pwc-archive/papers-with-abstracts huggingface dataset:
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Authors: Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi
Date: 2016-09-15 00:00:00
Proceeding: CVPR 2017 7
Tasks: Generative Adversarial Network, Image Super-Resolution, Super-Resolution

Despite the breakthroughs in accuracy and speed of single image
super-resolution using faster and deeper convolutional neural networks, one
central problem remains largely unsolved: how do we recover the finer texture
details when we super-resolve at large upscaling factors? The behavior of
optimization-based super-resolution methods is principally driven by the choice
of the objective function. Recent work has largely focused on minimizing the
mean squared reconstruction error. The resulting estimates have high peak
signal-to-noise ratios, but they are often lacking high-frequency details and
are perceptually unsatisfying in the sense that they fail to match the fidelity
expected at the higher resolution. In this paper, we present SRGAN, a
generative adversarial network (GAN) for image super-resolution (SR). To our
knowledge, it is the first framework capable of inferring photo-realistic
natural images for 4x upscaling factors. To achieve this, we propose a
perceptual loss function which consists of an adversarial loss and a content
loss. The adversarial loss pushes our solution to the natural image manifold
using a discriminator network that is trained to differentiate between the
super-resolved images and original photo-realistic images. In addition, we use
a content loss motivated by perceptual similarity instead of similarity in
pixel space. Our deep residual network is able to recover photo-realistic
textures from heavily downsampled images on public benchmarks. An extensive
mean-opinion-score (MOS) test shows hugely significant gains in perceptual
quality using SRGAN. The MOS scores obtained with SRGAN are closer to those of
the original high-resolution images than to those obtained with any
state-of-the-art method.
SLAM based Quasi Dense Reconstruction For Minimally Invasive Surgery Scenes
Authors: Nader Mahmoud, Alexandre Hostettler, Toby Collins, Luc Soler, Christophe Doignon, J. M. M. Montiel
Date: 2017-05-25 00:00:00
Tasks: Patch Matching

Recovering surgical scene structure in laparoscope surgery is crucial step
for surgical guidance and augmented reality applications. In this paper, a
quasi dense reconstruction algorithm of surgical scene is proposed. This is
based on a state-of-the-art SLAM system, and is exploiting the initial
exploration phase that is typically performed by the surgeon at the beginning
of the surgery. We show how to convert the sparse SLAM map to a quasi dense
scene reconstruction, using pairs of keyframe images and correlation-based
featureless patch matching. We have validated the approach with a live porcine
experiment using Computed Tomography as ground truth, yielding a Root Mean
Squared Error of 4.9mm.
Deep Face Deblurring
Authors: Grigorios G. Chrysos, Stefanos Zafeiriou
Date: 2017-04-27 00:00:00
Tasks: Deblurring, Face Alignment

Blind deblurring consists a long studied task, however the outcomes of
generic methods are not effective in real world blurred images. Domain-specific
methods for deblurring targeted object categories, e.g. text or faces,
frequently outperform their generic counterparts, hence they are attracting an
increasing amount of attention. In this work, we develop such a domain-specific
method to tackle deblurring of human faces, henceforth referred to as face
deblurring. Studying faces is of tremendous significance in computer vision,
however face deblurring has yet to demonstrate some convincing results. This
can be partly attributed to the combination of i) poor texture and ii) highly
structure shape that yield the contour/gradient priors (that are typically
used) sub-optimal. In our work instead of making assumptions over the prior, we
adopt a learning approach by inserting weak supervision that exploits the
well-documented structure of the face. Namely, we utilise a deep network to
perform the deblurring and employ a face alignment technique to pre-process
each face. We additionally surpass the requirement of the deep network for
thousands training samples, by introducing an efficient framework that allows
the generation of a large dataset. We utilised this framework to create 2MF2, a
dataset of over two million frames. We conducted experiments with real world
blurred facial images and report that our method returns a result close to the
sharp natural latent image.
An Empirical Analysis of Approximation Algorithms for the Euclidean Traveling Salesman Problem
Authors: Yihui He, Ming Xiang
Date: 2017-05-25 00:00:00
Tasks: Traveling Salesman Problem

With applications to many disciplines, the traveling salesman problem (TSP)
is a classical computer science optimization problem with applications to
industrial engineering, theoretical computer science, bioinformatics, and
several other disciplines. In recent years, there have been a plethora of novel
approaches for approximate solutions ranging from simplistic greedy to
cooperative distributed algorithms derived from artificial intelligence. In
this paper, we perform an evaluation and analysis of cornerstone algorithms for
the Euclidean TSP. We evaluate greedy, 2-opt, and genetic algorithms. We use
several datasets as input for the algorithms including a small dataset, a
mediumsized dataset representing cities in the United States, and a synthetic
dataset consisting of 200 cities to test algorithm scalability. We discover
that the greedy and 2-opt algorithms efficiently calculate solutions for
smaller datasets. Genetic algorithm has the best performance for optimality for
medium to large datasets, but generally have longer runtime. Our
implementations is public available.
The cost of fairness in classification
Authors: Aditya Krishna Menon, Robert C. Williamson
Date: 2017-05-25 00:00:00
Tasks: Classification, Fairness, General Classification

