Embedding Theory of Reservoir Computing and Reducing Reservoir Network Using Time Delays
Soochow University（苏州大学）；Fudan University（复旦大学）；Potsdam Institute for Climate Impact Research (PIK)（波茨坦气候影响研究所（PIK））；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Xing-Yue Duan, Xiong Ying, Si-Yang Leng, Jürgen Kurths, Wei Lin, Huan-Fei Ma
Reservoir computing (RC), a particular form of recurrent neural network, is under explosive development due to its exceptional efficacy and high performance in reconstruction or/and prediction of complex physical systems. However, the mechanism triggering such effective applications of RC is still unclear, awaiting deep and systematic exploration. Here, combining the delayed embedding theory with the generalized embedding theory, we rigorously prove that RC is essentially a high dimensional embedding of the original input nonlinear dynamical system. Thus, using this embedding property, we unify into a universal framework the standard RC and the time-delayed RC where we novelly introduce time delays only into the network's output layer, and we further find a trade-off relation between the time delays and the number of neurons in RC. Based on this finding, we significantly reduce the network size of RC for reconstructing and predicting some representative physical systems, and, more surprisingly, only using a single neuron reservoir with time delays is sometimes sufficient for achieving those tasks.
Deep Time-Delay Reservoir Computing: Dynamics and Memory Capacity
Institute of Theoretical Physics, Technische Universität Berlin（柏林工业大学理论物理研究所）；Department of Mathematics, Humboldt-Universität zu Berlin（柏林洪堡大学数学系）；Institute of Mathematics, Technische Universität Berlin（柏林工业大学数学研究所）
Performance boost of time-delay reservoir computing by non-resonant clock cycle
Technische Universität Berlin（柏林工业大学）；Humboldt-Universität zu Berlin（柏林洪堡大学）；Instituto de Física Interdisciplinar y Sistemas Complejos, IFISC (CSIC-UIB)（跨学科物理与复杂系统研究所（IFISC，西班牙国家研究委员会-巴利阿里群岛大学））
Techniques for Enhancing Memory Capacity of Reservoir Computing
Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology（九州工业大学生命科学与系统工程学院）；Research Center for Neuromorphic AI Hardware, Kyushu Institute of Technology（九州工业大学神经形态人工智能硬件研究中心）；Faculty of Informatics, The University of Fukuchiyama（福知山公立大学信息学部）
Harnessing Synthetic Active Particles for Physical Reservoir Computing
Peter Debye Institute for Soft Matter Physics, Leipzig University（莱比锡大学彼得·德拜软物质物理研究所）；Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig（德累斯顿/莱比锡可扩展数据分析与人工智能中心（ScaDS.AI））
On the emergence of numerical instabilities in Next Generation Reservoir Computing
Department of Electrical and Computer Engineering, Clarkson University, Potsdam, 13699, New York, U.S.A.（电气与计算机工程系，克林顿大学）；Clarkson Center for Complex Systems Science, Clarkson University, Potsdam, 13699, New York, U.S.A.（复杂系统科学中心，克林顿大学）
How more data can hurt: Instability and regularization in next-generation reservoir computing
Santa Fe Institute（圣达菲研究所）；Department of Electrical and Computer Engineering（电气与计算机工程系）；Clarkson University（克拉克逊大学）；Clarkson Center for Complex Systems Science（复杂系统科学中心）；Department of Physics（物理系）；Toronto Metropolitan University（多伦多 Metropolitan 大学）；Department of Automation（自动化系）
Next-Generation Reservoir Computing for Dynamical Inference
Centre for Mathematical Science, Lund University（数学科学中心，隆德大学）；IMFUFA, Department of Science and Environment, Roskilde University（IMFUFA，科学与环境系，罗斯基尔德大学）
Identifying Systems with Symmetries using Equivariant Autoregressive Reservoir Computers
Department of Statistics and Research, CNBS（统计与研究系，CNBS）；Department of Applied Mathematics, UNAH（应用数学系，UNAH）；Department of Chemical Engineering, Norwegian University of Science and Technology（化学工程系，挪威科学技术大学）
Designing a Deep Learning-Driven Resource-Efficient Diagnostic System for Metastatic Breast Cancer: Reducing Long Delays of Clinical Diagnosis and Improving Patient Survival in Developing Countries
University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）；Meta Platforms Inc.（元平台公司）
