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One-Shot Neural Architecture Search: Maximising Diversity to Overcome Catastrophic Forgetting 期刊论文
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2021, 卷号: 43, 期号: 9, 页码: 2921-2935
Authors:  Zhang, Miao;  Li, Huiqi;  Pan, Shirui;  Chang, Xiaojun;  Zhou, Chuan;  Ge, Zongyuan;  Su, Steven
Favorite  |  View/Download:10/0  |  Submit date:2021/10/26
Computer architecture  Training  Optimization  Neural networks  Search methods  Australia  Germanium  AutoML  neural architecture search  continual learning  catastrophic forgetting  novelty search  
Deep Nitsche Method: Deep Ritz Method with Essential Boundary Conditions 期刊论文
COMMUNICATIONS IN COMPUTATIONAL PHYSICS, 2021, 卷号: 29, 期号: 5, 页码: 1365-1384
Authors:  Liao, Yulei;  Ming, Pingbing
Favorite  |  View/Download:13/0  |  Submit date:2021/06/01
Deep Nitsche Method  Deep Ritz Method  neural network approximation  mixed boundary conditions  curse of dimensionality  
A Novel Synchronization Protocol for Nonlinear Stochastic Dynamical Networked Systems 期刊论文
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2021, 卷号: 51, 期号: 5, 页码: 2676-2686
Authors:  Gu, Haibo;  Wang, Xiong;  Liu, Kexin;  Lu, Jinhu
Favorite  |  View/Download:16/0  |  Submit date:2021/06/01
Synchronization  Protocols  Stochastic processes  Artificial neural networks  Adaptive systems  Perturbation methods  Complex dynamical network  Lyapunov function  networked system  stochastic perturbation  synchronization  
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators 期刊论文
NATURE MACHINE INTELLIGENCE, 2021, 卷号: 3, 期号: 3, 页码: 218-+
Authors:  Lu, Lu;  Jin, Pengzhan;  Pang, Guofei;  Zhang, Zhongqiang;  Karniadakis, George Em
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A wavelet-based learning approach assisted multiscale analysis for estimating the effective thermal conductivities of particulate composites 期刊论文
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2021, 卷号: 374, 页码: 28
Authors:  Dong, Hao;  Nie, Yufeng;  Cui, Junzhi;  Kou, Wenbo;  Zou, Minqiang;  Han, Junyan;  Guan, Xiaofei;  Yang, Zihao
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Particulate composites  Effective thermal conductivities  Multiscale modeling  Artificial neural network  Wavelet transform  
SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems 期刊论文
NEURAL NETWORKS, 2020, 卷号: 132, 页码: 166-179
Authors:  Jin, Pengzhan;  Zhang, Zhen;  Zhu, Aiqing;  Tang, Yifa;  Karniadakis, George Em
Favorite  |  View/Download:15/0  |  Submit date:2021/04/26
Deep learning  Physics-informed  Dynamical systems  Hamiltonian systems  Symplectic maps  Symplectic integrators  
Circular Complex-Valued GMDH-Type Neural Network for Real-Valued Classification Problems 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2020, 卷号: 31, 期号: 12, 页码: 5285-5299
Authors:  Xiao, Jin;  Jia, Yanlin;  Jiang, Xiaoyi;  Wang, Shouyang
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Biological system modeling  Data models  Biological neural networks  Predictive models  Neurons  Mathematical model  Complex-valued external criterion  complex-valued group method of data handling (GMDH)-type neural network  parameter estimation  real-valued classification  
Nonlinear quantum neuron: A fundamental building block for quantum neural networks 期刊论文
PHYSICAL REVIEW A, 2020, 卷号: 102, 期号: 5, 页码: 8
Authors:  Yan, Shilu;  Qi, Hongsheng;  Cui, Wei
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Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness 期刊论文
NEURAL NETWORKS, 2020, 卷号: 130, 页码: 85-99
Authors:  Jin, Pengzhan;  Lu, Lu;  Tang, Yifa;  Karniadakis, George Em
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Neural networks  Generalization error  Learnability  Data distribution  Cover complexity  Neural network smoothness  
Output Tracking of Boolean Control Networks 期刊论文
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2020, 卷号: 65, 期号: 6, 页码: 2730-2735
Authors:  Zhang, Xiao;  Wang, Yuanhua;  Cheng, Daizhan
Favorite  |  View/Download:21/0  |  Submit date:2020/09/23
Controllability  Indexes  Trajectory  Open loop systems  Artificial neural networks  Tracking loops  Control theory  Boolean control network (BCN)  control invariant subset  output tracking  semitensor product (STP)  set controllability