Differentiable reservoir computing
Authors: Lyudmila Grigoryeva, Juan-Pablo Ortega
JMLR 2019 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This constitutes a novel strong contribution to the long line of research on the ESP and the FMP and, in particular, links to existing research on the input-dependence of the ESP. Differentiability has been shown in the literature to be a key feature in the learning of attractors of chaotic dynamical systems. A Volterra-type series representation for reservoir filters with semi-infinite discrete-time inputs is constructed in the analytic case using Taylor s theorem and corresponding approximation bounds are provided. Finally, it is shown as a corollary of these results that any fading memory filter can be uniformly approximated by a finite Volterra series with finite memory. |
| Researcher Affiliation | Academia | Lyudmila Grigoryeva EMAIL Department of Mathematics and Statistics Graduate School of Decision Sciences Universit at Konstanz Germany. Juan-Pablo Ortega EMAIL Faculty of Mathematics and Statistics Universit at Sankt Gallen Switzerland Centre National de la Recherche Scientifique (CNRS) France |
| Pseudocode | No | The paper contains mathematical derivations, theorems, and proofs, but no structured pseudocode or algorithm blocks are present. |
| Open Source Code | No | The paper does not mention the release of any source code, nor does it provide links to code repositories. |
| Open Datasets | No | The paper is theoretical and focuses on mathematical properties of reservoir computing filters. It does not conduct experiments on specific datasets or provide access information for any open datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical evaluation on datasets, thus no dataset split information is provided. |
| Hardware Specification | No | The paper describes theoretical work and does not detail any experimental setup or specific hardware used for computations. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical derivations; it does not list any specific software dependencies or their version numbers. |
| Experiment Setup | No | The paper presents theoretical analysis and mathematical proofs rather than empirical experiments, therefore, it does not include details on experimental setup or hyperparameters. |