pwc-archive/papers-with-abstracts huggingface dataset:
Merge Non-Dominated Sorting Algorithm for Many-Objective Optimization
Authors: Javier Moreno, Daniel Rodriguez, Antonio Nebro, Jose A. Lozano
Date: 2018-09-17 00:00:00
Tasks: Evolutionary Algorithms

Many Pareto-based multi-objective evolutionary algorithms require to rank the
solutions of the population in each iteration according to the dominance
principle, what can become a costly operation particularly in the case of
dealing with many-objective optimization problems. In this paper, we present a
new efficient algorithm for computing the non-dominated sorting procedure,
called Merge Non-Dominated Sorting (MNDS), which has a best computational
complexity of $\Theta(NlogN)$ and a worst computational complexity of
$\Theta(MN^2)$. Our approach is based on the computation of the dominance set
of each solution by taking advantage of the characteristics of the merge sort
algorithm. We compare the MNDS against four well-known techniques that can be
considered as the state-of-the-art. The results indicate that the MNDS
algorithm outperforms the other techniques in terms of number of comparisons as
well as the total running time.
Policy Optimization via Importance Sampling
Authors: Alberto Maria Metelli, Matteo Papini, Francesco Faccio, Marcello Restelli
Date: 2018-09-17 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: continuous-control, Continuous Control, Reinforcement Learning

Policy optimization is an effective reinforcement learning approach to solve
continuous control tasks. Recent achievements have shown that alternating
online and offline optimization is a successful choice for efficient trajectory
reuse. However, deciding when to stop optimizing and collect new trajectories
is non-trivial, as it requires to account for the variance of the objective
function estimate. In this paper, we propose a novel, model-free, policy search
algorithm, POIS, applicable in both action-based and parameter-based settings.
We first derive a high-confidence bound for importance sampling estimation;
then we define a surrogate objective function, which is optimized offline
whenever a new batch of trajectories is collected. Finally, the algorithm is
tested on a selection of continuous control tasks, with both linear and deep
policies, and compared with state-of-the-art policy optimization methods.
Similarity measure for Public Persons
Authors: Andreas Stöckl
Date: 2018-09-17 00:00:00
Tasks: Articles, Entity Extraction using GAN, Time Series, Time Series Analysis

For the webportal "Who is in the News!" with statistics about the appearence
of persons in written news we developed an extension, which measures the
relationship of public persons depending on a time parameter, as the
relationship may vary over time. On a training corpus of English and German
news articles we built a measure by extracting the persons occurrence in the
text via pretrained named entity extraction and then construct time series of
counts for each person. Pearson correlation over a sliding window is then used
to measure the relation of two persons.
An Integral Pose Regression System for the ECCV2018 PoseTrack Challenge
Authors: Xiao Sun, Chuankang Li, Stephen Lin
Date: 2018-09-17 00:00:00
Tasks: 3D Human Pose Estimation, Pose Estimation, regression

For the ECCV 2018 PoseTrack Challenge, we present a 3D human pose estimation
system based mainly on the integral human pose regression method. We show a
comprehensive ablation study to examine the key performance factors of the
proposed system. Our system obtains 47mm MPJPE on the CHALL_H80K test dataset,
placing second in the ECCV2018 3D human pose estimation challenge. Code will be
released to facilitate future work.
Seuillage par hystérésis pour le test de photo-consistance des voxels dans le cadre de la reconstruction 3D
Authors: Mohamed Chafik Bakkay, Walid Barhoumi, Ezzeddine Zagrouba
Date: 2018-09-17 00:00:00

