04-23 00:00:00

In this paper, we explore Latent Dirichlet Allocation (LDA) and Polylingual
Latent Dirichlet Allocation (PolyLDA), as a means to discover trending styles
in Overstock from deep visual semantic features transferred from a pretrained
convolutional neural network and text-based item attributes. To utilize deep
visual semantic features in conjunction with LDA, we develop a method for
creating a bag of words representation of unrolled image vectors. By viewing
the channels within the convolutional layers of a Resnet-50 as being
representative of a word, we can index these activations to create visual
documents. We then train LDA over these documents to discover the latent style
in the images. We also incorporate text-based data with PolyLDA, where each
representation is viewed as an independent language attempting to describe the
same style. The resulting topics are shown to be excellent indicators of visual
style across our platform.
Simultaneous shot inversion for nonuniform geometries using fast data interpolation
Authors: Michelle Liu, Rajiv Kumar, Eldad Haber, Aleksandr Aravkin
Date: 2018-04-23 00:00:00
Tasks: Stochastic Optimization

Stochastic optimization is key to efficient inversion in PDE-constrained
optimization. Using 'simultaneous shots', or random superposition of source
terms, works very well in simple acquisition geometries where all sources see
all receivers, but this rarely occurs in practice. We develop an approach that
interpolates data to an ideal acquisition geometry while solving the inverse
problem using simultaneous shots. The approach is formulated as a joint inverse
problem, combining ideas from low-rank interpolation with full-waveform
inversion. Results using synthetic experiments illustrate the flexibility and
efficiency of the approach.
DeepDIVA: A Highly-Functional Python Framework for Reproducible Experiments
Authors: Michele Alberti, Vinaychandran Pondenkandath, Marcel Würsch, Rolf Ingold, Marcus Liwicki
Date: 2018-04-23 00:00:00

We introduce DeepDIVA: an infrastructure designed to enable quick and
intuitive setup of reproducible experiments with a large range of useful
analysis functionality. Reproducing scientific results can be a frustrating
experience, not only in document image analysis but in machine learning in
general. Using DeepDIVA a researcher can either reproduce a given experiment
with a very limited amount of information or share their own experiments with
others. Moreover, the framework offers a large range of functions, such as
boilerplate code, keeping track of experiments, hyper-parameter optimization,
and visualization of data and results. To demonstrate the effectiveness of this
framework, this paper presents case studies in the area of handwritten document
analysis where researchers benefit from the integrated functionality. DeepDIVA
is implemented in Python and uses the deep learning framework PyTorch. It is
completely open source, and accessible as Web Service through DIVAServices.
Boltzmann Encoded Adversarial Machines
Authors: Charles K. Fisher, Aaron M. Smith, Jonathan R. Walsh
Date: 2018-04-23 00:00:00

Restricted Boltzmann Machines (RBMs) are a class of generative neural network
that are typically trained to maximize a log-likelihood objective function. We
argue that likelihood-based training strategies may fail because the objective
does not sufficiently penalize models that place a high probability in regions
where the training data distribution has low probability. To overcome this
problem, we introduce Boltzmann Encoded Adversarial Machines (BEAMs). A BEAM is
an RBM trained against an adversary that uses the hidden layer activations of
the RBM to discriminate between the training data and the probability
distribution generated by the model. We present experiments demonstrating that
BEAMs outperform RBMs and GANs on multiple benchmarks.
Data-Driven Investigative Journalism For Connectas Dataset
Authors: Aniket Jain, Bhavya Sharma, Paridhi Choudhary, Rohan Sangave, William Yang
Date: 2018-04-23 00:00:00
Tasks: BIG-bench Machine Learning, Feature Engineering

The following paper explores the possibility of using Machine Learning
algorithms to detect the cases of corruption and malpractice by governments.
The dataset used by the authors contains information about several government
contracts in Colombia from year 2007 to 2012. The authors begin with exploring
and cleaning the data, followed by which they perform feature engineering
before finally implementing Machine Learning models to detect anomalies in the
given dataset.
Optimized Projections for Compressed Sensing via Direct Mutual Coherence Minimization
Authors: Canyi Lu, Huan Li, Zhouchen Lin
Date: 2015-08-13 00:00:00
Tasks: compressed sensing

