pwc-archive/papers-with-abstracts huggingface dataset:
An Iterative Boundary Random Walks Algorithm for Interactive Image Segmentation
Authors: Xiao-Feng Xie, Zhuliang Yu, Zhenghui Gu, Yuanqing Li
Date: 2018-08-09 00:00:00
Tasks: Image Segmentation, Segmentation, Self-Learning, Semantic Segmentation

The interactive image segmentation algorithm can provide an intelligent ways
to understand the intention of user input. Many interactive methods have the
problem of that ask for large number of user input. To efficient produce
intuitive segmentation under limited user input is important for industrial
application. In this paper, we reveal a positive feedback system on image
segmentation to show the pixels of self-learning. Two approaches, iterative
random walks and boundary random walks, are proposed for segmentation
potential, which is the key step in feedback system. Experiment results on
image segmentation indicates that proposed algorithms can obtain more efficient
input to random walks. And higher segmentation performance can be obtained by
applying the iterative boundary random walks algorithm.
Compressed Sensing Using Binary Matrices of Nearly Optimal Dimensions
Authors: Mahsa Lotfi, Mathukumalli Vidyasagar
Date: 2018-08-09 00:00:00
Tasks: compressed sensing, CPU

In this paper, we study the problem of compressed sensing using binary measurement matrices and $\ell_1$-norm minimization (basis pursuit) as the recovery algorithm. We derive new upper and lower bounds on the number of measurements to achieve robust sparse recovery with binary matrices. We establish sufficient conditions for a column-regular binary matrix to satisfy the robust null space property (RNSP) and show that the associated sufficient conditions % sparsity bounds for robust sparse recovery obtained using the RNSP are better by a factor of $(3 \sqrt{3})/2 \approx 2.6$ compared to the sufficient conditions obtained using the restricted isometry property (RIP). Next we derive universal \textit{lower} bounds on the number of measurements that any binary matrix needs to have in order to satisfy the weaker sufficient condition based on the RNSP and show that bipartite graphs of girth six are optimal. Then we display two classes of binary matrices, namely parity check matrices of array codes and Euler squares, which have girth six and are nearly optimal in the sense of almost satisfying the lower bound. In principle, randomly generated Gaussian measurement matrices are "order-optimal". So we compare the phase transition behavior of the basis pursuit formulation using binary array codes and Gaussian matrices and show that (i) there is essentially no difference between the phase transition boundaries in the two cases and (ii) the CPU time of basis pursuit with binary matrices is hundreds of times faster than with Gaussian matrices and the storage requirements are less. Therefore it is suggested that binary matrices are a viable alternative to Gaussian matrices for compressed sensing using basis pursuit. \end{abstract}
Sample size estimation for power and accuracy in the experimental comparison of algorithms
Authors: Felipe Campelo, Fernanda Takahashi
Date: 2018-08-09 00:00:00

Experimental comparisons of performance represent an important aspect of
research on optimization algorithms. In this work we present a methodology for
defining the required sample sizes for designing experiments with desired
statistical properties for the comparison of two methods on a given problem
class. The proposed approach allows the experimenter to define desired levels
of accuracy for estimates of mean performance differences on individual problem
instances, as well as the desired statistical power for comparing mean
performances over a problem class of interest. The method calculates the
required number of problem instances, and runs the algorithms on each test
instance so that the accuracy of the estimated differences in performance is
controlled at the predefined level. Two examples illustrate the application of
the proposed method, and its ability to achieve the desired statistical
properties with a methodologically sound definition of the relevant sample
sizes.
Object Detection in Satellite Imagery using 2-Step Convolutional Neural Networks
Authors: Hiroki Miyamoto, Kazuki Uehara, Masahiro Murakawa, Hidenori Sakanashi, Hirokazu Nosato, Toru Kouyama, Ryosuke Nakamura
Date: 2018-08-09 00:00:00
Tasks: BIG-bench Machine Learning, Object, object-detection, Object Detection

This paper presents an efficient object detection method from satellite
imagery. Among a number of machine learning algorithms, we proposed a
combination of two convolutional neural networks (CNN) aimed at high precision
and high recall, respectively. We validated our models using golf courses as
target objects. The proposed deep learning method demonstrated higher accuracy
than previous object identification methods.
Controllable Image-to-Video Translation: A Case Study on Facial Expression Generation
Authors: Lijie Fan, Wenbing Huang, Chuang Gan, Junzhou Huang, Boqing Gong
Date: 2018-08-09 00:00:00
Tasks: Facial expression generation, Image-to-Image Translation, Translation, Video Generation