We study the problem of learning classifiers with a fairness constraint, with
three main contributions towards the goal of quantifying the problem's inherent
tradeoffs. First, we relate two existing fairness measures to cost-sensitive
risks. Second, we show that for cost-sensitive classification and fairness
measures, the optimal classifier is an instance-dependent thresholding of the
class-probability function. Third, we show how the tradeoff between accuracy
and fairness is determined by the alignment between the class-probabilities for
the target and sensitive features. Underpinning our analysis is a general
framework that casts the problem of learning with a fairness requirement as one
of minimising the difference of two statistical risks.
Max-Cosine Matching Based Neural Models for Recognizing Textual Entailment
Authors: Zhipeng Xie, Junfeng Hu
Date: 2017-05-25 00:00:00
Tasks: Natural Language Inference, RTE

Recognizing textual entailment is a fundamental task in a variety of text
mining or natural language processing applications. This paper proposes a
simple neural model for RTE problem. It first matches each word in the
hypothesis with its most-similar word in the premise, producing an augmented
representation of the hypothesis conditioned on the premise as a sequence of
word pairs. The LSTM model is then used to model this augmented sequence, and
the final output from the LSTM is fed into a softmax layer to make the
prediction. Besides the base model, in order to enhance its performance, we
also proposed three techniques: the integration of multiple word-embedding
library, bi-way integration, and ensemble based on model averaging.
Experimental results on the SNLI dataset have shown that the three techniques
are effective in boosting the predicative accuracy and that our method
outperforms several state-of-the-state ones.
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations
Authors: Andrew Slavin Ross, Michael C. Hughes, Finale Doshi-Velez
Date: 2017-03-10 00:00:00

Neural networks are among the most accurate supervised learning methods in
use today, but their opacity makes them difficult to trust in critical
applications, especially when conditions in training differ from those in test.
Recent work on explanations for black-box models has produced tools (e.g. LIME)
to show the implicit rules behind predictions, which can help us identify when
models are right for the wrong reasons. However, these methods do not scale to
explaining entire datasets and cannot correct the problems they reveal. We
introduce a method for efficiently explaining and regularizing differentiable
models by examining and selectively penalizing their input gradients, which
provide a normal to the decision boundary. We apply these penalties both based
on expert annotation and in an unsupervised fashion that encourages diverse
models with qualitatively different decision boundaries for the same
classification problem. On multiple datasets, we show our approach generates
faithful explanations and models that generalize much better when conditions
differ between training and test.
A Clustering-based Consistency Adaptation Strategy for Distributed SDN Controllers
Authors: Mohamed Aslan, Ashraf Matrawy
Date: 2017-05-25 00:00:00
Tasks: Clustering

Distributed controllers are oftentimes used in large-scale SDN deployments
where they run a myriad of network applications simultaneously. Such
applications could have different consistency and availability preferences.
These controllers need to communicate via east/west interfaces in order to
synchronize their state information. The consistency and the availability of
the distributed state information are governed by an underlying consistency
model. Earlier, we suggested the use of adaptively-consistent controllers that
can autonomously tune their consistency parameters in order to meet the
performance requirements of a certain application. In this paper, we examine
the feasibility of employing adaptive controllers that are built on-top of
tunable consistency models similar to that of Apache Cassandra. We present an
adaptation strategy that uses clustering techniques (sequential k-means and
incremental k-means) in order to map a given application performance indicator
into a feasible consistency level that can be used with the underlying tunable
consistency model. In the cases that we modeled and tested, our results show
that in the case of sequential k-means, with a reasonable number of clusters
(>= 50), a plausible mapping (low RMSE) could be estimated between the
application performance indicators and the consistency level indicator. In the
case of incremental k-means, the results also showed that a plausible mapping
(low RMSE) could be estimated using a similar number of clusters (>= 50) by
using a small threshold (~$ 0.01).
Adequacy of the Gradient-Descent Method for Classifier Evasion Attacks
Authors: Yi Han, Benjamin I. P. Rubinstein
Date: 2017-04-06 00:00:00
Tasks: Computer Security

