Image de couverture de livre

Symplectic Reservoir Representation of Legendre Dynamics

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Fong, Robert Simon, Tanaka, Gouhei, Aihara, Kazuyuki
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Structuring Multiple Simple Cycle Reservoirs with Particle Swarm Optimization
par: Li, Ziqiang, et autres
Publié: (2025)

Predicting unobserved climate time series data at distant areas via spatial correlation using reservoir computing
par: Koyama, Shihori, et autres
Publié: (2024)

Online Adaptive Reinforcement Learning with Echo State Networks for Non-Stationary Dynamics
par: Yoshimura, Aoi, et autres
Publié: (2026)

Federated Learning with Reservoir State Analysis for Time Series Anomaly Detection
par: Nogami, Keigo, et autres
Publié: (2025)

Reservoir Predictive Path Integral Control for Unknown Nonlinear Dynamics
par: Inoue, Daisuke, et autres
Publié: (2025)

Hand Gesture Recognition from Doppler Radar Signals Using Echo State Networks
par: Sano, Towa, et autres
Publié: (2026)

Enhancing Time-Series Anomaly Detection by Integrating Spectral-Residual Bottom-Up Attention with Reservoir Computing
par: Nihei, Hayato, et autres
Publié: (2025)

Attractor-merging Crises and Intermittency in Reservoir Computing
par: Kabayama, Tempei, et autres
Publié: (2025)

Universality of Real Minimal Complexity Reservoir
par: Fong, Robert Simon, et autres
Publié: (2024)

Predictive Modeling in the Reservoir Kernel Motif Space
par: Tino, Peter, et autres
Publié: (2024)

Hebbian Physics Networks: A Self-Organizing Computational Architecture Based on Local Physical Laws
par: Auti, Gunjan, et autres
Publié: (2025)

Diffusion model for relational inference
par: Zheng, Shuhan, et autres
Publié: (2024)

Brain-inspired Chaotic Graph Backpropagation for Large-scale Combinatorial Optimization
par: Tao, Peng, et autres
Publié: (2024)

Chaos-based reinforcement learning with TD3
par: Matsuki, Toshitaka, et autres
Publié: (2024)

Timing-Based Backpropagation in Spiking Neural Networks Without Single-Spike Restrictions
par: Yamamoto, Kakei, et autres
Publié: (2022)

Harnessing Nonidealities in Analog In-Memory Computing Circuits: A Physical Modeling Approach for Neuromorphic Systems
par: Sakemi, Yusuke, et autres
Publié: (2024)

Designing Chaotic Attractors: A Semi-supervised Approach
par: Kabayama, Tempei, et autres
Publié: (2024)

Beyond Isotropy in JEPAs: Hamiltonian Geometry and Symplectic Prediction
par: Alvarez, Robert Jenkinson
Publié: (2026)

Kolmogorov-Arnold Representation for Symplectic Learning: Advancing Hamiltonian Neural Networks
par: Wu, Zongyu, et autres
Publié: (2025)

Fast instance-specific algorithm configuration with graph neural network
par: Aihara, Shingo, et autres
Publié: (2025)

Data-Driven Identification of Quadratic Representations for Nonlinear Hamiltonian Systems using Weakly Symplectic Liftings
par: Yildiz, Süleyman, et autres
Publié: (2023)

Relaxation-assisted reverse annealing on nonnegative/binary matrix factorization
par: Haba, Renichiro, et autres
Publié: (2025)

Dynamics-Informed Reservoir Computing with Visibility Graphs
par: Geier, Charlotte, et autres
Publié: (2025)

Next-Generation Reservoir Computing for Dynamical Inference
par: Cestnik, Rok, et autres
Publié: (2025)

Symplectic convolutional neural networks
par: Yıldız, Süleyman, et autres
Publié: (2025)

Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control
par: Zhong, Yaofeng Desmond, et autres
Publié: (2019)

Symplectic Bregman divergences
par: Nielsen, Frank
Publié: (2024)

Deciphering interventional dynamical causality from non-intervention complex systems
par: Shi, Jifan, et autres
Publié: (2024)

FLAME: Flow Enhanced Legendre Memory Models for General Time Series Forecasting
par: Wu, Xingjian, et autres
Publié: (2025)

Change of measure through the Legendre transform
par: Picard-Weibel, Antoine, et autres
Publié: (2022)

Recurrent Quantum Feature Maps for Reservoir Computing
par: Singh, Utkarsh, et autres
Publié: (2026)

OpenReservoirComputing: GPU-Accelerated Reservoir Computing in JAX
par: Williams, Jan, et autres
Publié: (2026)

A note on the Artstein-Avidan-Milman's generalized Legendre transforms
par: Nielsen, Frank
Publié: (2025)

Provable Low-Frequency Bias of In-Context Learning of Representations
par: Yang, Yongyi, et autres
Publié: (2025)

Symplectic Neural Networks Based on Dynamical Systems
par: Tapley, Benjamin K
Publié: (2024)

Ultra-Early Prediction of Tipping Points: Integrating Dynamical Measures with Reservoir Computing
par: Li, Xin, et autres
Publié: (2026)

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics
par: Schestakov, Stefan, et autres
Publié: (2024)

QuadraSHAP: Stable and Scalable Shapley Values for Product Games via Gauss-Legendre Quadrature
par: Mohammadi, Majid, et autres
Publié: (2026)

Hybridizing Traditional and Next-Generation Reservoir Computing to Accurately and Efficiently Forecast Dynamical Systems
par: Chepuri, Ravi, et autres
Publié: (2024)

Conformal Symplectic Optimization for Stable Reinforcement Learning
par: Lyu, Yao, et autres
Publié: (2024)