The recent advances in deep learning have made it possible to generate
photo-realistic images by using neural networks and even to extrapolate video
frames from an input video clip. In this paper, for the sake of both furthering
this exploration and our own interest in a realistic application, we study
image-to-video translation and particularly focus on the videos of facial
expressions. This problem challenges the deep neural networks by another
temporal dimension comparing to the image-to-image translation. Moreover, its
single input image fails most existing video generation methods that rely on
recurrent models. We propose a user-controllable approach so as to generate
video clips of various lengths from a single face image. The lengths and types
of the expressions are controlled by users. To this end, we design a novel
neural network architecture that can incorporate the user input into its skip
connections and propose several improvements to the adversarial training method
for the neural network. Experiments and user studies verify the effectiveness
of our approach. Especially, we would like to highlight that even for the face
images in the wild (downloaded from the Web and the authors' own photos), our
model can generate high-quality facial expression videos of which about 50\%
are labeled as real by Amazon Mechanical Turk workers.
Exploiting Effective Representations for Chinese Sentiment Analysis Using a Multi-Channel Convolutional Neural Network
Authors: Pengfei Liu, Ji Zhang, Cane Wing-Ki Leung, Chao He, Thomas L. Griffiths
Date: 2018-08-08 00:00:00
Tasks: Chinese Sentiment Analysis, Sentiment Analysis

Effective representation of a text is critical for various natural language
processing tasks. For the particular task of Chinese sentiment analysis, it is
important to understand and choose an effective representation of a text from
different forms of Chinese representations such as word, character and pinyin.
This paper presents a systematic study of the effect of these representations
for Chinese sentiment analysis by proposing a multi-channel convolutional
neural network (MCCNN), where each channel corresponds to a representation.
Experimental results show that: (1) Word wins on the dataset of low OOV rate
while character wins otherwise; (2) Using these representations in combination
generally improves the performance; (3) The representations based on MCCNN
outperform conventional ngram features using SVM; (4) The proposed MCCNN model
achieves the competitive performance against the state-of-the-art model
fastText for Chinese sentiment analysis.
Feature Dimensionality Reduction for Video Affect Classification: A Comparative Study
Authors: Chenfeng Guo, Dongrui Wu
Date: 2018-08-08 00:00:00
Tasks: Classification, Dimensionality Reduction, General Classification

Affective computing has become a very important research area in
human-machine interaction. However, affects are subjective, subtle, and
uncertain. So, it is very difficult to obtain a large number of labeled
training samples, compared with the number of possible features we could
extract. Thus, dimensionality reduction is critical in affective computing.
This paper presents our preliminary study on dimensionality reduction for
affect classification. Five popular dimensionality reduction approaches are
introduced and compared. Experiments on the DEAP dataset showed that no
approach can universally outperform others, and performing classification using
the raw features directly may not always be a bad choice.
On the Convergence of A Class of Adam-Type Algorithms for Non-Convex Optimization
Authors: Xiangyi Chen, Sijia Liu, Ruoyu Sun, Mingyi Hong
Date: 2018-08-08 00:00:00
Proceeding: ICLR 2019 5
Tasks: Open-Ended Question Answering, Stochastic Optimization

This paper studies a class of adaptive gradient based momentum algorithms
that update the search directions and learning rates simultaneously using past
gradients. This class, which we refer to as the "Adam-type", includes the
popular algorithms such as the Adam, AMSGrad and AdaGrad. Despite their
popularity in training deep neural networks, the convergence of these
algorithms for solving nonconvex problems remains an open question. This paper
provides a set of mild sufficient conditions that guarantee the convergence for
the Adam-type methods. We prove that under our derived conditions, these
methods can achieve the convergence rate of order $O(\log{T}/\sqrt{T})$ for
nonconvex stochastic optimization. We show the conditions are essential in the
sense that violating them may make the algorithm diverge. Moreover, we propose
and analyze a class of (deterministic) incremental adaptive gradient
algorithms, which has the same $O(\log{T}/\sqrt{T})$ convergence rate. Our
study could also be extended to a broader class of adaptive gradient methods in
machine learning and optimization.
Towards Learning Fine-Grained Disentangled Representations from Speech
Authors: Yuan Gong, Christian Poellabauer
Date: 2018-08-08 00:00:00
Tasks: Representation Learning, Speech Representation Learning

Learning disentangled representations of high-dimensional data is currently
an active research area. However, compared to the field of computer vision,
less work has been done for speech processing. In this paper, we provide a
review of two representative efforts on this topic and propose the novel
concept of fine-grained disentangled speech representation learning.
Sequential Monte Carlo Bandits
Authors: Iñigo Urteaga, Chris H. Wiggins
Date: 2018-08-08 00:00:00
Tasks: Decision Making, Sequential Decision Making, Thompson Sampling

