pwc-archive/papers-with-abstracts huggingface dataset:
Prostate Segmentation using 2D Bridged U-net
Authors: Wanli Chen, Yue Zhang, Junjun He, Yu Qiao, Yi-fan Chen, Hongjian Shi, Xiaoying Tang
Date: 2018-07-12 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

In this paper, we focus on three problems in deep learning based medical
image segmentation. Firstly, U-net, as a popular model for medical image
segmentation, is difficult to train when convolutional layers increase even
though a deeper network usually has a better generalization ability because of
more learnable parameters. Secondly, the exponential ReLU (ELU), as an
alternative of ReLU, is not much different from ReLU when the network of
interest gets deep. Thirdly, the Dice loss, as one of the pervasive loss
functions for medical image segmentation, is not effective when the prediction
is close to ground truth and will cause oscillation during training. To address
the aforementioned three problems, we propose and validate a deeper network
that can fit medical image datasets that are usually small in the sample size.
Meanwhile, we propose a new loss function to accelerate the learning process
and a combination of different activation functions to improve the network
performance. Our experimental results suggest that our network is comparable or
superior to state-of-the-art methods.
Monte Carlo Methods for the Game Kingdomino
Authors: Magnus Gedda, Mikael Z. Lagerkvist, Martin Butler
Date: 2018-07-12 00:00:00

Kingdomino is introduced as an interesting game for studying game playing:
the game is multiplayer (4 independent players per game); it has a limited game
depth (13 moves per player); and it has limited but not insignificant
interaction among players.
  Several strategies based on locally greedy players, Monte Carlo Evaluation
(MCE), and Monte Carlo Tree Search (MCTS) are presented with variants. We
examine a variation of UCT called progressive win bias and a playout policy
(Player-greedy) focused on selecting good moves for the player. A thorough
evaluation is done showing how the strategies perform and how to choose
parameters given specific time constraints. The evaluation shows that
surprisingly MCE is stronger than MCTS for a game like Kingdomino.
  All experiments use a cloud-native design, with a game server in a Docker
container, and agents communicating using a REST-style JSON protocol. This
enables a multi-language approach to separating the game state, the strategy
implementations, and the coordination layer.
Query-Efficient Hard-label Black-box Attack:An Optimization-based Approach
Authors: Minhao Cheng, Thong Le, Pin-Yu Chen, Jin-Feng Yi, huan zhang, Cho-Jui Hsieh
Date: 2018-07-12 00:00:00
Tasks: BIG-bench Machine Learning

We study the problem of attacking a machine learning model in the hard-label
black-box setting, where no model information is revealed except that the
attacker can make queries to probe the corresponding hard-label decisions. This
is a very challenging problem since the direct extension of state-of-the-art
white-box attacks (e.g., CW or PGD) to the hard-label black-box setting will
require minimizing a non-continuous step function, which is combinatorial and
cannot be solved by a gradient-based optimizer. The only current approach is
based on random walk on the boundary, which requires lots of queries and lacks
convergence guarantees. We propose a novel way to formulate the hard-label
black-box attack as a real-valued optimization problem which is usually
continuous and can be solved by any zeroth order optimization algorithm. For
example, using the Randomized Gradient-Free method, we are able to bound the
number of iterations needed for our algorithm to achieve stationary points. We
demonstrate that our proposed method outperforms the previous random walk
approach to attacking convolutional neural networks on MNIST, CIFAR, and
ImageNet datasets. More interestingly, we show that the proposed algorithm can
also be used to attack other discrete and non-continuous machine learning
models, such as Gradient Boosting Decision Trees (GBDT).
Adding Attentiveness to the Neurons in Recurrent Neural Networks
Authors: Pengfei Zhang, Jianru Xue, Cuiling Lan, Wen-Jun Zeng, Zhanning Gao, Nanning Zheng
Date: 2018-07-12 00:00:00
Proceeding: ECCV 2018 9
Tasks: Action Recognition, Skeleton Based Action Recognition, Temporal Action Localization

