pwc-archive/papers-with-abstracts huggingface dataset:
Character-based Surprisal as a Model of Reading Difficulty in the Presence of Error
Authors: Michael Hahn, Frank Keller, Yonatan Bisk, Yonatan Belinkov
Date: 2019-02-02 00:00:00

Intuitively, human readers cope easily with errors in text; typos, misspelling, word substitutions, etc. do not unduly disrupt natural reading. Previous work indicates that letter transpositions result in increased reading times, but it is unclear if this effect generalizes to more natural errors. In this paper, we report an eye-tracking study that compares two error types (letter transpositions and naturally occurring misspelling) and two error rates (10% or 50% of all words contain errors). We find that human readers show unimpaired comprehension in spite of these errors, but error words cause more reading difficulty than correct words. Also, transpositions are more difficult than misspellings, and a high error rate increases difficulty for all words, including correct ones. We then present a computational model that uses character-based (rather than traditional word-based) surprisal to account for these results. The model explains that transpositions are harder than misspellings because they contain unexpected letter combinations. It also explains the error rate effect: upcoming words are more difficultto predict when the context is degraded, leading to increased surprisal.
Multi-step Reasoning via Recurrent Dual Attention for Visual Dialog
Authors: Zhe Gan, Yu Cheng, Ahmed El Kholy, Linjie Li, Jingjing Liu, Jianfeng Gao
Date: 2019-02-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Question Answering, Visual Dialog

This paper presents a new model for visual dialog, Recurrent Dual Attention Network (ReDAN), using multi-step reasoning to answer a series of questions about an image. In each question-answering turn of a dialog, ReDAN infers the answer progressively through multiple reasoning steps. In each step of the reasoning process, the semantic representation of the question is updated based on the image and the previous dialog history, and the recurrently-refined representation is used for further reasoning in the subsequent step. On the VisDial v1.0 dataset, the proposed ReDAN model achieves a new state-of-the-art of 64.47% NDCG score. Visualization on the reasoning process further demonstrates that ReDAN can locate context-relevant visual and textual clues via iterative refinement, which can lead to the correct answer step-by-step.
Robustness of Generalized Learning Vector Quantization Models against Adversarial Attacks
Authors: Sascha Saralajew, Lars Holdijk, Maike Rees, Thomas Villmann
Date: 2019-02-01 00:00:00
Tasks: Quantization

Adversarial attacks and the development of (deep) neural networks robust
against them are currently two widely researched topics. The robustness of
Learning Vector Quantization (LVQ) models against adversarial attacks has
however not yet been studied to the same extent. We therefore present an
extensive evaluation of three LVQ models: Generalized LVQ, Generalized Matrix
LVQ and Generalized Tangent LVQ. The evaluation suggests that both Generalized
LVQ and Generalized Tangent LVQ have a high base robustness, on par with the
current state-of-the-art in robust neural network methods. In contrast to this,
Generalized Matrix LVQ shows a high susceptibility to adversarial attacks,
scoring consistently behind all other models. Additionally, our numerical
evaluation indicates that increasing the number of prototypes per class
improves the robustness of the models.
Visual Rationalizations in Deep Reinforcement Learning for Atari Games
Authors: Laurens Weitkamp, Elise van der Pol, Zeynep Akata
Date: 2019-02-01 00:00:00
Tasks: Atari Games, Decision Making, Deep Learning, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Due to the capability of deep learning to perform well in high dimensional
problems, deep reinforcement learning agents perform well in challenging tasks
such as Atari 2600 games. However, clearly explaining why a certain action is
taken by the agent can be as important as the decision itself. Deep
reinforcement learning models, as other deep learning models, tend to be opaque
in their decision-making process. In this work, we propose to make deep
reinforcement learning more transparent by visualizing the evidence on which
the agent bases its decision. In this work, we emphasize the importance of
producing a justification for an observed action, which could be applied to a
black-box decision agent.
Forecasting Intra-Hour Imbalances in Electric Power Systems
Authors: Tárik S. Salem, Karan Kathuria, Heri Ramampiaro, Helge Langseth
Date: 2019-02-01 00:00:00

