{"total":84,"rows":[{"id":"2107.06658","title":"A Framework for Machine Learning of Model Error in Dynamical Systems","created":"2021-07-14","cited_by_count":38,"area":null},{"id":"2103.03174","title":"Robust Optimization and Validation of Echo State Networks for learning chaotic dynamics","created":"2021-02-09","cited_by_count":27,"area":null},{"id":"2201.13094","title":"Designing Universal Causal Deep Learning Models: The Geometric (Hyper)Transformer","created":"2022-01-31","cited_by_count":22,"area":null},{"id":"2306.03303","title":"Global universal approximation of functional input maps on weighted spaces","created":"2023-06-05","cited_by_count":19,"area":null},{"id":"2108.05024","title":"Learning strange attractors with reservoir systems","created":"2021-08-11","cited_by_count":17,"area":null},{"id":"2305.08753","title":"Neural Oscillators are Universal","created":"2023-05-15","cited_by_count":16,"area":null},{"id":"2307.12904","title":"Universal Approximation Theorem and error bounds for quantum neural networks and quantum reservoirs","created":"2023-07-24","cited_by_count":16,"area":null},{"id":"2302.13752","title":"A Brief Survey on the Approximation Theory for Sequence Modelling","created":"2023-02-27","cited_by_count":15,"area":null},{"id":"2405.05192","title":"Full error analysis of the random deep splitting method for nonlinear parabolic PDEs and PIDEs","created":"2024-05-08","cited_by_count":12,"area":null},{"id":"2209.10166","title":"Chaotic Hedging with Iterated Integrals and Neural Networks","created":"2022-09-21","cited_by_count":10,"area":null},{"id":"2201.02441","title":"Applications of Signature Methods to Market Anomaly Detection","created":"2022-01-07","cited_by_count":9,"area":null},{"id":"2412.09800","title":"Infinite-dimensional next-generation reservoir computing","created":"2024-12-13","cited_by_count":9,"area":null},{"id":"2212.14641","title":"Reservoir kernels and Volterra series","created":"2022-12-30","cited_by_count":8,"area":null},{"id":"2304.00490","title":"Infinite-dimensional reservoir computing","created":"2023-04-02","cited_by_count":7,"area":null},{"id":"2410.00480","title":"Stability analysis of chaotic systems in latent spaces","created":"2024-10-01","cited_by_count":7,"area":null},{"id":"2204.11682","title":"Data-driven prediction and control of extreme events in a chaotic flow","created":"2022-04-25","cited_by_count":6,"area":null},{"id":"2305.01457","title":"Memory of recurrent networks: Do we compute it right?","created":"2023-05-02","cited_by_count":6,"area":null},{"id":"2105.10766","title":"Embedding Information onto a Dynamical System","created":"2021-05-22","cited_by_count":5,"area":null},{"id":"2206.05669","title":"Universality and approximation bounds for echo state networks with random weights","created":"2022-06-12","cited_by_count":5,"area":null},{"id":"2309.05413","title":"Learning noise-induced transitions by multi-scaling reservoir computing","created":"2023-09-11","cited_by_count":5,"area":null},{"id":"2402.00236","title":"Positional Encoding Helps Recurrent Neural Networks Handle a Large Vocabulary","created":"2024-01-31","cited_by_count":5,"area":null},{"id":"2407.08641","title":"How more data can hurt: Instability and regularization in next-generation reservoir computing","created":"2024-07-11","cited_by_count":5,"area":null},{"id":"2506.16332","title":"Feedback-driven recurrent quantum neural network universality","created":"2025-06-19","cited_by_count":5,"area":null},{"id":"2510.05060","title":"ResCP: Reservoir Conformal Prediction for Time Series Forecasting","created":"2025-10-06","cited_by_count":5,"area":null},{"id":"2211.11379","title":"Modelling spatiotemporal turbulent dynamics with the convolutional autoencoder echo state network","created":"2022-11-21","cited_by_count":4,"area":null},{"id":"2405.12382","title":"Stochastic Reservoir Computers","created":"2024-05-20","cited_by_count":4,"area":null},{"id":"2406.10214","title":"Universal randomised signatures for generative time series modelling","created":"2024-06-14","cited_by_count":4,"area":null},{"id":"2108.08001","title":"Nonlinear Autoregression with Convergent Dynamics on Novel Computational Platforms","created":"2021-08-18","cited_by_count":3,"area":null},{"id":"2211.05992","title":"Delay Embedded Echo-State Network: A Predictor for Partially Observed