Voxel coloring is a popular method of reconstructing a three-dimensional
surface model from a set of calibrated 2D images. However, the reconstruction
quality is largely dependent on a thresholding procedure allowing the authors
to decide, for each voxel, whether it is photo-consistent or not. Even so, this
method is widely used because of its simplicity and low computational cost. We
have returned to this method in order to propose an improvement in the
thresholding step which will be fully automated. Indeed, the geometrical
information is implicitly integrated using an hysteresis thresholding which
takes into account the spatial coherence of color voxels. Moreover, the
ambiguity of choosing the thresholds is extremely minimized by defining a fuzzy
degree of membership of each voxel into the class of consistent voxels. Also,
there is no need for preset thresholds since the hysteresis ones are defined
automatically and adaptively depending on the number of images that the voxel
isprojected onto. Preliminary results are very promising and demonstrate that
the proposed method performs automatically precise and smooth volumetric scene
reconstruction.
Object-sensitive Deep Reinforcement Learning
Authors: Yuezhang Li, Katia Sycara, Rahul Iyer
Date: 2018-09-17 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, Object, Object Recognition, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Robot Navigation

Deep reinforcement learning has become popular over recent years, showing
superiority on different visual-input tasks such as playing Atari games and
robot navigation. Although objects are important image elements, few work
considers enhancing deep reinforcement learning with object characteristics. In
this paper, we propose a novel method that can incorporate object recognition
processing to deep reinforcement learning models. This approach can be adapted
to any existing deep reinforcement learning frameworks. State-of-the-art
results are shown in experiments on Atari games. We also propose a new approach
called "object saliency maps" to visually explain the actions made by deep
reinforcement learning agents.
Transparency and Explanation in Deep Reinforcement Learning Neural Networks
Authors: Rahul Iyer, Yuezhang Li, Huao Li, Michael Lewis, Ramitha Sundar, Katia Sycara
Date: 2018-09-17 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, Object Recognition, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Autonomous AI systems will be entering human society in the near future to
provide services and work alongside humans. For those systems to be accepted
and trusted, the users should be able to understand the reasoning process of
the system, i.e. the system should be transparent. System transparency enables
humans to form coherent explanations of the system's decisions and actions.
Transparency is important not only for user trust, but also for software
debugging and certification. In recent years, Deep Neural Networks have made
great advances in multiple application areas. However, deep neural networks are
opaque. In this paper, we report on work in transparency in Deep Reinforcement
Learning Networks (DRLN). Such networks have been extremely successful in
accurately learning action control in image input domains, such as Atari games.
In this paper, we propose a novel and general method that (a) incorporates
explicit object recognition processing into deep reinforcement learning models,
(b) forms the basis for the development of "object saliency maps", to provide
visualization of internal states of DRLNs, thus enabling the formation of
explanations and (c) can be incorporated in any existing deep reinforcement
learning framework. We present computational results and human experiments to
evaluate our approach.
Model-Driven Deep Learning for Physical Layer Communications
Authors: Hengtao He, Shi Jin, Chao-Kai Wen, Feifei Gao, Geoffrey Ye Li, Zongben Xu
Date: 2018-09-17 00:00:00
Tasks: Deep Learning, Intelligent Communication, model

Intelligent communication is gradually considered as the mainstream direction
in future wireless communications. As a major branch of machine learning, deep
learning (DL) has been applied in physical layer communications and has
demonstrated an impressive performance improvement in recent years. However,
most of the existing works related to DL focus on data-driven approaches, which
consider the communication system as a black box and train it by using a huge
volume of data. Training a network requires sufficient computing resources and
extensive time, both of which are rarely found in communication devices. By
contrast, model-driven DL approaches combine communication domain knowledge
with DL to reduce the demand for computing resources and training time. This
article reviews the recent advancements in the application of model-driven DL
approaches in physical layer communications, including transmission scheme,
receiver design, and channel information recovery. Several open issues for
further research are also highlighted after presenting the comprehensive
survey.
A Grover-search Based Quantum Learning Scheme for Classification
Authors: Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, DaCheng Tao
Date: 2018-09-17 00:00:00
Tasks: Classification, Ensemble Learning

