pwc-archive/papers-with-abstracts huggingface dataset:
An Explainable Adversarial Robustness Metric for Deep Learning Neural Networks
Authors: Chirag Agarwal, Bo Dong, Dan Schonfeld, Anthony Hoogs
Date: 2018-06-05 00:00:00
Tasks: Adversarial Attack, Adversarial Robustness, Deep Learning, Machine Translation, speech-recognition, Speech Recognition

Deep Neural Networks(DNN) have excessively advanced the field of computer
vision by achieving state of the art performance in various vision tasks. These
results are not limited to the field of vision but can also be seen in speech
recognition and machine translation tasks. Recently, DNNs are found to poorly
fail when tested with samples that are crafted by making imperceptible changes
to the original input images. This causes a gap between the validation and
adversarial performance of a DNN. An effective and generalizable robustness
metric for evaluating the performance of DNN on these adversarial inputs is
still missing from the literature. In this paper, we propose Noise Sensitivity
Score (NSS), a metric that quantifies the performance of a DNN on a specific
input under different forms of fix-directional attacks. An insightful
mathematical explanation is provided for deeply understanding the proposed
metric. By leveraging the NSS, we also proposed a skewness based dataset
robustness metric for evaluating a DNN's adversarial performance on a given
dataset. Extensive experiments using widely used state of the art architectures
along with popular classification datasets, such as MNIST, CIFAR-10, CIFAR-100,
and ImageNet, are used to validate the effectiveness and generalization of our
proposed metrics. Instead of simply measuring a DNN's adversarial robustness in
the input domain, as previous works, the proposed NSS is built on top of
insightful mathematical understanding of the adversarial attack and gives a
more explicit explanation of the robustness.
A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music
Authors: Adam Roberts, Jesse Engel, Colin Raffel, Curtis Hawthorne, Douglas Eck
Date: 2018-03-13 00:00:00
Proceeding: ICML 2018 7
Tasks: Decoder

The Variational Autoencoder (VAE) has proven to be an effective model for producing semantically meaningful latent representations for natural data. However, it has thus far seen limited application to sequential data, and, as we demonstrate, existing recurrent VAE models have difficulty modeling sequences with long-term structure. To address this issue, we propose the use of a hierarchical decoder, which first outputs embeddings for subsequences of the input and then uses these embeddings to generate each subsequence independently. This structure encourages the model to utilize its latent code, thereby avoiding the "posterior collapse" problem, which remains an issue for recurrent VAEs. We apply this architecture to modeling sequences of musical notes and find that it exhibits dramatically better sampling, interpolation, and reconstruction performance than a "flat" baseline model. An implementation of our "MusicVAE" is available online at http://g.co/magenta/musicvae-code.
Beyond Trees: Classification with Sparse Pairwise Dependencies
Authors: Yaniv Tenzer, Amit Moscovich, Mary Frances Dorn, Boaz Nadler, Clifford Spiegelman
Date: 2018-06-06 00:00:00
Tasks: Classification, General Classification

Several classification methods assume that the underlying distributions follow tree-structured graphical models. Indeed, trees capture statistical dependencies between pairs of variables, which may be crucial to attain low classification errors. The resulting classifier is linear in the log-transformed univariate and bivariate densities that correspond to the tree edges. In practice, however, observed data may not be well approximated by trees. Yet, motivated by the importance of pairwise dependencies for accurate classification, here we propose to approximate the optimal decision boundary by a sparse linear combination of the univariate and bivariate log-transformed densities. Our proposed approach is semi-parametric in nature: we non-parametrically estimate the univariate and bivariate densities, remove pairs of variables that are nearly independent using the Hilbert-Schmidt independence criteria, and finally construct a linear SVM on the retained log-transformed densities. We demonstrate using both synthetic and real data that our resulting classifier, denoted SLB (Sparse Log-Bivariate density), is competitive with popular classification methods.
A Classification-Based Study of Covariate Shift in GAN Distributions
Authors: Shibani Santurkar, Ludwig Schmidt, Aleksander Mądry
Date: 2017-11-02 00:00:00
Proceeding: ICML 2018 7
Tasks: Classification, Diversity, General Classification

