pwc-archive/papers-with-abstracts huggingface dataset:
Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs)
Authors: Lei Bi, Jinman Kim, Ashnil Kumar, Dagan Feng, Michael Fulham
Date: 2017-07-31 00:00:00
Tasks: Computed Tomography (CT), Image Generation

Positron emission tomography (PET) image synthesis plays an important role,
which can be used to boost the training data for computer aided diagnosis
systems. However, existing image synthesis methods have problems in
synthesizing the low resolution PET images. To address these limitations, we
propose multi-channel generative adversarial networks (M-GAN) based PET image
synthesis method. Different to the existing methods which rely on using
low-level features, the proposed M-GAN is capable to represent the features in
a high-level of semantic based on the adversarial learning concept. In
addition, M-GAN enables to take the input from the annotation (label) to
synthesize the high uptake regions e.g., tumors and from the computed
tomography (CT) images to constrain the appearance consistency and output the
synthetic PET images directly. Our results on 50 lung cancer PET-CT studies
indicate that our method was much closer to the real PET images when compared
with the existing methods.
Empirical Bayes Estimators for High-Dimensional Sparse Vectors
Authors: Pavan Srinath, Ramji Venkataramanan
Date: 2017-07-28 00:00:00
Tasks: compressed sensing, Vocal Bursts Intensity Prediction

The problem of estimating a high-dimensional sparse vector
$\boldsymbol{\theta} \in \mathbb{R}^n$ from an observation in i.i.d. Gaussian
noise is considered. The performance is measured using squared-error loss. An
empirical Bayes shrinkage estimator, derived using a Bernoulli-Gaussian prior,
is analyzed and compared with the well-known soft-thresholding estimator. We
obtain concentration inequalities for the Stein's unbiased risk estimate and
the loss function of both estimators. The results show that for large $n$, both
the risk estimate and the loss function concentrate on deterministic values
close to the true risk.
  Depending on the underlying $\boldsymbol{\theta}$, either the proposed
empirical Bayes (eBayes) estimator or soft-thresholding may have smaller loss.
We consider a hybrid estimator that attempts to pick the better of the
soft-thresholding estimator and the eBayes estimator by comparing their risk
estimates. It is shown that: i) the loss of the hybrid estimator concentrates
on the minimum of the losses of the two competing estimators, and ii) the risk
of the hybrid estimator is within order $\frac{1}{\sqrt{n}}$ of the minimum of
the two risks. Simulation results are provided to support the theoretical
results. Finally, we use the eBayes and hybrid estimators as denoisers in the
approximate message passing (AMP) algorithm for compressed sensing, and show
that their performance is superior to the soft-thresholding denoiser in a wide
range of settings.
Taming Non-stationary Bandits: A Bayesian Approach
Authors: Vishnu Raj, Sheetal Kalyani
Date: 2017-07-31 00:00:00
Tasks: Thompson Sampling

We consider the multi armed bandit problem in non-stationary environments.
Based on the Bayesian method, we propose a variant of Thompson Sampling which
can be used in both rested and restless bandit scenarios. Applying discounting
to the parameters of prior distribution, we describe a way to systematically
reduce the effect of past observations. Further, we derive the exact expression
for the probability of picking sub-optimal arms. By increasing the exploitative
value of Bayes' samples, we also provide an optimistic version of the
algorithm. Extensive empirical analysis is conducted under various scenarios to
validate the utility of proposed algorithms. A comparison study with various
state-of-the-arm algorithms is also included.
Analysis and Optimization of Convolutional Neural Network Architectures
Authors: Martin Thoma
Date: 2017-07-31 00:00:00
Tasks: Data Augmentation, Object Recognition