Keeping the electricity production in balance with the actual demand is
becoming a difficult and expensive task in spite of an involvement of
experienced human operators. This is due to the increasing complexity of the
electric power grid system with the intermittent renewable production as one of
the contributors. A beforehand information about an occurring imbalance can
help the transmission system operator to adjust the production plans, and thus
ensure a high security of supply by reducing the use of costly balancing
reserves, and consequently reduce undesirable fluctuations of the 50 Hz power
system frequency. In this paper, we introduce the relatively new problem of an
intra-hour imbalance forecasting for the transmission system operator (TSO). We
focus on the use case of the Norwegian TSO, Statnett. We present a
complementary imbalance forecasting tool that is able to support the TSO in
determining the trend of future imbalances, and show the potential to
proactively alleviate imbalances with a higher accuracy compared to the
contemporary solution.
The Spatially-Conscious Machine Learning Model
Authors: Timothy J. Kiely, Nathaniel D. Bastian
Date: 2019-02-01 00:00:00
Tasks: BIG-bench Machine Learning, Feature Engineering, General Classification, model, regression

Successfully predicting gentrification could have many social and commercial
applications; however, real estate sales are difficult to predict because they
belong to a chaotic system comprised of intrinsic and extrinsic
characteristics, perceived value, and market speculation. Using New York City
real estate as our subject, we combine modern techniques of data science and
machine learning with traditional spatial analysis to create robust real estate
prediction models for both classification and regression tasks. We compare
several cutting edge machine learning algorithms across spatial, semi-spatial
and non-spatial feature engineering techniques, and we empirically show that
spatially-conscious machine learning models outperform non-spatial models when
married with advanced prediction techniques such as feed-forward artificial
neural networks and gradient boosting machine models.
2D and 3D Vascular Structures Enhancement via Multiscale Fractional Anisotropy Tensor
Authors: Haifa F. Alhasson, Shuaa S. Alharbi, Boguslaw Obara
Date: 2019-02-01 00:00:00

The detection of vascular structures from noisy images is a fundamental
process for extracting meaningful information in many applications. Most
well-known vascular enhancing techniques often rely on Hessian-based filters.
This paper investigates the feasibility and deficiencies of detecting
curve-like structures using a Hessian matrix. The main contribution is a novel
enhancement function, which overcomes the deficiencies of established methods.
Our approach has been evaluated quantitatively and qualitatively using
synthetic examples and a wide range of real 2D and 3D biomedical images.
Compared with other existing approaches, the experimental results prove that
our proposed approach achieves high-quality curvilinear structure enhancement.
The Efficacy of SHIELD under Different Threat Models
Authors: Cory Cornelius, Nilaksh Das, Shang-Tse Chen, Li Chen, Michael E. Kounavis, Duen Horng Chau
Date: 2019-02-01 00:00:00
Tasks: Adversarial Attack, image-classification, Image Classification

In this appraisal paper, we evaluate the efficacy of SHIELD, a compression-based defense framework for countering adversarial attacks on image classification models, which was published at KDD 2018. Here, we consider alternative threat models not studied in the original work, where we assume that an adaptive adversary is aware of the ensemble defense approach, the defensive pre-processing, and the architecture and weights of the models used in the ensemble. We define scenarios with varying levels of threat and empirically analyze the proposed defense by varying the degree of information available to the attacker, spanning from a full white-box attack to the gray-box threat model described in the original work. To evaluate the robustness of the defense against an adaptive attacker, we consider the targeted-attack success rate of the Projected Gradient Descent (PGD) attack, which is a strong gradient-based adversarial attack proposed in adversarial machine learning research. We also experiment with training the SHIELD ensemble from scratch, which is different from re-training using a pre-trained model as done in the original work. We find that the targeted PGD attack has a success rate of 64.3% against the original SHIELD ensemble in the full white box scenario, but this drops to 48.9% if the models used in the ensemble are trained from scratch instead of being retrained. Our experiments further reveal that an ensemble whose models are re-trained indeed have higher correlation in the cosine similarity space, and models that are trained from scratch are less vulnerable to targeted attacks in the white-box and gray-box scenarios.
Comparison of Patch-Based Conditional Generative Adversarial Neural Net Models with Emphasis on Model Robustness for Use in Head and Neck Cases for MR-Only planning
Authors: Peter Klages, Ilyes Benslimane, Sadegh Riyahi, Jue Jiang, Margie Hunt, Joe Deasy, Harini Veeraraghavan, Neelam Tyagi
Date: 2019-02-01 00:00:00
Tasks: Anatomy