A basic, and still largely unanswered, question in the context of Generative
Adversarial Networks (GANs) is whether they are truly able to capture all the
fundamental characteristics of the distributions they are trained on. In
particular, evaluating the diversity of GAN distributions is challenging and
existing methods provide only a partial understanding of this issue. In this
paper, we develop quantitative and scalable tools for assessing the diversity
of GAN distributions. Specifically, we take a classification-based perspective
and view loss of diversity as a form of covariate shift introduced by GANs. We
examine two specific forms of such shift: mode collapse and boundary
distortion. In contrast to prior work, our methods need only minimal human
supervision and can be readily applied to state-of-the-art GANs on large,
canonical datasets. Examining popular GANs using our tools indicates that these
GANs have significant problems in reproducing the more distributional
properties of their training dataset.
Robust Structured Multi-task Multi-view Sparse Tracking
Authors: Mohammadreza Javanmardi, Xiaojun Qi
Date: 2018-06-06 00:00:00
Tasks: Visual Tracking

Sparse representation is a viable solution to visual tracking. In this paper,
we propose a structured multi-task multi-view tracking (SMTMVT) method, which
exploits the sparse appearance model in the particle filter framework to track
targets under different challenges. Specifically, we extract features of the
target candidates from different views and sparsely represent them by a linear
combination of templates of different views. Unlike the conventional sparse
trackers, SMTMVT not only jointly considers the relationship between different
tasks and different views but also retains the structures among different views
in a robust multi-task multi-view formulation. We introduce a numerical
algorithm based on the proximal gradient method to quickly and effectively find
the sparsity by dividing the optimization problem into two subproblems with the
closed-form solutions. Both qualitative and quantitative evaluations on the
benchmark of challenging image sequences demonstrate the superior performance
of the proposed tracker against various state-of-the-art trackers.
Cluster Naturalistic Driving Encounters Using Deep Unsupervised Learning
Authors: Sisi Li, Wenshuo Wang, Zhaobin Mo, Ding Zhao
Date: 2018-02-28 00:00:00
Tasks: Clustering, Self-Driving Cars

Learning knowledge from driving encounters could help self-driving cars make
appropriate decisions when driving in complex settings with nearby vehicles
engaged. This paper develops an unsupervised classifier to group naturalistic
driving encounters into distinguishable clusters by combining an auto-encoder
with k-means clustering (AE-kMC). The effectiveness of AE-kMC was validated
using the data of 10,000 naturalistic driving encounters which were collected
by the University of Michigan, Ann Arbor in the past five years. We compare our
developed method with the $k$-means clustering methods and experimental results
demonstrate that the AE-kMC method outperforms the original k-means clustering
method.
Reinforced Mnemonic Reader for Machine Reading Comprehension
Authors: Minghao Hu, Yuxing Peng, Zhen Huang, Xipeng Qiu, Furu Wei, Ming Zhou
Date: 2017-05-08 00:00:00
Tasks: Machine Reading Comprehension, Question Answering, Reading Comprehension, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we introduce the Reinforced Mnemonic Reader for machine
reading comprehension tasks, which enhances previous attentive readers in two
aspects. First, a reattention mechanism is proposed to refine current
attentions by directly accessing to past attentions that are temporally
memorized in a multi-round alignment architecture, so as to avoid the problems
of attention redundancy and attention deficiency. Second, a new optimization
approach, called dynamic-critical reinforcement learning, is introduced to
extend the standard supervised method. It always encourages to predict a more
acceptable answer so as to address the convergence suppression problem occurred
in traditional reinforcement learning algorithms. Extensive experiments on the
Stanford Question Answering Dataset (SQuAD) show that our model achieves
state-of-the-art results. Meanwhile, our model outperforms previous systems by
over 6% in terms of both Exact Match and F1 metrics on two adversarial SQuAD
datasets.
A Variational Image Segmentation Model based on Normalized Cut with Adaptive Similarity and Spatial Regularization
Authors: Faqiang Wang, Cuicui Zhao, Jun Liu, Hai-yang Huang
Date: 2018-06-06 00:00:00
Tasks: Clustering, Graph Clustering, Image Segmentation, Segmentation, Semantic Segmentation, Spectral Graph Clustering