Convolutional Neural Networks (CNNs) dominate various computer vision tasks
since Alex Krizhevsky showed that they can be trained effectively and reduced
the top-5 error from 26.2 % to 15.3 % on the ImageNet large scale visual
recognition challenge. Many aspects of CNNs are examined in various
publications, but literature about the analysis and construction of neural
network architectures is rare. This work is one step to close this gap. A
comprehensive overview over existing techniques for CNN analysis and topology
construction is provided. A novel way to visualize classification errors with
confusion matrices was developed. Based on this method, hierarchical
classifiers are described and evaluated. Additionally, some results are
confirmed and quantified for CIFAR-100. For example, the positive impact of
smaller batch sizes, averaging ensembles, data augmentation and test-time
transformations on the accuracy. Other results, such as the positive impact of
learned color transformation on the test accuracy could not be confirmed. A
model which has only one million learned parameters for an input size of
32x32x3 and 100 classes and which beats the state of the art on the benchmark
dataset Asirra, GTSRB, HASYv2 and STL-10 was developed.
Transfer Learning with Label Noise
Authors: Xiyu Yu, Tongliang Liu, Mingming Gong, Kun Zhang, Kayhan Batmanghelich, DaCheng Tao
Date: 2017-07-31 00:00:00
Tasks: Denoising, Transfer Learning

Transfer learning aims to improve learning in target domain by borrowing
knowledge from a related but different source domain. To reduce the
distribution shift between source and target domains, recent methods have
focused on exploring invariant representations that have similar distributions
across domains. However, when learning this invariant knowledge, existing
methods assume that the labels in source domain are uncontaminated, while in
reality, we often have access to source data with noisy labels. In this paper,
we first show how label noise adversely affect the learning of invariant
representations and the correcting of label shift in various transfer learning
scenarios. To reduce the adverse effects, we propose a novel Denoising
Conditional Invariant Component (DCIC) framework, which provably ensures (1)
extracting invariant representations given examples with noisy labels in source
domain and unlabeled examples in target domain; (2) estimating the label
distribution in target domain with no bias. Experimental results on both
synthetic and real-world data verify the effectiveness of the proposed method.
Automatic Crack Detection in Built Infrastructure Using Unmanned Aerial Vehicles
Authors: Manh Duong Phung, Van Truong Hoang, Tran Hiep Dinh, Quang Ha
Date: 2017-07-31 00:00:00

This paper addresses the problem of crack detection which is essential for
health monitoring of built infrastructure. Our approach includes two stages,
data collection using unmanned aerial vehicles (UAVs) and crack detection using
histogram analysis. For the data collection, a 3D model of the structure is
first created by using laser scanners. Based on the model, geometric properties
are extracted to generate way points necessary for navigating the UAV to take
images of the structure. Then, our next step is to stick together those
obtained images from the overlapped field of view. The resulting image is then
clustered by histogram analysis and peak detection. Potential cracks are
finally identified by using locally adaptive thresholds. The whole process is
automatically carried out so that the inspection time is significantly improved
while safety hazards can be minimised. A prototypical system has been developed
for evaluation and experimental results are included.
Developing Knowledge-enhanced Chronic Disease Risk Prediction Models from Regional EHR Repositories
Authors: Jing Mei, Eryu Xia, Xiang Li, Guotong Xie
Date: 2017-07-31 00:00:00
Tasks: Prediction

Precision medicine requires the precision disease risk prediction models. In
literature, there have been a lot well-established (inter-)national risk
models, but when applying them into the local population, the prediction
performance becomes unsatisfactory. To address the localization issue, this
paper exploits the way to develop knowledge-enhanced localized risk models. On
the one hand, we tune models by learning from regional Electronic Health Record
(EHR) repositories, and on the other hand, we propose knowledge injection into
the EHR data learning process. For experiments, we leverage the Pooled Cohort
Equations (PCE, as recommended in ACC/AHA guidelines to estimate the risk of
ASCVD) to develop a localized ASCVD risk prediction model in diabetes. The
experimental results show that, if directly using the PCE algorithm on our
cohort, the AUC is only 0.653, while our knowledge-enhanced localized risk
model can achieve higher prediction performance with AUC of 0.723 (improved by
10.7%).
Cost and Actual Causation
Authors: Liang Zhou
Date: 2017-07-31 00:00:00