The hybrid quantum-classical learning scheme provides a prominent way to achieve quantum advantages on near-term quantum devices. A concrete example towards this goal is the quantum neural network (QNN), which has been developed to accomplish various supervised learning tasks such as classification and regression. However, there are two central issues that remain obscure when QNN is exploited to accomplish classification tasks. First, a quantum classifier that can well balance the computational cost such as the number of measurements and the learning performance is unexplored. Second, it is unclear whether quantum classifiers can be applied to solve certain problems that outperform their classical counterparts. Here we devise a Grover-search based quantum learning scheme (GBLS) to address the above two issues. Notably, most existing QNN-based quantum classifiers can be seamlessly embedded into the proposed scheme. The key insight behind our proposal is reformulating the classification tasks as the search problem. Numerical simulations exhibit that GBLS can achieve comparable performance with other quantum classifiers under various noise settings, while the required number of measurements is dramatically reduced. We further demonstrate a potential quantum advantage of GBLS over classical classifiers in the measure of query complexity. Our work provides guidance to develop advanced quantum classifiers on near-term quantum devices and opens up an avenue to explore potential quantum advantages in various classification tasks.
Building Prior Knowledge: A Markov Based Pedestrian Prediction Model Using Urban Environmental Data
Authors: Pavan Vasishta, Dominique Vaufreydaz, Anne Spalanzani
Date: 2018-09-17 00:00:00
Tasks: Autonomous Vehicles

Autonomous Vehicles navigating in urban areas have a need to understand and
predict future pedestrian behavior for safer navigation. This high level of
situational awareness requires observing pedestrian behavior and extrapolating
their positions to know future positions. While some work has been done in this
field using Hidden Markov Models (HMMs), one of the few observed drawbacks of
the method is the need for informed priors for learning behavior. In this work,
an extension to the Growing Hidden Markov Model (GHMM) method is proposed to
solve some of these drawbacks. This is achieved by building on existing work
using potential cost maps and the principle of Natural Vision. As a
consequence, the proposed model is able to predict pedestrian positions more
precisely over a longer horizon compared to the state of the art. The method is
tested over "legal" and "illegal" behavior of pedestrians, having trained the
model with sparse observations and partial trajectories. The method, with no
training data, is compared against a trained state of the art model. It is
observed that the proposed method is robust even in new, previously unseen
areas.
Multi-Player Bandits: A Trekking Approach
Authors: Manjesh K. Hanawal, Sumit J. Darak
Date: 2018-09-17 00:00:00
Tasks: Multi-Armed Bandits

We study stochastic multi-armed bandits with many players. The players do not
know the number of players, cannot communicate with each other and if multiple
players select a common arm they collide and none of them receive any reward.
We consider the static scenario, where the number of players remains fixed, and
the dynamic scenario, where the players enter and leave at any time. We provide
algorithms based on a novel `trekking approach' that guarantees constant regret
for the static case and sub-linear regret for the dynamic case with high
probability. The trekking approach eliminates the need to estimate the number
of players resulting in fewer collisions and improved regret performance
compared to the state-of-the-art algorithms. We also develop an epoch-less
algorithm that eliminates any requirement of time synchronization across the
players provided each player can detect the presence of other players on an
arm. We validate our theoretical guarantees using simulation based and real
test-bed based experiments.
Binocular Tone Mapping with Improved Overall Contrast and Local Details
Authors: Zhuming Zhang, Xinghong Hu, Xueting Liu, Tien-Tsin Wong
Date: 2018-09-17 00:00:00
Tasks: Tone Mapping

Tone mapping is a commonly used technique that maps the set of colors in
high-dynamic-range (HDR) images to another set of colors in low-dynamic-range
(LDR) images, to fit the need for print-outs, LCD monitors and projectors.
Unfortunately, during the compression of dynamic range, the overall contrast
and local details generally cannot be preserved simultaneously. Recently, with
the increased use of stereoscopic devices, the notion of binocular tone mapping
has been proposed in the existing research study. However, the existing
research lacks the binocular perception study and is unable to generate the
optimal binocular pair that presents the most visual content. In this paper, we
propose a novel perception-based binocular tone mapping method, that can
generate an optimal binocular image pair (generating left and right images
simultaneously) from an HDR image that presents the most visual content by
designing a binocular perception metric. Our method outperforms the existing
method in terms of both visual and time performance.
Extracting representations of cognition across neuroimaging studies improves brain decoding
Authors: Arthur Mensch, Julien Mairal, Bertrand Thirion, Gaël Varoquaux
Date: 2018-09-17 00:00:00
Tasks: Brain Decoding