Image segmentation is a fundamental research topic in image processing and computer vision. In the last decades, researchers developed a large number of segmentation algorithms for various applications. Amongst these algorithms, the Normalized cut (Ncut) segmentation method is widely applied due to its good performance. The Ncut segmentation model is an optimization problem whose energy is defined on a specifically designed graph. Thus, the segmentation results of the existing Ncut method are largely dependent on a pre-constructed similarity measure on the graph since this measure is usually given empirically by users. This flaw will lead to some undesirable segmentation results. In this paper, we propose a Ncut-based segmentation algorithm by integrating an adaptive similarity measure and spatial regularization. The proposed model combines the Parzen-Rosenblatt window method, non-local weights entropy, Ncut energy, and regularizer of phase field in a variational framework. Our method can adaptively update the similarity measure function by estimating some parameters. This adaptive procedure enables the proposed algorithm finding a better similarity measure for classification than the Ncut method. We provide some mathematical interpretation of the proposed adaptive similarity from multi-viewpoints such as statistics and convex optimization. In addition, the regularizer of phase field can guarantee that the proposed algorithm has a robust performance in the presence of noise, and it can also rectify the similarity measure with a spatial priori. The well-posed theory such as the existence of the minimizer for the proposed model is given in the paper. Compared with some existing segmentation methods such as the traditional Ncut-based model and the classical Chan-Vese model, the numerical experiments show that our method can provide promising segmentation results.
Distributionally Robust Submodular Maximization
Authors: Matthew Staib, Bryan Wilder, Stefanie Jegelka
Date: 2018-02-14 00:00:00

Submodular functions have applications throughout machine learning, but in
many settings, we do not have direct access to the underlying function $f$. We
focus on stochastic functions that are given as an expectation of functions
over a distribution $P$. In practice, we often have only a limited set of
samples $f_i$ from $P$. The standard approach indirectly optimizes $f$ by
maximizing the sum of $f_i$. However, this ignores generalization to the true
(unknown) distribution. In this paper, we achieve better performance on the
actual underlying function $f$ by directly optimizing a combination of bias and
variance. Algorithmically, we accomplish this by showing how to carry out
distributionally robust optimization (DRO) for submodular functions, providing
efficient algorithms backed by theoretical guarantees which leverage several
novel contributions to the general theory of DRO. We also show compelling
empirical evidence that DRO improves generalization to the unknown stochastic
submodular function.
Detecting Comma-shaped Clouds for Severe Weather Forecasting using Shape and Motion
Authors: Xinye Zheng, Jianbo Ye, Yukun Chen, Stephen Wistar, Jia Li, Jose A. Piedra-Fernández, Michael A. Steinberg, James Z. Wang
Date: 2018-02-25 00:00:00
Tasks: Weather Forecasting

Meteorologists use shapes and movements of clouds in satellite images as
indicators of several major types of severe storms. Satellite imaginary data
are in increasingly higher resolution, both spatially and temporally, making it
impossible for humans to fully leverage the data in their forecast. Automatic
satellite imagery analysis methods that can find storm-related cloud patterns
as soon as they are detectable are in demand. We propose a machine learning and
pattern recognition based approach to detect "comma-shaped" clouds in satellite
images, which are specific cloud distribution patterns strongly associated with
the cyclone formulation. In order to detect regions with the targeted movement
patterns, our method is trained on manually annotated cloud examples
represented by both shape and motion-sensitive features. Sliding windows in
different scales are used to ensure that dense clouds will be captured, and we
implement effective selection rules to shrink the region of interest among
these sliding windows. Finally, we evaluate the method on a hold-out annotated
comma-shaped cloud dataset and cross-match the results with recorded storm
events in the severe weather database. The validated utility and accuracy of
our method suggest a high potential for assisting meteorologists in weather
forecasting.
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
Authors: Rex Ying, Ruining He, Kai-Feng Chen, Pong Eksombatchai, William L. Hamilton, Jure Leskovec
Date: 2018-06-06 00:00:00
Tasks: Recommendation Systems