I propose the purpose our concept of actual causation serves is minimizing
various cost in intervention practice. Actual causation has three features:
nonredundant sufficiency, continuity and abnormality; these features correspond
to the minimization of exploitative cost, exploratory cost and risk cost in
intervention practice. Incorporating these three features, a definition of
actual causation is given. I test the definition in 66 causal cases from actual
causation literature and show that this definition's application fit intuition
better than some other causal modelling based definitions.
Detection Algorithms for Communication Systems Using Deep Learning
Authors: Nariman Farsad, Andrea Goldsmith
Date: 2017-05-22 00:00:00
Tasks: Deep Learning

The design and analysis of communication systems typically rely on the
development of mathematical models that describe the underlying communication
channel, which dictates the relationship between the transmitted and the
received signals. However, in some systems, such as molecular communication
systems where chemical signals are used for transfer of information, it is not
possible to accurately model this relationship. In these scenarios, because of
the lack of mathematical channel models, a completely new approach to design
and analysis is required. In this work, we focus on one important aspect of
communication systems, the detection algorithms, and demonstrate that by
borrowing tools from deep learning, it is possible to train detectors that
perform well, without any knowledge of the underlying channel models. We
evaluate these algorithms using experimental data that is collected by a
chemical communication platform, where the channel model is unknown and
difficult to model analytically. We show that deep learning algorithms perform
significantly better than a simple detector that was used in previous works,
which also did not assume any knowledge of the channel.
Recurrent 3D Pose Sequence Machines
Authors: Mude Lin, Liang Lin, Xiaodan Liang, Keze Wang, Hui Cheng
Date: 2017-07-31 00:00:00
Proceeding: CVPR 2017 7
Tasks: 3D Human Pose Estimation, 3D Pose Estimation, Pose Estimation

3D human articulated pose recovery from monocular image sequences is very
challenging due to the diverse appearances, viewpoints, occlusions, and also
the human 3D pose is inherently ambiguous from the monocular imagery. It is
thus critical to exploit rich spatial and temporal long-range dependencies
among body joints for accurate 3D pose sequence prediction. Existing approaches
usually manually design some elaborate prior terms and human body kinematic
constraints for capturing structures, which are often insufficient to exploit
all intrinsic structures and not scalable for all scenarios. In contrast, this
paper presents a Recurrent 3D Pose Sequence Machine(RPSM) to automatically
learn the image-dependent structural constraint and sequence-dependent temporal
context by using a multi-stage sequential refinement. At each stage, our RPSM
is composed of three modules to predict the 3D pose sequences based on the
previously learned 2D pose representations and 3D poses: (i) a 2D pose module
extracting the image-dependent pose representations, (ii) a 3D pose recurrent
module regressing 3D poses and (iii) a feature adaption module serving as a
bridge between module (i) and (ii) to enable the representation transformation
from 2D to 3D domain. These three modules are then assembled into a sequential
prediction framework to refine the predicted poses with multiple recurrent
stages. Extensive evaluations on the Human3.6M dataset and HumanEva-I dataset
show that our RPSM outperforms all state-of-the-art approaches for 3D pose
estimation.
EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis
Authors: Mehdi S. M. Sajjadi, Bernhard Schölkopf, Michael Hirsch
Date: 2016-12-23 00:00:00
Proceeding: ICCV 2017 10
Tasks: Image Super-Resolution, Super-Resolution, Texture Synthesis

Single image super-resolution is the task of inferring a high-resolution
image from a single low-resolution input. Traditionally, the performance of
algorithms for this task is measured using pixel-wise reconstruction measures
such as peak signal-to-noise ratio (PSNR) which have been shown to correlate
poorly with the human perception of image quality. As a result, algorithms
minimizing these metrics tend to produce over-smoothed images that lack
high-frequency textures and do not look natural despite yielding high PSNR
values.
  We propose a novel application of automated texture synthesis in combination
with a perceptual loss focusing on creating realistic textures rather than
optimizing for a pixel-accurate reproduction of ground truth images during
training. By using feed-forward fully convolutional neural networks in an
adversarial training setting, we achieve a significant boost in image quality
at high magnification ratios. Extensive experiments on a number of datasets
show the effectiveness of our approach, yielding state-of-the-art results in
both quantitative and qualitative benchmarks.
Learning to Match
Authors: Philip Ekman, Sebastian Bellevik, Christos Dimitrakakis, Aristide Tossou
Date: 2017-07-30 00:00:00