We extend Bayesian multi-armed bandit (MAB) algorithms beyond their original setting by making use of sequential Monte Carlo (SMC) methods. A MAB is a sequential decision making problem where the goal is to learn a policy that maximizes long term payoff, where only the reward of the executed action is observed. In the stochastic MAB, the reward for each action is generated from an unknown distribution, often assumed to be stationary. To decide which action to take next, a MAB agent must learn the characteristics of the unknown reward distribution, e.g., compute its sufficient statistics. However, closed-form expressions for these statistics are analytically intractable except for simple, stationary cases. We here utilize SMC for estimation of the statistics Bayesian MAB agents compute, and devise flexible policies that can address a rich class of bandit problems: i.e., MABs with nonlinear, stateless- and context-dependent reward distributions that evolve over time. We showcase how non-stationary bandits, where time dynamics are modeled via linear dynamical systems, can be successfully addressed by SMC-based Bayesian bandit agents. We empirically demonstrate good regret performance of the proposed SMC-based bandit policies in several MAB scenarios that have remained elusive, i.e., in non-stationary bandits with nonlinear rewards.
Nonparametric Gaussian Mixture Models for the Multi-Armed Bandit
Authors: Iñigo Urteaga, Chris H. Wiggins
Date: 2018-08-08 00:00:00
Tasks: Density Estimation, Multi-Armed Bandits, Thompson Sampling

We here adopt Bayesian nonparametric mixture models to extend multi-armed bandits in general, and Thompson sampling in particular, to scenarios where there is reward model uncertainty. In the stochastic multi-armed bandit, the reward for the played arm is generated from an unknown distribution. Reward uncertainty, i.e., the lack of knowledge about the reward-generating distribution, induces the exploration-exploitation trade-off: a bandit agent needs to simultaneously learn the properties of the reward distribution and sequentially decide which action to take next. In this work, we extend Thompson sampling to scenarios where there is reward model uncertainty by adopting Bayesian nonparametric Gaussian mixture models for flexible reward density estimation. The proposed Bayesian nonparametric mixture model Thompson sampling sequentially learns the reward model that best approximates the true, yet unknown, per-arm reward distribution, achieving successful regret performance. We derive, based on a novel posterior convergence based analysis, an asymptotic regret bound for the proposed method. In addition, we empirically evaluate its performance in diverse and previously elusive bandit environments, e.g., with rewards not in the exponential family, subject to outliers, and with different per-arm reward distributions. We show that the proposed Bayesian nonparametric Thompson sampling outperforms, both in averaged cumulative regret and in regret volatility, state-of-the-art alternatives. The proposed method is valuable in the presence of bandit reward model uncertainty, as it avoids stringent case-by-case model design choices, yet provides important regret savings.
OCT segmentation: Integrating open parametric contour model of the retinal layers and shape constraint to the Mumford-Shah functional
Authors: Jinming Duan, Weicheng Xie, Ryan Wen Liu, Christopher Tench, Irene Gottlob, Frank Proudlock, Li Bai
Date: 2018-08-08 00:00:00

In this paper, we propose a novel retinal layer boundary model for
segmentation of optical coherence tomography (OCT) images. The retinal layer
boundary model consists of 9 open parametric contours representing the 9
retinal layers in OCT images. An intensity-based Mumford-Shah (MS) variational
functional is first defined to evolve the retinal layer boundary model to
segment the 9 layers simultaneously. By making use of the normals of open
parametric contours, we construct equal sized adjacent narrowbands that are
divided by each contour. Regional information in each narrowband can thus be
integrated into the MS energy functional such that its optimisation is robust
against different initialisations. A statistical prior is also imposed on the
shape of the segmented parametric contours for the functional. As such, by
minimising the MS energy functional the parametric contours can be driven
towards the true boundaries of retinal layers, while the similarity of the
contours with respect to training OCT shapes is preserved. Experimental results
on real OCT images demonstrate that the method is accurate and robust to low
quality OCT images with low contrast and high-level speckle noise, and it
outperforms the recent geodesic distance based method for segmenting 9 layers
of the retina in OCT images.
A new approach to content-based file type detection
Authors: M. C. Amirani, M. Toorani, A. A. Beheshti
Date: 2010-02-17 00:00:00
Tasks: Clustering, File Type Identification, Vocal Bursts Type Prediction

File type identification and file type clustering may be difficult tasks that
have an increasingly importance in the field of computer and network security.
Classical methods of file type detection including considering file extensions
and magic bytes can be easily spoofed. Content-based file type detection is a
newer way that is taken into account recently. In this paper, a new
content-based method for the purpose of file type detection and file type
clustering is proposed that is based on the PCA and neural networks. The
proposed method has a good accuracy and is fast enough.
Evolutionary optimisation of neural network models for fish collective behaviours in mixed groups of robots and zebrafish
Authors: Leo Cazenille, Nicolas Bredeche, José Halloy
Date: 2018-08-09 00:00:00
Tasks: Evolutionary Algorithms