Systems","created":"2022-11-11","cited_by_count":3,"area":null},{"id":"2404.08717","title":"State-space systems as dynamic generative models","created":"2024-04-12","cited_by_count":3,"area":null},{"id":"2406.09310","title":"Neural networks in non-metric spaces","created":"2024-06-13","cited_by_count":3,"area":null},{"id":"2408.08071","title":"Universality of Real Minimal Complexity Reservoir","created":"2024-08-15","cited_by_count":3,"area":null},{"id":"2502.15769","title":"Asymptotic evaluation of the information processing capacity in reservoir computing","created":"2025-02-15","cited_by_count":3,"area":null},{"id":"2505.10080","title":"Role of scrambling and noise in temporal information processing with quantum systems","created":"2025-05-15","cited_by_count":3,"area":null},{"id":"2508.07876","title":"Stochastic dynamics learning with state-space systems","created":"2025-08-11","cited_by_count":3,"area":null},{"id":"2209.00497","title":"Quantum-Classical Hybrid Information Processing via a Single Quantum System","created":"2022-09-01","cited_by_count":2,"area":null},{"id":"2312.15141","title":"Improving the Performance of Echo State Networks Through State Feedback","created":"2023-12-23","cited_by_count":2,"area":null},{"id":"2408.03626","title":"On the choice of the non-trainable internal weights in random feature maps","created":"2024-08-07","cited_by_count":2,"area":null},{"id":"2408.07386","title":"Fading memory and the convolution theorem","created":"2024-08-14","cited_by_count":2,"area":null},{"id":"2501.08640","title":"Quantum Reservoir Computing and Risk Bounds","created":"2025-01-15","cited_by_count":2,"area":null},{"id":"2507.06050","title":"Minimal Deterministic Echo State Networks Outperform Random Reservoirs in Learning Chaotic Dynamics","created":"2025-07-08","cited_by_count":2,"area":null},{"id":"2508.19145","title":"Echoes of the Past: A Unified Perspective on Fading memory and Echo States","created":"2025-08-26","cited_by_count":2,"area":null},{"id":"2509.04422","title":"Echo State Networks as State-Space Models: A Systems Perspective","created":"2025-09-04","cited_by_count":2,"area":null},{"id":"2601.22296","title":"ParalESN: Enabling parallel information processing in Reservoir Computing","created":"2026-01-29","cited_by_count":2,"area":null},{"id":"2105.10759","title":"Universal set of Observables for Forecasting Physical Systems through Causal Embedding","created":"2021-05-22","cited_by_count":1,"area":null},{"id":"2211.08018","title":"Universal Time-Uniform Trajectory Approximation for Random Dynamical Systems with Recurrent Neural Networks","created":"2022-11-15","cited_by_count":1,"area":null},{"id":"2301.06786","title":"Ensemble Reservoir Computing for Dynamical Systems: Prediction of Phase-Space Stable Region for Hadron Storage Rings","created":"2023-01-17","cited_by_count":1,"area":null},{"id":"2304.00198","title":"Sequential Learning from Noisy Data: Data-Assimilation Meets Echo-State Network","created":"2023-04-01","cited_by_count":1,"area":null},{"id":"2309.17032","title":"Refined Kolmogorov Complexity of Analog, Evolving and Stochastic Recurrent Neural Networks","created":"2023-09-29","cited_by_count":1,"area":null},{"id":"2403.01900","title":"Universality of reservoir systems with recurrent neural networks","created":"2024-03-04","cited_by_count":1,"area":null},{"id":"2404.17429","title":"Separation capacity of linear reservoirs with random connectivity matrix","created":"2024-04-26","cited_by_count":1,"area":null},{"id":"2505.00216","title":"Online Federation For Mixtures of Proprietary Agents with Black-Box Encoders","created":"2025-04-30","cited_by_count":1,"area":null},{"id":"2508.18130","title":"Frozen in Time: Parameter-Efficient Time Series Transformers via Reservoir-Induced Feature Expansion and Fixed Random Dynamics","created":"2025-08-25","cited_by_count":1,"area":null},{"id":"2509.11338","title":"Next-Generation Reservoir Computing for Dynamical Inference","created":"2025-09-14","cited_by_count":1,"area":null},{"id":"2512.01015","title":"Upper Approximation Bounds for Neural Oscillators","created":"2025-11-30","cited_by_count":1,"area":null},{"id":"2602.13531","title":"QuaRK: A Quantum Reservoir Kernel for Time Series Learning","created":"2026-02-14","cited_by_count":1,"area":null},{"id":"2608.04028","title":"Lindblad-Inspired Multi-Timescale Reservoir Computing with