Cognitive brain imaging is accumulating datasets about the neural substrate of many different mental processes. Yet, most studies are based on few subjects and have low statistical power. Analyzing data across studies could bring more statistical power; yet the current brain-imaging analytic framework cannot be used at scale as it requires casting all cognitive tasks in a unified theoretical framework. We introduce a new methodology to analyze brain responses across tasks without a joint model of the psychological processes. The method boosts statistical power in small studies with specific cognitive focus by analyzing them jointly with large studies that probe less focal mental processes. Our approach improves decoding performance for 80% of 35 widely-different functional-imaging studies. It finds commonalities across tasks in a data-driven way, via common brain representations that predict mental processes. These are brain networks tuned to psychological manipulations. They outline interpretable and plausible brain structures. The extracted networks have been made available; they can be readily reused in new neuro-imaging studies. We provide a multi-study decoding tool to adapt to new data.
Greedy Algorithms for Sparse Sensor Placement via Deep Learning
Authors: Louis Ly, Yen-Hsi Richard Tsai
Date: 2018-09-17 00:00:00
Tasks: Deep Learning

We consider the exploration problem: an agent equipped with a depth sensor must map out a previously unknown environment using as few sensor measurements as possible. We propose an approach based on supervised learning of a greedy algorithm. We provide a bound on the optimality of the greedy algorithm using submodularity theory. Using a level set representation, we train a convolutional neural network to determine vantage points that maximize visibility. We show that this method drastically reduces the on-line computational cost and determines a small set of vantage points that solve the problem. This enables us to efficiently produce highly-resolved and topologically accurate maps of complex 3D environments. Unlike traditional next-best-view and frontier-based strategies, the proposed method accounts for geometric priors while evaluating potential vantage points. While existing deep learning approaches focus on obstacle avoidance and local navigation, our method aims at finding near-optimal solutions to the more global exploration problem. We present realistic simulations on 2D and 3D urban environments.
A convex formulation for high-dimensional sparse sliced inverse regression
Authors: Kean Ming Tan, Zhaoran Wang, Tong Zhang, Han Liu, R. Dennis Cook
Date: 2018-09-17 00:00:00
Tasks: Dimensionality Reduction, regression, Variable Selection, Vocal Bursts Intensity Prediction

Sliced inverse regression is a popular tool for sufficient dimension
reduction, which replaces covariates with a minimal set of their linear
combinations without loss of information on the conditional distribution of the
response given the covariates. The estimated linear combinations include all
covariates, making results difficult to interpret and perhaps unnecessarily
variable, particularly when the number of covariates is large. In this paper,
we propose a convex formulation for fitting sparse sliced inverse regression in
high dimensions. Our proposal estimates the subspace of the linear combinations
of the covariates directly and performs variable selection simultaneously. We
solve the resulting convex optimization problem via the linearized alternating
direction methods of multiplier algorithm, and establish an upper bound on the
subspace distance between the estimated and the true subspaces. Through
numerical studies, we show that our proposal is able to identify the correct
covariates in the high-dimensional setting.
Learning-based attacks in cyber-physical systems
Authors: Mohammad Javad Khojasteh, Anatoly Khina, Massimo Franceschetti, Tara Javidi
Date: 2018-09-17 00:00:00
Tasks: Gaussian Processes