Recent advancements in deep neural networks for graph-structured data have
led to state-of-the-art performance on recommender system benchmarks. However,
making these methods practical and scalable to web-scale recommendation tasks
with billions of items and hundreds of millions of users remains a challenge.
Here we describe a large-scale deep recommendation engine that we developed and
deployed at Pinterest. We develop a data-efficient Graph Convolutional Network
(GCN) algorithm PinSage, which combines efficient random walks and graph
convolutions to generate embeddings of nodes (i.e., items) that incorporate
both graph structure as well as node feature information. Compared to prior GCN
approaches, we develop a novel method based on highly efficient random walks to
structure the convolutions and design a novel training strategy that relies on
harder-and-harder training examples to improve robustness and convergence of
the model. We also develop an efficient MapReduce model inference algorithm to
generate embeddings using a trained model. We deploy PinSage at Pinterest and
train it on 7.5 billion examples on a graph with 3 billion nodes representing
pins and boards, and 18 billion edges. According to offline metrics, user
studies and A/B tests, PinSage generates higher-quality recommendations than
comparable deep learning and graph-based alternatives. To our knowledge, this
is the largest application of deep graph embeddings to date and paves the way
for a new generation of web-scale recommender systems based on graph
convolutional architectures.
Non-flat Ground Detection Based on A Local Descriptor
Authors: Kangru Wang, Lei Qu, Lili Chen, Yuzhang Gu, DongChen zhu, Xiaolin Zhang
Date: 2016-09-27 00:00:00

The detection of road and free space remains challenging for non-flat plane,
especially with the varying latitudinal and longitudinal slope or in the case
of multi-ground plane. In this paper, we propose a framework of the ground
plane detection with stereo vision. The main contribution of this paper is a
newly proposed descriptor which is implemented in the disparity image to obtain
a disparity texture image. The ground plane regions can be distinguished from
their surroundings effectively in the disparity texture image. Because the
descriptor is implemented in the local area of the image, it can address well
the problem of non-flat plane. And we also present a complete framework to
detect the ground plane regions base on the disparity texture image with
convolutional neural network architecture.
Reverse iterative volume sampling for linear regression
Authors: Michał Dereziński, Manfred K. Warmuth
Date: 2018-06-06 00:00:00
Tasks: BIG-bench Machine Learning, regression

We study the following basic machine learning task: Given a fixed set of
$d$-dimensional input points for a linear regression problem, we wish to
predict a hidden response value for each of the points. We can only afford to
attain the responses for a small subset of the points that are then used to
construct linear predictions for all points in the dataset. The performance of
the predictions is evaluated by the total square loss on all responses (the
attained as well as the hidden ones). We show that a good approximate solution
to this least squares problem can be obtained from just dimension $d$ many
responses by using a joint sampling technique called volume sampling. Moreover,
the least squares solution obtained for the volume sampled subproblem is an
unbiased estimator of optimal solution based on all n responses. This
unbiasedness is a desirable property that is not shared by other common subset
selection techniques.
  Motivated by these basic properties, we develop a theoretical framework for
studying volume sampling, resulting in a number of new matrix expectation
equalities and statistical guarantees which are of importance not only to least
squares regression but also to numerical linear algebra in general. Our methods
also lead to a regularized variant of volume sampling, and we propose the first
efficient algorithms for volume sampling which make this technique a practical
tool in the machine learning toolbox. Finally, we provide experimental evidence
which confirms our theoretical findings.
A Reinforcement Learning Approach to Interactive-Predictive Neural Machine Translation
Authors: Tsz Kin Lam, Julia Kreutzer, Stefan Riezler
Date: 2018-05-03 00:00:00
Tasks: Machine Translation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Translation