Recurrent neural networks (RNNs) are capable of modeling the temporal
dynamics of complex sequential information. However, the structures of existing
RNN neurons mainly focus on controlling the contributions of current and
historical information but do not explore the different importance levels of
different elements in an input vector of a time slot. We propose adding a
simple yet effective Element-wiseAttention Gate (EleAttG) to an RNN block
(e.g., all RNN neurons in a network layer) that empowers the RNN neurons to
have the attentiveness capability. For an RNN block, an EleAttG is added to
adaptively modulate the input by assigning different levels of importance,
i.e., attention, to each element/dimension of the input. We refer to an RNN
block equipped with an EleAttG as an EleAtt-RNN block. Specifically, the
modulation of the input is content adaptive and is performed at fine
granularity, being element-wise rather than input-wise. The proposed EleAttG,
as an additional fundamental unit, is general and can be applied to any RNN
structures, e.g., standard RNN, Long Short-Term Memory (LSTM), or Gated
Recurrent Unit (GRU). We demonstrate the effectiveness of the proposed
EleAtt-RNN by applying it to the action recognition tasks on both 3D human
skeleton data and RGB videos. Experiments show that adding attentiveness
through EleAttGs to RNN blocks significantly boosts the power of RNNs.
Tracking the Evolution of Words with Time-reflective Text Representations
Authors: Roberto Camacho Barranco, Raimundo F. Dos Santos, M. Shahriar Hossain
Date: 2018-07-12 00:00:00
Tasks: Articles, Time Series Analysis, Word Embeddings

More than 80% of today's data is unstructured in nature, and these
unstructured datasets evolve over time. A large part of these datasets are text
documents generated by media outlets, scholarly articles in digital libraries,
findings from scientific and professional communities, and social media. Vector
space models were developed to analyze text data using data mining and machine
learning algorithms. While ample vector space models exist for text data, the
evolutionary aspect of ever-changing text corpora is still missing in
vector-based representations. The advent of word embeddings has enabled us to
create a contextual vector space, but the embeddings fail to consider the
temporal aspects of the feature space successfully. This paper presents an
approach to include temporal aspects in feature spaces. The inclusion of the
time aspect in the feature space provides vectors for every natural language
element, such as words or entities, at every timestamp. Such temporal word
vectors allow us to track how the meaning of a word changes over time, by
studying the changes in its neighborhood. Moreover, a time-reflective text
representation will pave the way to a new set of text analytic abilities
involving time series for text collections. In this paper, we present a
time-reflective vector space model for temporal text data that is able to
capture short and long-term changes in the meaning of words. We compare our
approach with the limited literature on dynamic embeddings. We present
qualitative and quantitative evaluations using the tracking of semantic
evolution as the target application.
Will it Blend? Composing Value Functions in Reinforcement Learning
Authors: Benjamin van Niekerk, Steven James, Adam Earle, Benjamin Rosman
Date: 2018-07-12 00:00:00
Tasks: Lifelong learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

An important property for lifelong-learning agents is the ability to combine
existing skills to solve unseen tasks. In general, however, it is unclear how
to compose skills in a principled way. We provide a "recipe" for optimal value
function composition in entropy-regularised reinforcement learning (RL) and
then extend this to the standard RL setting. Composition is demonstrated in a
video game environment, where an agent with an existing library of policies is
able to solve new tasks without the need for further learning.
Statistical Inference with Local Optima
Authors: Yen-Chi Chen
Date: 2018-07-12 00:00:00

We study the statistical properties of an estimator derived by applying a gradient ascent method with multiple initializations to a multi-modal likelihood function. We derive the population quantity that is the target of this estimator and study the properties of confidence intervals (CIs) constructed from asymptotic normality and the bootstrap approach. In particular, we analyze the coverage deficiency due to finite number of random initializations. We also investigate the CIs by inverting the likelihood ratio test, the score test, and the Wald test, and we show that the resulting CIs may be very different. We propose a two-sample test procedure even when the MLE is intractable. In addition, we analyze the performance of the EM algorithm under random initializations and derive the coverage of a CI with a finite number of initializations.
Convergence Rate of Block-Coordinate Maximization Burer-Monteiro Method for Solving Large SDPs
Authors: Murat A. Erdogdu, Asuman Ozdaglar, Pablo A. Parrilo, Nuri Denizcan Vanli
Date: 2018-07-12 00:00:00
Tasks: Community Detection