Outsourcing tasks to previously unknown parties is becoming more common. One
specific such problem involves matching a set of workers to a set of tasks.
Even if the latter have precise requirements, the quality of individual workers
is usually unknown. The problem is thus a version of matching under
uncertainty. We believe that this type of problem is going to be increasingly
important.
  When the problem involves only a single skill or type of job, it is
essentially a type of bandit problem, and can be solved with standard
algorithms. However, we develop an algorithm that can perform matching for
workers with multiple skills hired for multiple jobs with multiple
requirements. We perform an experimental evaluation in both single-task and
multi-task problems, comparing with the bounded $\epsilon$-first algorithm, as
well as an oracle that knows the true skills of workers. One of the algorithms
we developed gives results approaching 85\% of oracle's performance. We invite
the community to take a closer look at this problem and develop real-world
benchmarks.
A Vision For Continuous Automated Game Design
Authors: Michael Cook
Date: 2017-07-30 00:00:00
Tasks: Game Design

ANGELINA is an automated game design system which has previously been built
as a single software block which designs games from start to finish. In this
paper we outline a roadmap for the development of a new version of ANGELINA,
designed to iterate on games in different ways to produce a continuous creative
process that will improve the quality of its work, but more importantly improve
the perception of the software as being an independently creative piece of
software. We provide an initial report of the system's structure here as well
as results from the first working module of the system.
Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling
Authors: Diego Marcheggiani, Ivan Titov
Date: 2017-03-14 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Semantic Role Labeling, Sentence

Semantic role labeling (SRL) is the task of identifying the
predicate-argument structure of a sentence. It is typically regarded as an
important step in the standard NLP pipeline. As the semantic representations
are closely related to syntactic ones, we exploit syntactic information in our
model. We propose a version of graph convolutional networks (GCNs), a recent
class of neural networks operating on graphs, suited to model syntactic
dependency graphs. GCNs over syntactic dependency trees are used as sentence
encoders, producing latent feature representations of words in a sentence. We
observe that GCN layers are complementary to LSTM ones: when we stack both GCN
and LSTM layers, we obtain a substantial improvement over an already
state-of-the-art LSTM SRL model, resulting in the best reported scores on the
standard benchmark (CoNLL-2009) both for Chinese and English.
A Novel Approach for Image Segmentation based on Histograms computed from Hue-data
Authors: Viraj Mavani, Ayesha Gurnani, Jhanvi Shah
Date: 2017-07-30 00:00:00
Tasks: General Classification, Image Segmentation, Segmentation, Semantic Segmentation

Computer Vision is growing day by day in terms of user specific applications.
The first step of any such application is segmenting an image. In this paper,
we propose a novel and grass-root level image segmentation algorithm for cases
in which the background has uniform color distribution. This algorithm can be
used for images of flowers, birds, insects and many more where such background
conditions occur. By image segmentation, the visualization of a computer
increases manifolds and it can even attain near-human accuracy during
classification.
Learning Word Relatedness over Time
Authors: Guy D. Rosin, Eytan Adar, Kira Radinsky
Date: 2017-07-25 00:00:00
Proceeding: EMNLP 2017 9

Search systems are often focused on providing relevant results for the "now",
assuming both corpora and user needs that focus on the present. However, many
corpora today reflect significant longitudinal collections ranging from 20
years of the Web to hundreds of years of digitized newspapers and books.
Understanding the temporal intent of the user and retrieving the most relevant
historical content has become a significant challenge. Common search features,
such as query expansion, leverage the relationship between terms but cannot
function well across all times when relationships vary temporally. In this
work, we introduce a temporal relationship model that is extracted from
longitudinal data collections. The model supports the task of identifying,
given two words, when they relate to each other. We present an algorithmic
framework for this task and show its application for the task of query
expansion, achieving high gain.
Laplacian-Steered Neural Style Transfer
Authors: Shaohua Li, Xinxing Xu, Liqiang Nie, Tat-Seng Chua
Date: 2017-07-05 00:00:00
Tasks: Image Generation, Style Transfer