Compressed Sensing (CS) is a novel technique for simultaneous signal sampling
and compression based on the existence of a sparse representation of signal and
a projected dictionary $PD$, where $P\in\mathbb{R}^{m\times d}$ is the
projection matrix and $D\in\mathbb{R}^{d\times n}$ is the dictionary. To
exactly recover the signal with a small number of measurements $m$, the
projected dictionary $PD$ is expected to be of low mutual coherence. Several
previous methods attempt to find the projection $P$ such that the mutual
coherence of $PD$ can be as low as possible. However, they do not minimize the
mutual coherence directly and thus their methods are far from optimal. Also the
solvers they used lack of the convergence guarantee and thus there has no
guarantee on the quality of their obtained solutions. This work aims to address
these issues. We propose to find an optimal projection by minimizing the mutual
coherence of $PD$ directly. This leads to a nonconvex nonsmooth minimization
problem. We then approximate it by smoothing and solve it by alternate
minimization. We further prove the convergence of our algorithm. To the best of
our knowledge, this is the first work which directly minimizes the mutual
coherence of the projected dictionary with a convergence guarantee. Numerical
experiments demonstrate that the proposed method can recover sparse signals
better than existing methods.
Integer Factorization with a Neuromorphic Sieve
Authors: John V. Monaco, Manuel M. Vindiola
Date: 2017-03-10 00:00:00

The bound to factor large integers is dominated by the computational effort
to discover numbers that are smooth, typically performed by sieving a
polynomial sequence. On a von Neumann architecture, sieving has log-log
amortized time complexity to check each value for smoothness. This work
presents a neuromorphic sieve that achieves a constant time check for
smoothness by exploiting two characteristic properties of neuromorphic
architectures: constant time synaptic integration and massively parallel
computation. The approach is validated by modifying msieve, one of the fastest
publicly available integer factorization implementations, to use the IBM
Neurosynaptic System (NS1e) as a coprocessor for the sieving stage.
Influencing Flock Formation in Low-Density Settings
Authors: Daniel Y. Fu, Emily S. Wang, Peter M. Krafft, Barbara J. Grosz
Date: 2018-04-23 00:00:00

Flocking is a coordinated collective behavior that results from local sensing
between individual agents that have a tendency to orient towards each other.
Flocking is common among animal groups and might also be useful in robotic
swarms. In the interest of learning how to control flocking behavior, recent
work in the multiagent systems literature has explored the use of influencing
agents for guiding flocking agents to face a target direction. The existing
work in this domain has focused on simulation settings of small areas with
toroidal shapes. In such settings, agent density is high, so interactions are
common, and flock formation occurs easily. In our work, we study new
environments with lower agent density, wherein interactions are more rare. We
study the efficacy of placement strategies and influencing agent behaviors
drawn from the literature, and find that the behaviors that have been shown to
work well in high-density conditions tend to be much less effective in lower
density environments. The source of this ineffectiveness is that the
influencing agents explored in prior work tended to face directions optimized
for maximal influence, but which actually separate the influencing agents from
the flock. We find that in low-density conditions maintaining a connection to
the flock is more important than rushing to orient towards the desired
direction. We use these insights to propose new influencing agent behaviors,
which we dub "follow-then-influence"; agents act like normal members of the
flock to achieve positions that allow for control and then exert their
influence. This strategy overcomes the difficulties posed by low density
environments.
Using Aspect Extraction Approaches to Generate Review Summaries and User Profiles
Authors: Christopher Mitcheltree, Skyler Wharton, Avneesh Saluja
Date: 2018-04-23 00:00:00
Proceeding: NAACL 2018 6
Tasks: Aspect Extraction, Reranking

Reviews of products or services on Internet marketplace websites contain a rich amount of information. Users often wish to survey reviews or review snippets from the perspective of a certain aspect, which has resulted in a large body of work on aspect identification and extraction from such corpora. In this work, we evaluate a newly-proposed neural model for aspect extraction on two practical tasks. The first is to extract canonical sentences of various aspects from reviews, and is judged by human evaluators against alternatives. A $k$-means baseline does remarkably well in this setting. The second experiment focuses on the suitability of the recovered aspect distributions to represent users by the reviews they have written. Through a set of review reranking experiments, we find that aspect-based profiles can largely capture notions of user preferences, by showing that divergent users generate markedly different review rankings.
Probabilistic Contraction Analysis of Iterated Random Operators
Authors: Abhishek Gupta, Rahul Jain, Peter Glynn
Date: 2018-04-04 00:00:00