Despite the wide use of machine learning in adversarial settings including
computer security, recent studies have demonstrated vulnerabilities to evasion
attacks---carefully crafted adversarial samples that closely resemble
legitimate instances, but cause misclassification. In this paper, we examine
the adequacy of the leading approach to generating adversarial samples---the
gradient descent approach. In particular (1) we perform extensive experiments
on three datasets, MNIST, USPS and Spambase, in order to analyse the
effectiveness of the gradient-descent method against non-linear support vector
machines, and conclude that carefully reduced kernel smoothness can
significantly increase robustness to the attack; (2) we demonstrate that
separated inter-class support vectors lead to more secure models, and propose a
quantity similar to margin that can efficiently predict potential
susceptibility to gradient-descent attacks, before the attack is launched; and
(3) we design a new adversarial sample construction algorithm based on
optimising the multiplicative ratio of class decision functions.
Lat-Net: Compressing Lattice Boltzmann Flow Simulations using Deep Neural Networks
Authors: Oliver Hennigh
Date: 2017-05-25 00:00:00
Tasks: Efficient Neural Network

Computational Fluid Dynamics (CFD) is a hugely important subject with
applications in almost every engineering field, however, fluid simulations are
extremely computationally and memory demanding. Towards this end, we present
Lat-Net, a method for compressing both the computation time and memory usage of
Lattice Boltzmann flow simulations using deep neural networks. Lat-Net employs
convolutional autoencoders and residual connections in a fully differentiable
scheme to compress the state size of a simulation and learn the dynamics on
this compressed form. The result is a computationally and memory efficient
neural network that can be iterated and queried to reproduce a fluid
simulation. We show that once Lat-Net is trained, it can generalize to large
grid sizes and complex geometries while maintaining accuracy. We also show that
Lat-Net is a general method for compressing other Lattice Boltzmann based
simulations such as Electromagnetism.
Fast Causal Inference with Non-Random Missingness by Test-Wise Deletion
Authors: Eric V. Strobl, Shyam Visweswaran, Peter L. Spirtes
Date: 2017-05-25 00:00:00
Tasks: Causal Discovery, Causal Inference, Imputation, Missing Values

Many real datasets contain values missing not at random (MNAR). In this
scenario, investigators often perform list-wise deletion, or delete samples
with any missing values, before applying causal discovery algorithms. List-wise
deletion is a sound and general strategy when paired with algorithms such as
FCI and RFCI, but the deletion procedure also eliminates otherwise good samples
that contain only a few missing values. In this report, we show that we can
more efficiently utilize the observed values with test-wise deletion while
still maintaining algorithmic soundness. Here, test-wise deletion refers to the
process of list-wise deleting samples only among the variables required for
each conditional independence (CI) test used in constraint-based searches.
Test-wise deletion therefore often saves more samples than list-wise deletion
for each CI test, especially when we have a sparse underlying graph. Our
theoretical results show that test-wise deletion is sound under the justifiable
assumption that none of the missingness mechanisms causally affect each other
in the underlying causal graph. We also find that FCI and RFCI with test-wise
deletion outperform their list-wise deletion and imputation counterparts on
average when MNAR holds in both synthetic and real data.
Learning to Pour
Authors: Yongqiang Huang, Yu Sun
Date: 2017-05-25 00:00:00

Pouring is a simple task people perform daily. It is the second most
frequently executed motion in cooking scenarios, after pick-and-place. We
present a pouring trajectory generation approach, which uses force feedback
from the cup to determine the future velocity of pouring. The approach uses
recurrent neural networks as its building blocks. We collected the pouring
demonstrations which we used for training. To test our approach in simulation,
we also created and trained a force estimation system. The simulated
experiments show that the system is able to generalize to single unseen element
of the pouring characteristics.
Extraction and Classification of Diving Clips from Continuous Video Footage
Authors: Aiden Nibali, Zhen He, Stuart Morgan, Daniel Greenwood
Date: 2017-05-25 00:00:00
Tasks: Classification, General Classification