Separable Rotation and Dissipation","created":"2026-07-27","cited_by_count":1,"area":null},{"id":"2102.06258","title":"Using Echo State Networks to Approximate Value Functions for Control","created":"2021-02-11","cited_by_count":0,"area":null},{"id":"2106.00047","title":"Learning and Generalization in RNNs","created":"2021-05-31","cited_by_count":0,"area":null},{"id":"2308.06213","title":"Change Point Detection with Conceptors","created":"2023-08-11","cited_by_count":0,"area":null},{"id":"2403.19806","title":"Feature-Based Echo-State Networks: A Step Towards Interpretability and Minimalism in Reservoir Computer","created":"2024-03-28","cited_by_count":0,"area":null},{"id":"2404.12315","title":"Adjoint Sensitivities of Chaotic Flows without Adjoint Solvers: A Data-Driven Approach","created":"2024-04-18","cited_by_count":0,"area":null},{"id":"2405.10054","title":"A finite-sample generalization bound for stable LPV systems","created":"2024-05-16","cited_by_count":0,"area":null},{"id":"2407.18017","title":"A Sensitivity Analysis of Cellular Automata and Heterogeneous Topology Networks: Partially-Local Cellular Automata and Homogeneous Homogeneous Random Boolean Networks","created":"2024-07-25","cited_by_count":0,"area":null},{"id":"2412.05126","title":"Skewed Neuronal Heterogeneity Enhances Efficiency On Various Computing Systems","created":"2024-12-06","cited_by_count":0,"area":null},{"id":"2501.11357","title":"On the dimension of pullback attractors in recurrent neural networks","created":"2025-01-20","cited_by_count":0,"area":null},{"id":"2504.08224","title":"Optical Echo State Network Reservoir Computing","created":"2025-04-11","cited_by_count":0,"area":null},{"id":"2505.17740","title":"A tensor network approach for chaotic time series prediction","created":"2025-05-23","cited_by_count":0,"area":null},{"id":"2507.06817","title":"Designing Robust Software Sensors for Nonlinear Systems via Neural Networks and Adaptive Sliding Mode Control","created":"2025-07-09","cited_by_count":0,"area":null},{"id":"2507.21377","title":"Reservoir Computation with Networks of Differentiating Neuron Ring Oscillators","created":"2025-07-28","cited_by_count":0,"area":null},{"id":"2512.14675","title":"Fractal and Chaotic Activation Functions in Echo State Networks: Preprocessing Topology Governs the Echo State Property","created":"2025-12-16","cited_by_count":0,"area":null},{"id":"2512.19409","title":"Symplectic Representation of Legendre Dynamics","created":"2025-12-22","cited_by_count":0,"area":null},{"id":"2603.02178","title":"Reservoir Subspace Injection for Online ICA under Top-n Whitening","created":"2026-03-02","cited_by_count":0,"area":null},{"id":"2603.09742","title":"Upper Generalization Bounds for Neural Oscillators","created":"2026-03-10","cited_by_count":0,"area":null},{"id":"2603.18706","title":"A mathematical framework for time-delay reservoir computing analysis","created":"2026-03-19","cited_by_count":0,"area":null},{"id":"2604.00473","title":"Phase space integrity in neural network models of Hamiltonian dynamics: A Lagrangian descriptor approach","created":"2026-04-01","cited_by_count":0,"area":null},{"id":"2604.03469","title":"Recurrent Quantum Feature Maps for Reservoir Computing","created":"2026-04-03","cited_by_count":0,"area":null},{"id":"2605.05492","title":"MEMOA: Massive Mixtures of Online Agents via Mean-Field Decentralized Nash Equilibria","created":"2026-05-06","cited_by_count":0,"area":null},{"id":"2605.25221","title":"Data-Specific Hyper-Parameter Design: A Paradigm Shift in Reservoir Computing","created":"2026-05-24","cited_by_count":0,"area":null},{"id":"2606.19984","title":"Kolmogorov-Arnold Reservoir Computing","created":"2026-06-18","cited_by_count":0,"area":null},{"id":"2607.14361","title":"NeuralChaos: Optimal Adapted Approximation of Square Integrable Predictable Processes","created":"2026-07-15","cited_by_count":0,"area":null},{"id":"2607.18552","title":"Quantum Reservoir Computing: Recent Advances and Future Directions","created":"2026-07-20","cited_by_count":0,"area":null},{"id":"2609.13315","title":"Feasibility and Memory Mechanisms of Chern-Simons Context Reservoir Computation","created":"2026-09-10","cited_by_count":0,"area":null},{"id":"2610.04343","title":"A KKL Observer Perspective on Reservoir Computing","created":"2026-10-03","cited_by_count":0,"area":null}],"next_cursor":null}