In many branches of engineering, Banach contraction mapping theorem is employed to establish the convergence of certain deterministic algorithms. Randomized versions of these algorithms have been developed that have proved useful in data-driven problems. In a class of randomized algorithms, in each iteration, the contraction map is approximated with an operator that uses independent and identically distributed samples of certain random variables. This leads to iterated random operators acting on an initial point in a complete metric space, and it generates a Markov chain. In this paper, we develop a new stochastic dominance based proof technique, called probabilistic contraction analysis, for establishing the convergence in probability of Markov chains generated by such iterated random operators in certain limiting regime. The methods developed in this paper provides a general framework for understanding convergence of a wide variety of Monte Carlo methods in which contractive property is present. We apply the convergence result to conclude the convergence of fitted value iteration and fitted relative value iteration in continuous state and continuous action Markov decision problems as representative applications of the general framework developed here.
Fingerprint Match in Box
Authors: Joshua J. Engelsma, Kai Cao, Anil K. Jain
Date: 2018-04-23 00:00:00

We open source fingerprint Match in Box, a complete end-to-end fingerprint
recognition system embedded within a 4 inch cube. Match in Box stands in
contrast to a typical bulky and expensive proprietary fingerprint recognition
system which requires sending a fingerprint image to an external host for
processing and subsequent spoof detection and matching. In particular, Match in
Box is a first of a kind, portable, low-cost, and easy-to-assemble fingerprint
reader with an enrollment database embedded within the reader's memory and open
source fingerprint spoof detector, feature extractor, and matcher all running
on the reader's internal vision processing unit (VPU). An onboard touch screen
and rechargeable battery pack make this device extremely portable and ideal for
applying both fingerprint authentication (1:1 comparison) and fingerprint
identification (1:N search) to applications (vaccination tracking, food and
benefit distribution programs, human trafficking prevention) in rural
communities, especially in developing countries. We also show that Match in Box
is suited for capturing neonate fingerprints due to its high resolution (1900
ppi) cameras.
Beyond Keywords and Relevance: A Personalized Ad Retrieval Framework in E-Commerce Sponsored Search
Authors: Su Yan, Wei. Lin, Tianshu Wu, Daorui Xiao, Xu Zheng, Bo Wu, Kaipeng Liu
Date: 2017-12-29 00:00:00
Tasks: Retrieval

On most sponsored search platforms, advertisers bid on some keywords for
their advertisements (ads). Given a search request, ad retrieval module
rewrites the query into bidding keywords, and uses these keywords as keys to
select Top N ads through inverted indexes. In this way, an ad will not be
retrieved even if queries are related when the advertiser does not bid on
corresponding keywords. Moreover, most ad retrieval approaches regard rewriting
and ad-selecting as two separated tasks, and focus on boosting relevance
between search queries and ads. Recently, in e-commerce sponsored search more
and more personalized information has been introduced, such as user profiles,
long-time and real-time clicks. Personalized information makes ad retrieval
able to employ more elements (e.g. real-time clicks) as search signals and
retrieval keys, however it makes ad retrieval more difficult to measure ads
retrieved through different signals. To address these problems, we propose a
novel ad retrieval framework beyond keywords and relevance in e-commerce
sponsored search. Firstly, we employ historical ad click data to initialize a
hierarchical network representing signals, keys and ads, in which personalized
information is introduced. Then we train a model on top of the hierarchical
network by learning the weights of edges. Finally we select the best edges
according to the model, boosting RPM/CTR. Experimental results on our
e-commerce platform demonstrate that our ad retrieval framework achieves good
performance.
Rendition: Reclaiming what a black box takes away
Authors: Peyman Milanfar
Date: 2018-04-23 00:00:00