Semidefinite programming (SDP) with diagonal constraints arise in many optimization problems, such as Max-Cut, community detection and group synchronization. Although SDPs can be solved to arbitrary precision in polynomial time, generic convex solvers do not scale well with the dimension of the problem. In order to address this issue, Burer and Monteiro proposed to reduce the dimension of the problem by appealing to a low-rank factorization and solve the subsequent non-convex problem instead. In this paper, we present coordinate ascent based methods to solve this non-convex problem with provable convergence guarantees. More specifically, we prove that the block-coordinate maximization algorithm applied to the non-convex Burer-Monteiro method globally converges to a first-order stationary point with a sublinear rate without any assumptions on the problem. We further show that this algorithm converges linearly around a local maximum provided that the objective function exhibits quadratic decay. We establish that this condition generically holds when the rank of the factorization is sufficiently large. Furthermore, incorporating Lanczos method to the block-coordinate maximization, we propose an algorithm that is guaranteed to return a solution that provides $1-O(1/r)$ approximation to the original SDP without any assumptions, where $r$ is the rank of the factorization. This approximation ratio is known to be optimal (up to constants) under the unique games conjecture, and we can explicitly quantify the number of iterations to obtain such a solution.
Simultaneous Coherent Structure Coloring facilitates interpretable clustering of scientific data by amplifying dissimilarity
Authors: Brooke E. Husic, Kristy L. Schlueter-Kuck, John O. Dabiri
Date: 2018-07-12 00:00:00
Tasks: Clustering, Protein Folding

The clustering of data into physically meaningful subsets often requires
assumptions regarding the number, size, or shape of the subgroups. Here, we
present a new method, simultaneous coherent structure coloring (sCSC), which
accomplishes the task of unsupervised clustering without a priori guidance
regarding the underlying structure of the data. sCSC performs a sequence of
binary splittings on the dataset such that the most dissimilar data points are
required to be in separate clusters. To achieve this, we obtain a set of
orthogonal coordinates along which dissimilarity in the dataset is maximized
from a generalized eigenvalue problem based on the pairwise dissimilarity
between the data points to be clustered. This sequence of bifurcations produces
a binary tree representation of the system, from which the number of clusters
in the data and their interrelationships naturally emerge. To illustrate the
effectiveness of the method in the absence of a priori assumptions, we apply it
to three exemplary problems in fluid dynamics. Then, we illustrate its capacity
for interpretability using a high-dimensional protein folding simulation
dataset. While we restrict our examples to dynamical physical systems in this
work, we anticipate straightforward translation to other fields where existing
analysis tools require ad hoc assumptions on the data structure, lack the
interpretability of the present method, or in which the underlying processes
are less accessible, such as genomics and neuroscience.
A likelihood-ratio type test for stochastic block models with bounded degrees
Authors: Mingao Yuan, Yang Feng, Zuofeng Shang
Date: 2018-07-12 00:00:00
Tasks: Community Detection, Stochastic Block Model