We introduce the problem of learning-based attacks in a simple abstraction of cyber-physical systems---the case of a discrete-time, linear, time-invariant plant that may be subject to an attack that overrides the sensor readings and the controller actions. The attacker attempts to learn the dynamics of the plant and subsequently override the controller's actuation signal, to destroy the plant without being detected. The attacker can feed fictitious sensor readings to the controller using its estimate of the plant dynamics and mimic the legitimate plant operation. The controller, on the other hand, is constantly on the lookout for an attack; once the controller detects an attack, it immediately shuts the plant off. In the case of scalar plants, we derive an upper bound on the attacker's deception probability for any measurable control policy when the attacker uses an arbitrary learning algorithm to estimate the system dynamics. We then derive lower bounds for the attacker's deception probability for both scalar and vector plants by assuming a specific authentication test that inspects the empirical variance of the system disturbance. We also show how the controller can improve the security of the system by superimposing a carefully crafted privacy-enhancing signal on top of the "nominal control policy." Finally, for nonlinear scalar dynamics that belong to the Reproducing Kernel Hilbert Space (RKHS), we investigate the performance of attacks based on nonlinear Gaussian-processes (GP) learning algorithms.
Statistically and Computationally Efficient Variance Estimator for Kernel Ridge Regression
Authors: Meimei Liu, Jean Honorio, Guang Cheng
Date: 2018-09-17 00:00:00
Tasks: regression

In this paper, we propose a random projection approach to estimate variance
in kernel ridge regression. Our approach leads to a consistent estimator of the
true variance, while being computationally more efficient. Our variance
estimator is optimal for a large family of kernels, including cubic splines and
Gaussian kernels. Simulation analysis is conducted to support our theory.
Integrative Analysis of Patient Health Records and Neuroimages via Memory-based Graph Convolutional Network
Authors: Xi Sheryl Zhang, Jingyuan Chou, Fei Wang
Date: 2018-09-17 00:00:00
Tasks: General Classification

With the arrival of the big data era, more and more data are becoming readily available in various real-world applications and those data are usually highly heterogeneous. Taking computational medicine as an example, we have both Electronic Health Records (EHR) and medical images for each patient. For complicated diseases such as Parkinson's and Alzheimer's, both EHR and neuroimaging information are very important for disease understanding because they contain complementary aspects of the disease. However, EHR and neuroimage are completely different. So far the existing research has been mainly focusing on one of them. In this paper, we proposed a framework, Memory-Based Graph Convolution Network (MemGCN), to perform integrative analysis with such multi-modal data. Specifically, GCN is used to extract useful information from the patients' neuroimages. The information contained in the patient EHRs before the acquisition of each brain image is captured by a memory network because of its sequential nature. The information contained in each brain image is combined with the information read out from the memory network to infer the disease state at the image acquisition timestamp. To further enhance the analytical power of MemGCN, we also designed a multi-hop strategy that allows multiple reading and updating on the memory can be performed at each iteration. We conduct experiments using the patient data from the Parkinson's Progression Markers Initiative (PPMI) with the task of classification of Parkinson's Disease (PD) cases versus controls. We demonstrate that superior classification performance can be achieved with our proposed framework, comparing with existing approaches involving a single type of data.
DASNet: Reducing Pixel-level Annotations for Instance and Semantic Segmentation
Authors: Chuang Niu, Shenghan Ren, Jimin Liang
Date: 2018-09-17 00:00:00
Tasks: Segmentation, Semantic Segmentation

Pixel-level annotation demands expensive human efforts and limits the performance of deep networks that usually benefits from more such training data. In this work we aim to achieve high quality instance and semantic segmentation results over a small set of pixel-level mask annotations and a large set of box annotations. The basic idea is exploring detection models to simplify the pixel-level supervised learning task and thus reduce the required amount of mask annotations. Our architecture, named DASNet, consists of three modules: detection, attention, and segmentation. The detection module detects all classes of objects, the attention module generates multi-scale class-specific features, and the segmentation module recovers the binary masks. Our method demonstrates substantially improved performance compared to existing semi-supervised approaches on PASCAL VOC 2012 dataset.