pwc-archive/papers-with-abstracts huggingface dataset:
Acceleration of RED via Vector Extrapolation
Authors: Tao Hong, Yaniv Romano, Michael Elad
Date: 2018-05-06 00:00:00
Tasks: Denoising

Models play an important role in inverse problems, serving as the prior for
representing the original signal to be recovered. REgularization by Denoising
(RED) is a recently introduced general framework for constructing such priors
using state-of-the-art denoising algorithms. Using RED, solving inverse
problems is shown to amount to an iterated denoising process. However, as the
complexity of denoising algorithms is generally high, this might lead to an
overall slow algorithm. In this paper, we suggest an accelerated technique
based on vector extrapolation (VE) to speed-up existing RED solvers. Numerical
experiments validate the obtained gain by VE, leading to a substantial savings
in computations compared with the original fixed-point method.
Attentive Generative Adversarial Network for Raindrop Removal from a Single Image
Authors: Rui Qian, Robby T. Tan, Wenhan Yang, Jiajun Su, Jiaying Liu
Date: 2017-11-28 00:00:00
Proceeding: CVPR 2018 6
Tasks: Generative Adversarial Network, Raindrop Removal, Rain Removal

Raindrops adhered to a glass window or camera lens can severely hamper the
visibility of a background scene and degrade an image considerably. In this
paper, we address the problem by visually removing raindrops, and thus
transforming a raindrop degraded image into a clean one. The problem is
intractable, since first the regions occluded by raindrops are not given.
Second, the information about the background scene of the occluded regions is
completely lost for most part. To resolve the problem, we apply an attentive
generative network using adversarial training. Our main idea is to inject
visual attention into both the generative and discriminative networks. During
the training, our visual attention learns about raindrop regions and their
surroundings. Hence, by injecting this information, the generative network will
pay more attention to the raindrop regions and the surrounding structures, and
the discriminative network will be able to assess the local consistency of the
restored regions. This injection of visual attention to both generative and
discriminative networks is the main contribution of this paper. Our experiments
show the effectiveness of our approach, which outperforms the state of the art
methods quantitatively and qualitatively.
Tracking in Aerial Hyperspectral Videos using Deep Kernelized Correlation Filters
Authors: Burak Uzkent, Aneesh Rangnekar, Matthew J. Hoffman
Date: 2017-11-20 00:00:00
Tasks: General Classification, Image Generation, Object Tracking, vehicle detection

Hyperspectral imaging holds enormous potential to improve the
state-of-the-art in aerial vehicle tracking with low spatial and temporal
resolutions. Recently, adaptive multi-modal hyperspectral sensors have
attracted growing interest due to their ability to record extended data quickly
from aerial platforms. In this study, we apply popular concepts from
traditional object tracking, namely (1) Kernelized Correlation Filters (KCF)
and (2) Deep Convolutional Neural Network (CNN) features to aerial tracking in
hyperspectral domain. We propose the Deep Hyperspectral Kernelized Correlation
Filter based tracker (DeepHKCF) to efficiently track aerial vehicles using an
adaptive multi-modal hyperspectral sensor. We address low temporal resolution
by designing a single KCF-in-multiple Regions-of-Interest (ROIs) approach to
cover a reasonably large area. To increase the speed of deep convolutional
features extraction from multiple ROIs, we design an effective ROI mapping
strategy. The proposed tracker also provides flexibility to couple with the
more advanced correlation filter trackers. The DeepHKCF tracker performs
exceptionally well with deep features set up in a synthetic hyperspectral video
generated by the Digital Imaging and Remote Sensing Image Generation (DIRSIG)
software. Additionally, we generate a large, synthetic, single-channel dataset
using DIRSIG to perform vehicle classification in the Wide Area Motion Imagery
(WAMI) platform. This way, the high-fidelity of the DIRSIG software is proved
and a large scale aerial vehicle classification dataset is released to support
studies on vehicle detection and tracking in the WAMI platform.
Tensorized Self-Attention: Efficiently Modeling Pairwise and Global Dependencies Together
Authors: Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang
Date: 2018-05-02 00:00:00
Proceeding: NAACL 2019 6