A fundamental problem in network data analysis is to test Erd\"{o}s-R\'{e}nyi
model $\mathcal{G}\left(n,\frac{a+b}{2n}\right)$ versus a bisection stochastic
block model $\mathcal{G}\left(n,\frac{a}{n},\frac{b}{n}\right)$, where $a,b>0$
are constants that represent the expected degrees of the graphs and $n$ denotes
the number of nodes. This problem serves as the foundation of many other
problems such as testing-based methods for determining the number of
communities (\cite{BS16,L16}) and community detection (\cite{MS16}). Existing
work has been focusing on growing-degree regime $a,b\to\infty$
(\cite{BS16,L16,MS16,BM17,B18,GL17a,GL17b}) while leaving the bounded-degree
regime untreated. In this paper, we propose a likelihood-ratio (LR) type
procedure based on regularization to test stochastic block models with bounded
degrees. We derive the limit distributions as power Poisson laws under both
null and alternative hypotheses, based on which the limit power of the test is
carefully analyzed. We also examine a Monte-Carlo method that partly resolves
the computational cost issue. The proposed procedures are examined by both
simulated and real-world data. The proof depends on a contiguity theory
developed by Janson \cite{J95}.
Subsampled Turbulence Removal Network
Authors: Wai Ho Chak, Chun Pong Lau, Lok Ming Lui
Date: 2018-07-12 00:00:00
Tasks: Data Augmentation

We present a deep-learning approach to restore a sequence of
turbulence-distorted video frames from turbulent deformations and space-time
varying blurs. Instead of requiring a massive training sample size in deep
networks, we purpose a training strategy that is based on a new data
augmentation method to model turbulence from a relatively small dataset. Then
we introduce a subsampled method to enhance the restoration performance of the
presented GAN model. The contributions of the paper is threefold: first, we
introduce a simple but effective data augmentation algorithm to model the
turbulence in real life for training in the deep network; Second, we firstly
purpose the Wasserstein GAN combined with $\ell_1$ cost for successful
restoration of turbulence-corrupted video sequence; Third, we combine the
subsampling algorithm to filter out strongly corrupted frames to generate a
video sequence with better quality.
Process Discovery using Classification Tree Hidden Semi-Markov Model
Authors: Yihuang Kang, Vladimir Zadorozhny
Date: 2018-07-12 00:00:00
Tasks: Classification, General Classification

Various and ubiquitous information systems are being used in monitoring,
exchanging, and collecting information. These systems are generating massive
amount of event sequence logs that may help us understand underlying
phenomenon. By analyzing these logs, we can learn process models that describe
system procedures, predict the development of the system, or check whether the
changes are expected. In this paper, we consider a novel technique that models
these sequences of events in temporal-probabilistic manners. Specifically, we
propose a probabilistic process model that combines hidden semi-Markov model
and classification trees learning. Our experimental result shows that the
proposed approach can answer a kind of question-"what are the most frequent
sequence of system dynamics relevant to a given sequence of observable
events?". For example, "Given a series of medical treatments, what are the most
relevant patients' health condition pattern changes at different times?"
Sem-GAN: Semantically-Consistent Image-to-Image Translation
Authors: Anoop Cherian, Alan Sullivan
Date: 2018-07-12 00:00:00
Tasks: Image Segmentation, Image-to-Image Translation, Segmentation, Semantic Segmentation, Translation

Unpaired image-to-image translation is the problem of mapping an image in the
source domain to one in the target domain, without requiring corresponding
image pairs. To ensure the translated images are realistically plausible,
recent works, such as Cycle-GAN, demands this mapping to be invertible. While,
this requirement demonstrates promising results when the domains are unimodal,
its performance is unpredictable in a multi-modal scenario such as in an image
segmentation task. This is because, invertibility does not necessarily enforce
semantic correctness. To this end, we present a semantically-consistent GAN
framework, dubbed Sem-GAN, in which the semantics are defined by the class
identities of image segments in the source domain as produced by a semantic
segmentation algorithm. Our proposed framework includes consistency constraints
on the translation task that, together with the GAN loss and the
cycle-constraints, enforces that the images when translated will inherit the
appearances of the target domain, while (approximately) maintaining their
identities from the source domain. We present experiments on several
image-to-image translation tasks and demonstrate that Sem-GAN improves the
quality of the translated images significantly, sometimes by more than 20% on
the FCN score. Further, we show that semantic segmentation models, trained with
synthetic images translated via Sem-GAN, leads to significantly better
segmentation results than other variants.
Topic Diffusion Discovery based on Sparseness-constrained Non-negative Matrix Factorization
Authors: Yihuang Kang, Keng-Pei Lin, I-Ling Cheng
Date: 2018-07-12 00:00:00
Tasks: Articles, Citation Recommendation

