{"total":20,"rows":[{"id":"2110.03303","title":"Universal Approximation Under Constraints is Possible with Transformers","created":"2021-10-07","cited_by_count":32,"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":"2302.13752","title":"A Brief Survey on the Approximation Theory for Sequence Modelling","created":"2023-02-27","cited_by_count":15,"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":"2410.14759","title":"Universal approximation results for neural networks with non-polynomial activation function over non-compact domains","created":"2024-10-18","cited_by_count":7,"area":null},{"id":"2204.11231","title":"Do ReLU Networks Have An Edge When Approximating Compactly-Supported Functions?","created":"2022-04-24","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":"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":"2510.05060","title":"ResCP: Reservoir Conformal Prediction for Time Series Forecasting","created":"2025-10-06","cited_by_count":5,"area":null},{"id":"2404.08717","title":"State-space systems as dynamic generative models","created":"2024-04-12","cited_by_count":3,"area":null},{"id":"2405.20094","title":"Low-dimensional approximations of the conditional law of Volterra processes: a non-positive curvature approach","created":"2024-05-30","cited_by_count":3,"area":null},{"id":"2411.06225","title":"RandNet-Parareal: a time-parallel PDE solver using Random Neural Networks","created":"2024-11-09","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":"2408.07386","title":"Fading memory and the convolution theorem","created":"2024-08-14","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":"2112.06891","title":"Interpretable Design of Reservoir Computing Networks using Realization Theory","created":"2021-12-13","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":"2505.17740","title":"A tensor network approach for chaotic time series prediction","created":"2025-05-23","cited_by_count":0,"area":null}],"next_cursor":null}