Neural networks equipped with self-attention have parallelizable computation,
light-weight structure, and the ability to capture both long-range and local
dependencies. Further, their expressive power and performance can be boosted by
using a vector to measure pairwise dependency, but this requires to expand the
alignment matrix to a tensor, which results in memory and computation
bottlenecks. In this paper, we propose a novel attention mechanism called
"Multi-mask Tensorized Self-Attention" (MTSA), which is as fast and as
memory-efficient as a CNN, but significantly outperforms previous
CNN-/RNN-/attention-based models. MTSA 1) captures both pairwise (token2token)
and global (source2token) dependencies by a novel compatibility function
composed of dot-product and additive attentions, 2) uses a tensor to represent
the feature-wise alignment scores for better expressive power but only requires
parallelizable matrix multiplications, and 3) combines multi-head with
multi-dimensional attentions, and applies a distinct positional mask to each
head (subspace), so the memory and computation can be distributed to multiple
heads, each with sequential information encoded independently. The experiments
show that a CNN/RNN-free model based on MTSA achieves state-of-the-art or
competitive performance on nine NLP benchmarks with compelling memory- and
time-efficiency.
Automatic Classification of Object Code Using Machine Learning
Authors: John Clemens
Date: 2018-05-06 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification, Object

Recent research has repeatedly shown that machine learning techniques can be
applied to either whole files or file fragments to classify them for analysis.
We build upon these techniques to show that for samples of un-labeled compiled
computer object code, one can apply the same type of analysis to classify
important aspects of the code, such as its target architecture and endianess.
We show that using simple byte-value histograms we retain enough information
about the opcodes within a sample to classify the target architecture with high
accuracy, and then discuss heuristic-based features that exploit information
within the operands to determine endianess. We introduce a dataset with over
16000 code samples from 20 architectures and experimentally show that by using
our features, classifiers can achieve very high accuracy with relatively small
sample sizes.
An Image dehazing approach based on the airlight field estimation
Authors: Lijun Zhang, Yongbin Gao, Yu-Jin Zhang
Date: 2018-05-06 00:00:00
Tasks: Image Dehazing, Single Image Dehazing, Single Image Haze Removal, valid

This paper proposes a scheme for single image haze removal based on the
airlight field (ALF) estimation. Conventional image dehazing methods which are
based on a physical model generally take the global atmospheric light as a
constant. However, the constant-airlight assumption may be unsuitable for
images with large sky regions, which causes unacceptable brightness imbalance
and color distortion in recovery images. This paper models the atmospheric
light as a field function, and presents a maximum a-priori (MAP) method for
jointly estimating the airlight field, the transmission rate and the haze free
image. We also introduce a valid haze-level prior for effective estimate of
transmission. Evaluation on real world images shows that the proposed approach
outperforms existing methods in single image dehazing, especially when the
large sky region is included.
A Counter-Forensic Method for CNN-Based Camera Model Identification
Authors: David Güera, Yu Wang, Luca Bondi, Paolo Bestagini, Stefano Tubaro, Edward J. Delp
Date: 2018-05-06 00:00:00

An increasing number of digital images are being shared and accessed through
websites, media, and social applications. Many of these images have been
modified and are not authentic. Recent advances in the use of deep
convolutional neural networks (CNNs) have facilitated the task of analyzing the
veracity and authenticity of largely distributed image datasets. We examine in
this paper the problem of identifying the camera model or type that was used to
take an image and that can be spoofed. Due to the linear nature of CNNs and the
high-dimensionality of images, neural networks are vulnerable to attacks with
adversarial examples. These examples are imperceptibly different from correctly
classified images but are misclassified with high confidence by CNNs. In this
paper, we describe a counter-forensic method capable of subtly altering images
to change their estimated camera model when they are analyzed by any CNN-based
camera model detector. Our method can use both the Fast Gradient Sign Method
(FGSM) or the Jacobian-based Saliency Map Attack (JSMA) to craft these
adversarial images and does not require direct access to the CNN. Our results
show that even advanced deep learning architectures trained to analyze images
and obtain camera model information are still vulnerable to our proposed
method.
Memcomputing: Leveraging memory and physics to compute efficiently
Authors: Massimiliano Di Ventra, Fabio L. Traversa
Date: 2018-02-20 00:00:00
Tasks: Combinatorial Optimization