Animal and robot social interactions are interesting both for ethological
studies and robotics. On the one hand, the robots can be tools and models to
analyse animal collective behaviours, on the other hand, the robots and their
artificial intelligence are directly confronted and compared to the natural
animal collective intelligence. The first step is to design robots and their
behavioural controllers that are capable of socially interact with animals.
Designing such behavioural bio-mimetic controllers remains an important
challenge as they have to reproduce the animal behaviours and have to be
calibrated on experimental data. Most animal collective behavioural models are
designed by modellers based on experimental data. This process is long and
costly because it is difficult to identify the relevant behavioural features
that are then used as a priori knowledge in model building. Here, we want to
model the fish individual and collective behaviours in order to develop robot
controllers. We explore the use of optimised black-box models based on
artificial neural networks (ANN) to model fish behaviours. While the ANN may
not be biomimetic but rather bio-inspired, they can be used to link perception
to motor responses. These models are designed to be implementable as robot
controllers to form mixed-groups of fish and robots, using few a priori
knowledge of the fish behaviours. We present a methodology with multilayer
perceptron or echo state networks that are optimised through evolutionary
algorithms to model accurately the fish individual and collective behaviours in
a bounded rectangular arena. We assess the biomimetism of the generated models
and compare them to the fish experimental behaviours.
Ensemble Kalman Inversion: A Derivative-Free Technique For Machine Learning Tasks
Authors: Nikola B. Kovachki, Andrew M. Stuart
Date: 2018-08-10 00:00:00
Tasks: BIG-bench Machine Learning

The standard probabilistic perspective on machine learning gives rise to
empirical risk-minimization tasks that are frequently solved by stochastic
gradient descent (SGD) and variants thereof. We present a formulation of these
tasks as classical inverse or filtering problems and, furthermore, we propose
an efficient, gradient-free algorithm for finding a solution to these problems
using ensemble Kalman inversion (EKI). Applications of our approach include
offline and online supervised learning with deep neural networks, as well as
graph-based semi-supervised learning. The essence of the EKI procedure is an
ensemble based approximate gradient descent in which derivatives are replaced
by differences from within the ensemble. We suggest several modifications to
the basic method, derived from empirically successful heuristics developed in
the context of SGD. Numerical results demonstrate wide applicability and
robustness of the proposed algorithm.
An Iterative Path-Breaking Approach with Mutation and Restart Strategies for the MAX-SAT Problem
Authors: Zhen-Xing Xu, Kun He, Chu-min Li
Date: 2018-08-10 00:00:00
Tasks: Combinatorial Optimization

Although Path-Relinking is an effective local search method for many
combinatorial optimization problems, its application is not straightforward in
solving the MAX-SAT, an optimization variant of the satisfiability problem
(SAT) that has many real-world applications and has gained more and more
attention in academy and industry. Indeed, it was not used in any recent
competitive MAX-SAT algorithms in our knowledge. In this paper, we propose a
new local search algorithm called IPBMR for the MAX-SAT, that remedies the
drawbacks of the Path-Relinking method by using a careful combination of three
components: a new strategy named Path-Breaking to avoid unpromising regions of
the search space when generating trajectories between two elite solutions; a
weak and a strong mutation strategies, together with restarts, to diversify the
search; and stochastic path generating steps to avoid premature local optimum
solutions. We then present experimental results to show that IPBMR outperforms
two of the best state-of-the-art MAX-SAT solvers, and an empirical
investigation to identify and explain the effect of the three components in
IPBMR.
Weakly supervised learning of indoor geometry by dual warping
Authors: Pulak Purkait, Ujwal Bonde, Christopher Zach
Date: 2018-08-10 00:00:00
Tasks: Prediction, Weakly-supervised Learning

A major element of depth perception and 3D understanding is the ability to
predict the 3D layout of a scene and its contained objects for a novel pose.
Indoor environments are particularly suitable for novel view prediction, since
the set of objects in such environments is relatively restricted. In this work
we address the task of 3D prediction especially for indoor scenes by leveraging
only weak supervision. In the literature 3D scene prediction is usually solved
via a 3D voxel grid. However, such methods are limited to estimating rather
coarse 3D voxel grids, since predicting entire voxel spaces has large
computational costs. Hence, our method operates in image-space rather than in
voxel space, and the task of 3D estimation essentially becomes a depth image
completion problem. We propose a novel approach to easily generate training
data containing depth maps with realistic occlusions, and subsequently train a
network for completing those occluded regions. Using multiple publicly
available dataset~\cite{song2017semantic,Silberman:ECCV12} we benchmark our
method against existing approaches and are able to obtain superior performance.
We further demonstrate the flexibility of our method by presenting results for
new view synthesis of RGB-D images.