A total of twenty paired CT and MR images were used in this study to
investigate two conditional generative adversarial networks, Pix2Pix, and Cycle
GAN, for generating synthetic CT images for Headand Neck cancer cases. Ten of
the patient cases were used for training and included such common artifacts as
dental implants; the remaining ten testing cases were used for testing and
included a larger range of image features commonly found in clinical head and
neck cases. These features included strong metal artifacts from dental
implants, one case with a metal implant, and one case with abnormal anatomy.
The original CT images were deformably registered to the mDixon FFE MR images
to minimize the effects of processing the MR images. The sCT generation
accuracy and robustness were evaluated using Mean Absolute Error (MAE) based on
the Hounsfield Units (HU) for three regions (whole body, bone, and air within
the body), Mean Error (ME) to observe systematic average offset errors in the
sCT generation, and dosimetric evaluation of all clinically relevant
structures. For the test set the MAE for the Pix2Pix and Cycle GAN models were
92.4 $\pm$ 13.5 HU, and 100.7 $\pm$ 14.6 HU, respectively, for the body region,
166.3 $\pm$ 31.8 HU, and 184 $\pm$ 31.9 HU, respectively, for the bone region,
and 183.7 $\pm$ 41.3 HU and 185.4 $\pm$ 37.9 HU for the air regions. The ME for
Pix2Pix and Cycle GAN were 21.0 $\pm$ 11.8 HU and 37.5 $\pm$ 14.9 HU,
respectively. Absolute Percent Mean/Max Dose Errors were less than 2% for the
PTV and all critical structures for both models, and DRRs generated from these
models looked qualitatively similar to CT generated DRRs showing these methods
are promising for MR-only planning.
Hyper-parameter Tuning under a Budget Constraint
Authors: Zhiyun Lu, Chao-Kai Chiang, Fei Sha
Date: 2019-02-01 00:00:00
Tasks: Decision Making, Sequential Decision Making

We study a budgeted hyper-parameter tuning problem, where we optimize the
tuning result under a hard resource constraint. We propose to solve it as a
sequential decision making problem, such that we can use the partial training
progress of configurations to dynamically allocate the remaining budget. Our
algorithm combines a Bayesian belief model which estimates the future
performance of configurations, with an action-value function which balances
exploration-exploitation tradeoff, to optimize the final output. It
automatically adapts the tuning behaviors to different constraints, which is
useful in practice. Experiment results demonstrate superior performance over
existing algorithms, including the-state-of-the-art one, on real-world tuning
tasks across a range of different budgets.
Competitive Experience Replay
Authors: Hao Liu, Alexander Trott, Richard Socher, Caiming Xiong
Date: 2019-02-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep learning has achieved remarkable successes in solving challenging
reinforcement learning (RL) problems when dense reward function is provided.
However, in sparse reward environment it still often suffers from the need to
carefully shape reward function to guide policy optimization. This limits the
applicability of RL in the real world since both reinforcement learning and
domain-specific knowledge are required. It is therefore of great practical
importance to develop algorithms which can learn from a binary signal
indicating successful task completion or other unshaped, sparse reward signals.
We propose a novel method called competitive experience replay, which
efficiently supplements a sparse reward by placing learning in the context of
an exploration competition between a pair of agents. Our method complements the
recently proposed hindsight experience replay (HER) by inducing an automatic
exploratory curriculum. We evaluate our approach on the tasks of reaching
various goal locations in an ant maze and manipulating objects with a robotic
arm. Each task provides only binary rewards indicating whether or not the goal
is achieved. Our method asymmetrically augments these sparse rewards for a pair
of agents each learning the same task, creating a competitive game designed to
drive exploration. Extensive experiments demonstrate that this method leads to
faster converge and improved task performance.
Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era
Authors: Gabrielle Allen, Igor Andreoni, Etienne Bachelet, G. Bruce Berriman, Federica B. Bianco, Rahul Biswas, Matias Carrasco Kind, Kyle Chard, Minsik Cho, Philip S. Cowperthwaite, Zachariah B. Etienne, Daniel George, Tom Gibbs, Matthew Graham, William Gropp, Anushri Gupta, Roland Haas, E. A. Huerta, Elise Jennings, Daniel S. Katz, Asad Khan, Volodymyr Kindratenko, William T. C. Kramer, Xin Liu, Ashish Mahabal, Kenton McHenry, J. M. Miller, M. S. Neubauer, Steve Oberlin, Alexander R. Olivas Jr, Shawn Rosofsky, Milton Ruiz, Aaron Saxton, Bernard Schutz, Alex Schwing, Ed Seidel, Stuart L. Shapiro, Hongyu Shen, Yue Shen, Brigitta M. Sipőcz, Lunan Sun, John Towns, Antonios Tsokaros, Wei Wei, Jack Wells, Timothy J. Williams, JinJun Xiong, Zhizhen Zhao
Date: 2019-02-01 00:00:00
Tasks: Astronomy, Management, scientific discovery