It is well known that physical phenomena may be of great help in computing
some difficult problems efficiently. A typical example is prime factorization
that may be solved in polynomial time by exploiting quantum entanglement on a
quantum computer. There are, however, other types of (non-quantum) physical
properties that one may leverage to compute efficiently a wide range of hard
problems. In this perspective we discuss how to employ one such property,
memory (time non-locality), in a novel physics-based approach to computation:
Memcomputing. In particular, we focus on digital memcomputing machines (DMMs)
that are scalable. DMMs can be realized with non-linear dynamical systems with
memory. The latter property allows the realization of a new type of Boolean
logic, one that is self-organizing. Self-organizing logic gates are
"terminal-agnostic", namely they do not distinguish between input and output
terminals. When appropriately assembled to represent a given
combinatorial/optimization problem, the corresponding self-organizing circuit
converges to the equilibrium points that express the solutions of the problem
at hand. In doing so, DMMs take advantage of the long-range order that develops
during the transient dynamics. This collective dynamical behavior, reminiscent
of a phase transition, or even the "edge of chaos", is mediated by families of
classical trajectories (instantons) that connect critical points of increasing
stability in the system's phase space. The topological character of the
solution search renders DMMs robust against noise and structural disorder.
Since DMMs are non-quantum systems described by ordinary differential
equations, not only can they be built in hardware with available technology,
they can also be simulated efficiently on modern classical computers. As an
example, we will show the polynomial-time solution of the subset-sum problem
for the worst...
Population Anomaly Detection through Deep Gaussianization
Authors: David Tolpin
Date: 2018-05-05 00:00:00
Tasks: Anomaly Detection

We introduce an algorithmic method for population anomaly detection based on
gaussianization through an adversarial autoencoder. This method is applicable
to detection of `soft' anomalies in arbitrarily distributed highly-dimensional
data.
  A soft, or population, anomaly is characterized by a shift in the
distribution of the data set, where certain elements appear with higher
probability than anticipated. Such anomalies must be detected by considering a
sufficiently large sample set rather than a single sample.
  Applications include, but not limited to, payment fraud trends, data
exfiltration, disease clusters and epidemics, and social unrests. We evaluate
the method on several domains and obtain both quantitative results and
qualitative insights.
Talking Face Generation by Conditional Recurrent Adversarial Network
Authors: Yang Song, Jingwen Zhu, Dawei Li, Xiaolong Wang, Hairong Qi
Date: 2018-04-13 00:00:00
Tasks: Constrained Lip-synchronization, Face Generation, Talking Face Generation, Video Generation

Given an arbitrary face image and an arbitrary speech clip, the proposed work attempts to generating the talking face video with accurate lip synchronization while maintaining smooth transition of both lip and facial movement over the entire video clip. Existing works either do not consider temporal dependency on face images across different video frames thus easily yielding noticeable/abrupt facial and lip movement or are only limited to the generation of talking face video for a specific person thus lacking generalization capacity. We propose a novel conditional video generation network where the audio input is treated as a condition for the recurrent adversarial network such that temporal dependency is incorporated to realize smooth transition for the lip and facial movement. In addition, we deploy a multi-task adversarial training scheme in the context of video generation to improve both photo-realism and the accuracy for lip synchronization. Finally, based on the phoneme distribution information extracted from the audio clip, we develop a sample selection method that effectively reduces the size of the training dataset without sacrificing the quality of the generated video. Extensive experiments on both controlled and uncontrolled datasets demonstrate the superiority of the proposed approach in terms of visual quality, lip sync accuracy, and smooth transition of lip and facial movement, as compared to the state-of-the-art.
Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging
Authors: Shusen Wang, Alex Gittens, Michael W. Mahoney
Date: 2017-02-16 00:00:00
Proceeding: ICML 2017 8
Tasks: regression