Due to recent explosion of text data, researchers have been overwhelmed by
ever-increasing volume of articles produced by different research communities.
Various scholarly search websites, citation recommendation engines, and
research databases have been created to simplify the text search tasks.
However, it is still difficult for researchers to be able to identify potential
research topics without doing intensive reviews on a tremendous number of
articles published by journals, conferences, meetings, and workshops. In this
paper, we consider a novel topic diffusion discovery technique that
incorporates sparseness-constrained Non-negative Matrix Factorization with
generalized Jensen-Shannon divergence to help understand term-topic evolutions
and identify topic diffusions. Our experimental result shows that this approach
can extract more prominent topics from large article databases, visualize
relationships between terms of interest and abstract topics, and further help
researchers understand whether given terms/topics have been widely explored or
whether new topics are emerging from literature.
AtDelfi: Automatically Designing Legible, Full Instructions For Games
Authors: Michael Cerny Green, Ahmed Khalifa, Gabriella A. B. Barros, Tiago Machado, Andy Nealen, Julian Togelius
Date: 2018-07-11 00:00:00

This paper introduces a fully automatic method for generating video game
tutorials. The AtDELFI system (AuTomatically DEsigning Legible, Full
Instructions for games) was created to investigate procedural generation of
instructions that teach players how to play video games. We present a
representation of game rules and mechanics using a graph system as well as a
tutorial generation method that uses said graph representation. We demonstrate
the concept by testing it on games within the General Video Game Artificial
Intelligence (GVG-AI) framework; the paper discusses tutorials generated for
eight different games. Our findings suggest that a graph representation scheme
works well for simple arcade style games such as Space Invaders and Pacman, but
it appears that tutorials for more complex games might require higher-level
understanding of the game than just single mechanics.
Differentially-Private "Draw and Discard" Machine Learning
Authors: Vasyl Pihur, Aleksandra Korolova, Frederick Liu, Subhash Sankuratripati, Moti Yung, Dachuan Huang, Ruogu Zeng
Date: 2018-07-11 00:00:00
Tasks: BIG-bench Machine Learning, Privacy Preserving

In this work, we propose a novel framework for privacy-preserving
client-distributed machine learning. It is motivated by the desire to achieve
differential privacy guarantees in the local model of privacy in a way that
satisfies all systems constraints using asynchronous client-server
communication and provides attractive model learning properties. We call it
"Draw and Discard" because it relies on random sampling of models for load
distribution (scalability), which also provides additional server-side privacy
protections and improved model quality through averaging. We present the
mechanics of client and server components of "Draw and Discard" and demonstrate
how the framework can be applied to learning Generalized Linear models. We then
analyze the privacy guarantees provided by our approach against several types
of adversaries and showcase experimental results that provide evidence for the
framework's viability in practical deployments.
A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoising
Authors: Jun Xu, Lei Zhang, David Zhang
Date: 2018-07-11 00:00:00
Proceeding: ECCV 2018 9
Tasks: Denoising, Image Denoising

Most of existing image denoising methods assume the corrupted noise to be
additive white Gaussian noise (AWGN). However, the realistic noise in
real-world noisy images is much more complex than AWGN, and is hard to be
modelled by simple analytical distributions. As a result, many state-of-the-art
denoising methods in literature become much less effective when applied to
real-world noisy images captured by CCD or CMOS cameras. In this paper, we
develop a trilateral weighted sparse coding (TWSC) scheme for robust real-world
image denoising. Specifically, we introduce three weight matrices into the data
and regularisation terms of the sparse coding framework to characterise the
statistics of realistic noise and image priors. TWSC can be reformulated as a
linear equality-constrained problem and can be solved by the alternating
direction method of multipliers. The existence and uniqueness of the solution
and convergence of the proposed algorithm are analysed. Extensive experiments
demonstrate that the proposed TWSC scheme outperforms state-of-the-art
denoising methods on removing realistic noise.