Neural Style Transfer based on Convolutional Neural Networks (CNN) aims to
synthesize a new image that retains the high-level structure of a content
image, rendered in the low-level texture of a style image. This is achieved by
constraining the new image to have high-level CNN features similar to the
content image, and lower-level CNN features similar to the style image. However
in the traditional optimization objective, low-level features of the content
image are absent, and the low-level features of the style image dominate the
low-level detail structures of the new image. Hence in the synthesized image,
many details of the content image are lost, and a lot of inconsistent and
unpleasing artifacts appear. As a remedy, we propose to steer image synthesis
with a novel loss function: the Laplacian loss. The Laplacian matrix
("Laplacian" in short), produced by a Laplacian operator, is widely used in
computer vision to detect edges and contours. The Laplacian loss measures the
difference of the Laplacians, and correspondingly the difference of the detail
structures, between the content image and a new image. It is flexible and
compatible with the traditional style transfer constraints. By incorporating
the Laplacian loss, we obtain a new optimization objective for neural style
transfer named Lapstyle. Minimizing this objective will produce a stylized
image that better preserves the detail structures of the content image and
eliminates the artifacts. Experiments show that Lapstyle produces more
appealing stylized images with less artifacts, without compromising their
"stylishness".
Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach
Authors: Xingyi Zhou, Qi-Xing Huang, Xiao Sun, xiangyang xue, Yichen Wei
Date: 2017-04-08 00:00:00
Proceeding: ICCV 2017 10
Tasks: 2D Pose Estimation, 3D Human Pose Estimation, 3D Multi-Person Pose Estimation (absolute), 3D Multi-Person Pose Estimation (root-relative), Monocular 3D Human Pose Estimation, Pose Estimation, Pose Prediction, Transfer Learning

In this paper, we study the task of 3D human pose estimation in the wild.
This task is challenging due to lack of training data, as existing datasets are
either in the wild images with 2D pose or in the lab images with 3D pose.
  We propose a weakly-supervised transfer learning method that uses mixed 2D
and 3D labels in a unified deep neutral network that presents two-stage
cascaded structure. Our network augments a state-of-the-art 2D pose estimation
sub-network with a 3D depth regression sub-network. Unlike previous two stage
approaches that train the two sub-networks sequentially and separately, our
training is end-to-end and fully exploits the correlation between the 2D pose
and depth estimation sub-tasks. The deep features are better learnt through
shared representations. In doing so, the 3D pose labels in controlled lab
environments are transferred to in the wild images. In addition, we introduce a
3D geometric constraint to regularize the 3D pose prediction, which is
effective in the absence of ground truth depth labels. Our method achieves
competitive results on both 2D and 3D benchmarks.
A Nearly Instance Optimal Algorithm for Top-k Ranking under the Multinomial Logit Model
Authors: Xi Chen, Yuanzhi Li, Jieming Mao
Date: 2017-07-25 00:00:00
Tasks: Active Learning

We study the active learning problem of top-$k$ ranking from multi-wise
comparisons under the popular multinomial logit model. Our goal is to identify
the top-$k$ items with high probability by adaptively querying sets for
comparisons and observing the noisy output of the most preferred item from each
comparison. To achieve this goal, we design a new active ranking algorithm
without using any information about the underlying items' preference scores. We
also establish a matching lower bound on the sample complexity even when the
set of preference scores is given to the algorithm. These two results together
show that the proposed algorithm is nearly instance optimal (similar to
instance optimal [FLN03], but up to polylog factors). Our work extends the
existing literature on rank aggregation in three directions. First, instead of
studying a static problem with fixed data, we investigate the top-$k$ ranking
problem in an active learning setting. Second, we show our algorithm is nearly
instance optimal, which is a much stronger theoretical guarantee. Finally, we
extend the pairwise comparison to the multi-wise comparison, which has not been
fully explored in ranking literature.