This report provides an overview of recent work that harnesses the Big Data
Revolution and Large Scale Computing to address grand computational challenges
in Multi-Messenger Astrophysics, with a particular emphasis on real-time
discovery campaigns. Acknowledging the transdisciplinary nature of
Multi-Messenger Astrophysics, this document has been prepared by members of the
physics, astronomy, computer science, data science, software and
cyberinfrastructure communities who attended the NSF-, DOE- and NVIDIA-funded
"Deep Learning for Multi-Messenger Astrophysics: Real-time Discovery at Scale"
workshop, hosted at the National Center for Supercomputing Applications,
October 17-19, 2018. Highlights of this report include unanimous agreement that
it is critical to accelerate the development and deployment of novel,
signal-processing algorithms that use the synergy between artificial
intelligence (AI) and high performance computing to maximize the potential for
scientific discovery with Multi-Messenger Astrophysics. We discuss key aspects
to realize this endeavor, namely (i) the design and exploitation of scalable
and computationally efficient AI algorithms for Multi-Messenger Astrophysics;
(ii) cyberinfrastructure requirements to numerically simulate astrophysical
sources, and to process and interpret Multi-Messenger Astrophysics data; (iii)
management of gravitational wave detections and triggers to enable
electromagnetic and astro-particle follow-ups; (iv) a vision to harness future
developments of machine and deep learning and cyberinfrastructure resources to
cope with the scale of discovery in the Big Data Era; (v) and the need to build
a community that brings domain experts together with data scientists on equal
footing to maximize and accelerate discovery in the nascent field of
Multi-Messenger Astrophysics.
Robustness Certificates Against Adversarial Examples for ReLU Networks
Authors: Sahil Singla, Soheil Feizi
Date: 2019-02-01 00:00:00
Tasks: General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

While neural networks have achieved high performance in different learning
tasks, their accuracy drops significantly in the presence of small adversarial
perturbations to inputs. Defenses based on regularization and adversarial
training are often followed by new attacks to defeat them. In this paper, we
propose attack-agnostic robustness certificates for a multi-label
classification problem using a deep ReLU network. Although computing the exact
distance of a given input sample to the classification decision boundary
requires solving a non-convex optimization, we characterize two lower bounds
for such distances, namely the simplex certificate and the decision boundary
certificate. These robustness certificates leverage the piece-wise linear
structure of ReLU networks and use the fact that in a polyhedron around a given
sample, the prediction function is linear. In particular, the proposed simplex
certificate has a closed-form, is differentiable and is an order of magnitude
faster to compute than the existing methods even for deep networks. In addition
to theoretical bounds, we provide numerical results for our certificates over
MNIST and compare them with some existing upper bounds.
Local minimax rates for closeness testing of discrete distributions
Authors: Joseph Lam-Weil, Alexandra Carpentier, Bharath K. Sriperumbudur
Date: 2019-02-01 00:00:00
Tasks: Two-sample testing