We address the statistical and optimization impacts of the classical sketch
and Hessian sketch used to approximately solve the Matrix Ridge Regression
(MRR) problem. Prior research has quantified the effects of classical sketch on
the strictly simpler least squares regression (LSR) problem. We establish that
classical sketch has a similar effect upon the optimization properties of MRR
as it does on those of LSR: namely, it recovers nearly optimal solutions. By
contrast, Hessian sketch does not have this guarantee, instead, the
approximation error is governed by a subtle interplay between the "mass" in the
responses and the optimal objective value.
  For both types of approximation, the regularization in the sketched MRR
problem results in significantly different statistical properties from those of
the sketched LSR problem. In particular, there is a bias-variance trade-off in
sketched MRR that is not present in sketched LSR. We provide upper and lower
bounds on the bias and variance of sketched MRR, these bounds show that
classical sketch significantly increases the variance, while Hessian sketch
significantly increases the bias. Empirically, sketched MRR solutions can have
risks that are higher by an order-of-magnitude than those of the optimal MRR
solutions.
  We establish theoretically and empirically that model averaging greatly
decreases the gap between the risks of the true and sketched solutions to the
MRR problem. Thus, in parallel or distributed settings, sketching combined with
model averaging is a powerful technique that quickly obtains near-optimal
solutions to the MRR problem while greatly mitigating the increased statistical
risk incurred by sketching.
An Accelerated Approach to Safely and Efficiently Test Pre-Production Autonomous Vehicles on Public Streets
Authors: Mansur Arief, Peter Glynn, Ding Zhao
Date: 2018-05-05 00:00:00
Tasks: Autonomous Vehicles

Various automobile and mobility companies, for instance Ford, Uber and Waymo,
are currently testing their pre-produced autonomous vehicle (AV) fleets on the
public roads. However, due to rareness of the safety-critical cases and,
effectively, unlimited number of possible traffic scenarios, these on-road
testing efforts have been acknowledged as tedious, costly, and risky. In this
study, we propose Accelerated De- ployment framework to safely and efficiently
estimate the AVs performance on public streets. We showed that by appropriately
addressing the gradual accuracy improvement and adaptively selecting meaningful
and safe environment under which the AV is deployed, the proposed framework
yield to highly accurate estimation with much faster evaluation time, and more
importantly, lower deployment risk. Our findings provide an answer to the
currently heated and active discussions on how to properly test AV performance
on public roads so as to achieve safe, efficient, and statistically-reliable
testing framework for AV technologies.
Geometric adaptive Monte Carlo in random environment
Authors: Theodore Papamarkou, Alexey Lindo, Eric B. Ford
Date: 2016-08-29 00:00:00

