
Reinforcement Learning Control of Constrained Dynamic Systems with Uniformly Ultimate Boundedness Stability Guarantee
Reinforcement learning (RL) is promising for complicated stochastic nonl...
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LyapunovBased Reinforcement Learning State Estimator
In this paper, we consider the state estimation problem for nonlinear st...
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SocialVRNN: OneShot Multimodal Trajectory Prediction for Interacting Pedestrians
Prediction of human motions is key for safe navigation of autonomous rob...
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Penalized modelbased clustering of fMRI data
Functional magnetic resonance imaging (fMRI) data have become increasing...
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LyapunovBased Reinforcement Learning for Decentralized MultiAgent Control
Decentralized multiagent control has broad applications, ranging from m...
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Assessing Impact of Unobserved Confounders with Sensitivity Index Probabilities through PseudoExperiments
Unobserved confounders are a longstanding issue in causal inference usi...
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ModelReference Reinforcement Learning for CollisionFree Tracking Control of Autonomous Surface Vehicles
This paper presents a novel modelreference reinforcement learning algor...
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Towards Lossless Binary Convolutional Neural Networks Using Piecewise Approximation
Binary Convolutional Neural Networks (CNNs) can significantly reduce the...
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Segmentation Based Mesh Denoising
Featurepreserving mesh denoising has received noticeable attention rece...
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ActorCritic Reinforcement Learning for Control with Stability Guarantee
Deep Reinforcement Learning (DRL) has achieved impressive performance in...
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ModelReference Reinforcement Learning Control of Autonomous Surface Vehicles with Uncertainties
This paper presents a novel modelreference reinforcement learning contr...
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Gaussian Curvature Filter on 3D Mesh
Minimizing Gaussian curvature of meshes is fundamentally important for o...
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A Sparse Bayesian Deep Learning Approach for Identification of Cascaded Tanks Benchmark
Nonlinear system identification is important with a wide range of applic...
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H_∞ Modelfree Reinforcement Learning with Robust Stability Guarantee
Reinforcement learning is showing great potentials in robotics applicati...
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H_inf Modelfree Reinforcement Learning with Robust Stability Guarantee
Reinforcement learning is showing great potentials in robotics applicati...
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Data assimilation for a quasigeostrophic model with circulationpreserving stochastic transport noise
This paper contains the latest installment of the authors' project on de...
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A Particle Filter for Stochastic Advection by Lie Transport (SALT): A case study for the damped and forced incompressible 2D Euler equation
In this work, we apply a particle filter with three additional procedure...
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BayesNAS: A Bayesian Approach for Neural Architecture Search
OneShot Neural Architecture Search (NAS) is a promising method to signi...
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HLO: Halfkernel Laplacian Operator for Surface Smoothing
This paper presents a simple yet effective method for featurepreserving...
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Probabilistic Recursive Reasoning for MultiAgent Reinforcement Learning
Humans are capable of attributing latent mental contents such as beliefs...
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Datadriven Discovery of CyberPhysical Systems
Cyberphysical systems (CPSs) embed software into the physical world. Th...
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Asymptotically Independent UStatistics in HighDimensional Testing
Many highdimensional hypothesis tests aim to globally examine marginal ...
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Towards Accurate Binary Convolutional Neural Network
We introduce a novel scheme to train binary convolutional neural network...
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Enabling Quality Control for Entity Resolution: A Human and Machine Cooperation Framework
Even though many machine algorithms have been proposed for entity resolu...
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Mixed Neural Network Approach for Temporal Sleep Stage Classification
This paper proposes a practical approach to addressing limitations posed...
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Human collective intelligence as distributed Bayesian inference
Collective intelligence is believed to underly the remarkable success of...
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DropNeuron: Simplifying the Structure of Deep Neural Networks
Deep learning using multilayer neural networks (NNs) architecture manif...
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Penalized modelbased clustering with clusterspecific diagonal covariance matrices and grouped variables
Clustering analysis is one of the most widely used statistical tools in ...
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Wei Pan
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