We consider the closeness testing problem for discrete distributions. The goal is to distinguish whether two samples are drawn from the same unspecified distribution, or whether their respective distributions are separated in $L_1$-norm. In this paper, we focus on adapting the rate to the shape of the underlying distributions, i.e. we consider \textit{a local minimax setting}. We provide, to the best of our knowledge, the first local minimax rate for the separation distance up to logarithmic factors, together with a test that achieves it. In view of the rate, closeness testing turns out to be substantially harder than the related one-sample testing problem over a wide range of cases.
Adaptive Gradient for Adversarial Perturbations Generation
Authors: Yatie Xiao, Chi-Man Pun
Date: 2019-02-01 00:00:00
Tasks: Image Classification

Deep Neural Networks have achieved remarkable success in computer vision, natural language processing, and audio tasks.
Estimating the Mixing Time of Ergodic Markov Chains
Authors: Geoffrey Wolfer, Aryeh Kontorovich
Date: 2019-02-01 00:00:00

We address the problem of estimating the mixing time $t_{\mathsf{mix}}$ of an arbitrary ergodic finite-state Markov chain from a single trajectory of length $m$. The reversible case was addressed by Hsu et al. [2019], who left the general case as an open problem. In the reversible case, the analysis is greatly facilitated by the fact that the Markov operator is self-adjoint, and Weyl's inequality allows for a dimension-free perturbation analysis of the empirical eigenvalues. As Hsu et al. point out, in the absence of reversibility (which induces asymmetric pair probabilities matrices), the existing perturbation analysis has a worst-case exponential dependence on the number of states $d$. Furthermore, even if an eigenvalue perturbation analysis with better dependence on $d$ were available, in the non-reversible case the connection between the spectral gap and the mixing time is not nearly as straightforward as in the reversible case. Our key insight is to estimate the pseudo-spectral gap $\gamma_{\mathsf{ps}}$ instead, which allows us to overcome the loss of symmetry and to achieve a polynomial dependence on the minimal stationary probability $\pi_\star$ and $\gamma_{\mathsf{ps}}$. Additionally, in the reversible case, we obtain simultaneous nearly (up to logarithmic factors) minimax rates in $t_{\mathsf{mix}}$ and precision $\varepsilon$, closing a gap in Hsu et al., who treated $\varepsilon$ as constant in the lower bounds. Finally, we construct fully empirical confidence intervals for $\gamma_{\mathsf{ps}}$, which shrink to zero at a rate of roughly $1/\sqrt{m}$, and improve the state of the art in even the reversible case.
A New Family of Neural Networks Provably Resistant to Adversarial Attacks
Authors: Rakshit Agrawal, Luca de Alfaro, David Helmbold
Date: 2019-02-01 00:00:00

Adversarial attacks add perturbations to the input features with the intent
of changing the classification produced by a machine learning system. Small
perturbations can yield adversarial examples which are misclassified despite
being virtually indistinguishable from the unperturbed input. Classifiers
trained with standard neural network techniques are highly susceptible to
adversarial examples, allowing an adversary to create misclassifications of
their choice.
  We introduce a new type of network unit, called MWD (max of weighed distance)
units that have a built-in resistant to adversarial attacks. These units are
highly non-linear, and we develop the techniques needed to effectively train
them. We show that simple interval techniques for propagating perturbation
effects through the network enables the efficient computation of robustness
(i.e., accuracy guarantees) for MWD networks under any perturbations, including
adversarial attacks.
  MWD networks are significantly more robust to input perturbations than ReLU
networks. On permutation invariant MNIST, when test examples can be perturbed
by 20% of the input range, MWD networks provably retain accuracy above 83%,
while the accuracy of ReLU networks drops below 5%. The provable accuracy of
MWD networks is superior even to the observed accuracy of ReLU networks trained
with the help of adversarial examples. In the absence of adversarial attacks,
MWD networks match the performance of sigmoid networks, and have accuracy only
slightly below that of ReLU networks.