The premise of our work is deceptively familiar: A black box $f(\cdot)$ has
altered an image $\mathbf{x} \rightarrow f(\mathbf{x})$. Recover the image
$\mathbf{x}$. This black box might be any number of simple or complicated
things: a linear or non-linear filter, some app on your phone, etc. The latter
is a good canonical example for the problem we address: Given only "the app"
and an image produced by the app, find the image that was fed to the app. You
can run the given image (or any other image) through the app as many times as
you like, but you can not look inside the (code for the) app to see how it
works. At first blush, the problem sounds a lot like a standard inverse
problem, but it is not in the following sense: While we have access to the
black box $f(\cdot)$ and can run any image through it and observe the output,
we do not know how the block box alters the image. Therefore we have no
explicit form or model of $f(\cdot)$. Nor are we necessarily interested in the
internal workings of the black box. We are simply happy to reverse its effect
on a particular image, to whatever extent possible. This is what we call the
"rendition" (rather than restoration) problem, as it does not fit the mold of
an inverse problem (blind or otherwise). We describe general conditions under
which rendition is possible, and provide a remarkably simple algorithm that
works for both contractive and expansive black box operators. The principal and
novel take-away message from our work is this surprising fact: One simple
algorithm can reliably undo a wide class of (not too violent) image
distortions.
  A higher quality pdf of this paper is available at http://www.milanfar.org
Design and Analysis of the NIPS 2016 Review Process
Authors: Nihar B. Shah, Behzad Tabibian, Krikamol Muandet, Isabelle Guyon, Ulrike Von Luxburg
Date: 2017-08-31 00:00:00

Neural Information Processing Systems (NIPS) is a top-tier annual conference
in machine learning. The 2016 edition of the conference comprised more than
2,400 paper submissions, 3,000 reviewers, and 8,000 attendees. This represents
a growth of nearly 40% in terms of submissions, 96% in terms of reviewers, and
over 100% in terms of attendees as compared to the previous year. The massive
scale as well as rapid growth of the conference calls for a thorough quality
assessment of the peer-review process and novel means of improvement. In this
paper, we analyze several aspects of the data collected during the review
process, including an experiment investigating the efficacy of collecting
ordinal rankings from reviewers. Our goal is to check the soundness of the
review process, and provide insights that may be useful in the design of the
review process of subsequent conferences.
A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results
Authors: Beau Coker, Cynthia Rudin, Gary King
Date: 2018-04-23 00:00:00

Inference is the process of using facts we know to learn about facts we do not know. A theory of inference gives assumptions necessary to get from the former to the latter, along with a definition for and summary of the resulting uncertainty. Any one theory of inference is neither right nor wrong, but merely an axiom that may or may not be useful. Each of the many diverse theories of inference can be valuable for certain applications. However, no existing theory of inference addresses the tendency to choose, from the range of plausible data analysis specifications consistent with prior evidence, those that inadvertently favor one's own hypotheses. Since the biases from these choices are a growing concern across scientific fields, and in a sense the reason the scientific community was invented in the first place, we introduce a new theory of inference designed to address this critical problem. We introduce hacking intervals, which are the range of a summary statistic one may obtain given a class of possible endogenous manipulations of the data. Hacking intervals require no appeal to hypothetical data sets drawn from imaginary superpopulations. A scientific result with a small hacking interval is more robust to researcher manipulation than one with a larger interval, and is often easier to interpret than a classical confidence interval. Some versions of hacking intervals turn out to be equivalent to classical confidence intervals, which means they may also provide a more intuitive and potentially more useful interpretation of classical confidence intervals.
On Abruptly-Changing and Slowly-Varying Multiarmed Bandit Problems
Authors: Lai Wei, Vaibhav Srivastava
Date: 2018-02-23 00:00:00

We study the non-stationary stochastic multiarmed bandit (MAB) problem and
propose two generic algorithms, namely, the limited memory deterministic
sequencing of exploration and exploitation (LM-DSEE) and the Sliding-Window
Upper Confidence Bound# (SW-UCB#). We rigorously analyze these algorithms in
abruptly-changing and slowly-varying environments and characterize their
performance. We show that the expected cumulative regret for these algorithms
under either of the environments is upper bounded by sublinear functions of
time, i.e., the time average of the regret asymptotically converges to zero. We
complement our analytic results with numerical illustrations.