Due to recent advances in technology, the recording and analysis of video
data has become an increasingly common component of athlete training
programmes. Today it is incredibly easy and affordable to set up a fixed camera
and record athletes in a wide range of sports, such as diving, gymnastics,
golf, tennis, etc. However, the manual analysis of the obtained footage is a
time-consuming task which involves isolating actions of interest and
categorizing them using domain-specific knowledge. In order to automate this
kind of task, three challenging sub-problems are often encountered: 1)
temporally cropping events/actions of interest from continuous video; 2)
tracking the object of interest; and 3) classifying the events/actions of
interest.
  Most previous work has focused on solving just one of the above sub-problems
in isolation. In contrast, this paper provides a complete solution to the
overall action monitoring task in the context of a challenging real-world
exemplar. Specifically, we address the problem of diving classification. This
is a challenging problem since the person (diver) of interest typically
occupies fewer than 1% of the pixels in each frame. The model is required to
learn the temporal boundaries of a dive, even though other divers and
bystanders may be in view. Finally, the model must be sensitive to subtle
changes in body pose over a large number of frames to determine the
classification code. We provide effective solutions to each of the sub-problems
which combine to provide a highly functional solution to the task as a whole.
The techniques proposed can be easily generalized to video footage recorded
from other sports.
State Space Decomposition and Subgoal Creation for Transfer in Deep Reinforcement Learning
Authors: Himanshu Sahni, Saurabh Kumar, Farhan Tejani, Yannick Schroecker, Charles Isbell
Date: 2017-05-24 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Typical reinforcement learning (RL) agents learn to complete tasks specified
by reward functions tailored to their domain. As such, the policies they learn
do not generalize even to similar domains. To address this issue, we develop a
framework through which a deep RL agent learns to generalize policies from
smaller, simpler domains to more complex ones using a recurrent attention
mechanism. The task is presented to the agent as an image and an instruction
specifying the goal. This meta-controller guides the agent towards its goal by
designing a sequence of smaller subtasks on the part of the state space within
the attention, effectively decomposing it. As a baseline, we consider a setup
without attention as well. Our experiments show that the meta-controller learns
to create subgoals within the attention.
Approximation and Convergence Properties of Generative Adversarial Learning
Authors: Shuang Liu, Olivier Bousquet, Kamalika Chaudhuri
Date: 2017-05-24 00:00:00
Proceeding: NeurIPS 2017 12

Generative adversarial networks (GAN) approximate a target data distribution
by jointly optimizing an objective function through a "two-player game" between
a generator and a discriminator. Despite their empirical success, however, two
very basic questions on how well they can approximate the target distribution
remain unanswered. First, it is not known how restricting the discriminator
family affects the approximation quality. Second, while a number of different
objective functions have been proposed, we do not understand when convergence
to the global minima of the objective function leads to convergence to the
target distribution under various notions of distributional convergence.
  In this paper, we address these questions in a broad and unified setting by
defining a notion of adversarial divergences that includes a number of recently
proposed objective functions. We show that if the objective function is an
adversarial divergence with some additional conditions, then using a restricted
discriminator family has a moment-matching effect. Additionally, we show that
for objective functions that are strict adversarial divergences, convergence in
the objective function implies weak convergence, thus generalizing previous
results.
Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks
Authors: Shuai Xiao, Junchi Yan, Stephen M. Chu, Xiaokang Yang, Hongyuan Zha
Date: 2017-05-24 00:00:00
Tasks: Point Processes, Time Series, Time Series Analysis

Event sequence, asynchronously generated with random timestamp, is ubiquitous
among applications. The precise and arbitrary timestamp can carry important
clues about the underlying dynamics, and has lent the event data fundamentally
different from the time-series whereby series is indexed with fixed and equal
time interval. One expressive mathematical tool for modeling event is point
process. The intensity functions of many point processes involve two
components: the background and the effect by the history. Due to its inherent
spontaneousness, the background can be treated as a time series while the other
need to handle the history events. In this paper, we model the background by a
Recurrent Neural Network (RNN) with its units aligned with time series indexes
while the history effect is modeled by another RNN whose units are aligned with
asynchronous events to capture the long-range dynamics. The whole model with
event type and timestamp prediction output layers can be trained end-to-end.
Our approach takes an RNN perspective to point process, and models its
background and history effect. For utility, our method allows a black-box
treatment for modeling the intensity which is often a pre-defined parametric
form in point processes. Meanwhile end-to-end training opens the venue for
reusing existing rich techniques in deep network for point process modeling. We
apply our model to the predictive maintenance problem using a log dataset by
more than 1000 ATMs from a global bank headquartered in North America.
Learning Word-Like Units from Joint Audio-Visual Analysis
Authors: David Harwath, James R. Glass
Date: 2017-01-25 00:00:00
Proceeding: ACL 2017 7
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Form, Language Acquisition, speech-recognition, Speech Recognition

Given a collection of images and spoken audio captions, we present a method
for discovering word-like acoustic units in the continuous speech signal and
grounding them to semantically relevant image regions. For example, our model
is able to detect spoken instances of the word 'lighthouse' within an utterance
and associate them with image regions containing lighthouses. We do not use any
form of conventional automatic speech recognition, nor do we use any text
transcriptions or conventional linguistic annotations. Our model effectively
implements a form of spoken language acquisition, in which the computer learns
not only to recognize word categories by sound, but also to enrich the words it
learns with semantics by grounding them in images.