Manifold Markov chain Monte Carlo algorithms have been introduced to sample more effectively from challenging target densities exhibiting multiple modes or strong correlations. Such algorithms exploit the local geometry of the parameter space, thus enabling chains to achieve a faster convergence rate when measured in number of steps. However, acquiring local geometric information can often increase computational complexity per step to the extent that sampling from high-dimensional targets becomes inefficient in terms of total computational time. This paper analyzes the computational complexity of manifold Langevin Monte Carlo and proposes a geometric adaptive Monte Carlo sampler aimed at balancing the benefits of exploiting local geometry with computational cost to achieve a high effective sample size for a given computational cost. The suggested sampler is a discrete-time stochastic process in random environment. The random environment allows to switch between local geometric and adaptive proposal kernels with the help of a schedule. An exponential schedule is put forward that enables more frequent use of geometric information in early transient phases of the chain, while saving computational time in late stationary phases. The average complexity can be manually set depending on the need for geometric exploitation posed by the underlying model.
Developing parsimonious ensembles using ensemble diversity within a reinforcement learning framework
Authors: Ana Stanescu, Gaurav Pandey
Date: 2018-05-05 00:00:00
Tasks: Diversity, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Heterogeneous ensembles built from the predictions of a wide variety and large number of diverse base predictors represent a potent approach to building predictive models for problems where the ideal base/individual predictor may not be obvious. Ensemble selection is an especially promising approach here, not only for improving prediction performance, but also because of its ability to select a collectively predictive subset, often a relatively small one, of the base predictors. In this paper, we present a set of algorithms that explicitly incorporate ensemble diversity, a known factor influencing predictive performance of ensembles, into a reinforcement learning framework for ensemble selection. We rigorously tested these approaches on several challenging problems and associated data sets, yielding that several of them produced more accurate ensembles than those that don't explicitly consider diversity. More importantly, these diversity-incorporating ensembles were much smaller in size, i.e., more parsimonious, than the latter types of ensembles. This can eventually aid the interpretation or reverse engineering of predictive models assimilated into the resultant ensemble(s).
The Impact of Local Geometry and Batch Size on Stochastic Gradient Descent for Nonconvex Problems
Authors: Vivak Patel
Date: 2017-09-14 00:00:00

In several experimental reports on nonconvex optimization problems in machine
learning, stochastic gradient descent (SGD) was observed to prefer minimizers
with flat basins in comparison to more deterministic methods, yet there is very
little rigorous understanding of this phenomenon. In fact, the lack of such
work has led to an unverified, but widely-accepted stochastic mechanism
describing why SGD prefers flatter minimizers to sharper minimizers. However,
as we demonstrate, the stochastic mechanism fails to explain this phenomenon.
Here, we propose an alternative deterministic mechanism that can accurately
explain why SGD prefers flatter minimizers to sharper minimizers. We derive
this mechanism based on a detailed analysis of a generic stochastic quadratic
problem, which generalizes known results for classical gradient descent.
Finally, we verify the predictions of our deterministic mechanism on two
nonconvex problems.
Cluster-based trajectory segmentation with local noise
Authors: Maria Luisa Damiani, Fatima Hachem, Issa Hamza, Nathan Ranc, Paul Moorcroft, Francesca Cagnacci
Date: 2018-05-05 00:00:00

We present a framework for the partitioning of a spatial trajectory in a
sequence of segments based on spatial density and temporal criteria. The result
is a set of temporally separated clusters interleaved by sub-sequences of
unclustered points. A major novelty is the proposal of an outlier or noise
model based on the distinction between intra-cluster (local noise) and
inter-cluster noise (transition): the local noise models the temporary absence
from a residence while the transition the definitive departure towards a next
residence. We analyze in detail the properties of the model and present a
comprehensive solution for the extraction of temporally ordered clusters. The
effectiveness of the solution is evaluated first qualitatively and next
quantitatively by contrasting the segmentation with ground truth. The ground
truth consists of a set of trajectories of labeled points simulating animal
movement. Moreover, we show that the approach can streamline the discovery of
additional derived patterns, by presenting a novel technique for the analysis
of periodic movement. From a methodological perspective, a valuable aspect of
this research is that it combines the theoretical investigation with the
application and external validation of the segmentation framework. This paves
the way to an effective deployment of the solution in broad and challenging
fields such as e-science.
Learning Patient Representations from Text
Authors: Dmitriy Dligach, Timothy Miller
Date: 2018-05-05 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: BIG-bench Machine Learning

Mining electronic health records for patients who satisfy a set of predefined
criteria is known in medical informatics as phenotyping. Phenotyping has
numerous applications such as outcome prediction, clinical trial recruitment,
and retrospective studies. Supervised machine learning for phenotyping
typically relies on sparse patient representations such as bag-of-words. We
consider an alternative that involves learning patient representations. We
develop a neural network model for learning patient representations and show
that the learned representations are general enough to obtain state-of-the-art
performance on a standard comorbidity detection task.