We present an approach to interactive-predictive neural machine translation
that attempts to reduce human effort from three directions: Firstly, instead of
requiring humans to select, correct, or delete segments, we employ the idea of
learning from human reinforcements in form of judgments on the quality of
partial translations. Secondly, human effort is further reduced by using the
entropy of word predictions as uncertainty criterion to trigger feedback
requests. Lastly, online updates of the model parameters after every
interaction allow the model to adapt quickly. We show in simulation experiments
that reward signals on partial translations significantly improve character
F-score and BLEU compared to feedback on full translations only, while human
effort can be reduced to an average number of $5$ feedback requests for every
input.
Efficient Hierarchical Robot Motion Planning Under Uncertainty and Hybrid Dynamics
Authors: Ajinkya Jain, Scott Niekum
Date: 2018-02-12 00:00:00
Tasks: Motion Planning

Noisy observations coupled with nonlinear dynamics pose one of the biggest
challenges in robot motion planning. By decomposing nonlinear dynamics into a
discrete set of local dynamics models, hybrid dynamics provide a natural way to
model nonlinear dynamics, especially in systems with sudden discontinuities in
dynamics due to factors such as contacts. We propose a hierarchical POMDP
planner that develops cost-optimized motion plans for hybrid dynamics models.
The hierarchical planner first develops a high-level motion plan to sequence
the local dynamics models to be visited and then converts it into a detailed
continuous state plan. This hierarchical planning approach results in a
decomposition of the POMDP planning problem into smaller sub-parts that can be
solved with significantly lower computational costs. The ability to sequence
the visitation of local dynamics models also provides a powerful way to
leverage the hybrid dynamics to reduce state uncertainty. We evaluate the
proposed planner on a navigation task in the simulated domain and on an
assembly task with a robotic manipulator, showing that our approach can solve
tasks having high observation noise and nonlinear dynamics effectively with
significantly lower computational costs compared to direct planning approaches.
Spiking Linear Dynamical Systems on Neuromorphic Hardware for Low-Power Brain-Machine Interfaces
Authors: David G. Clark, Jesse A. Livezey, Edward F. Chang, Kristofer E. Bouchard
Date: 2018-05-22 00:00:00

Neuromorphic architectures achieve low-power operation by using many simple
spiking neurons in lieu of traditional hardware. Here, we develop methods for
precise linear computations in spiking neural networks and use these methods to
map the evolution of a linear dynamical system (LDS) onto an existing
neuromorphic chip: IBM's TrueNorth. We analytically characterize, and
numerically validate, the discrepancy between the spiking LDS state sequence
and that of its non-spiking counterpart. These analytical results shed light on
the multiway tradeoff between time, space, energy, and accuracy in neuromorphic
computation. To demonstrate the utility of our work, we implemented a
neuromorphic Kalman filter (KF) and used it for offline decoding of human vocal
pitch from neural data. The neuromorphic KF could be used for low-power
filtering in domains beyond neuroscience, such as navigation or robotics.
Competitive Multi-agent Inverse Reinforcement Learning with Sub-optimal Demonstrations
Authors: Xingyu Wang, Diego Klabjan
Date: 2018-01-07 00:00:00
Proceeding: ICML 2018 7
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper considers the problem of inverse reinforcement learning in
zero-sum stochastic games when expert demonstrations are known to be not
optimal. Compared to previous works that decouple agents in the game by
assuming optimality in expert strategies, we introduce a new objective function
that directly pits experts against Nash Equilibrium strategies, and we design
an algorithm to solve for the reward function in the context of inverse
reinforcement learning with deep neural networks as model approximations. In
our setting the model and algorithm do not decouple by agent. In order to find
Nash Equilibrium in large-scale games, we also propose an adversarial training
algorithm for zero-sum stochastic games, and show the theoretical appeal of
non-existence of local optima in its objective function. In our numerical
experiments, we demonstrate that our Nash Equilibrium and inverse reinforcement
learning algorithms address games that are not amenable to previous approaches
using tabular representations. Moreover, with sub-optimal expert demonstrations
our algorithms recover both reward functions and strategies with good quality.