2026-06-07T07:48:11Zhttps://riuma.uma.es/rest/oai/requestoai:riuma.uma.es:10630/309232026-02-03T12:32:08Zcom_10630_2254col_10630_37959
RIUMA. Repositorio Institucional de la Universidad de Málaga
author
Atencia-Ruiz, Miguel Alejandro
2024-04-08T06:26:45Z
2024-04-08T06:26:45Z
2024
https://hdl.handle.net/10630/30923
In this expository talk, we will review the Echo State Network (ESN), a recurrent neural network that has achieved good results in time series tasks, such as forecasting, classification, and encoding-decoding. However, the lack of a rigorous mathematical foundation makes difficult their application in a general context. On the one hand, strong theoretical results, such as the Echo State Property and Universal Approximation, are non-constructive and require critical simplifying assumptions. On the other hand, usual heuristics for optimal hyper-parameter selection have turned out to be neither necessary nor sufficient. Some connections of ESN models with ideas from dynamical systems and ergodicity will be exposed, together with recent design proposals, as well as a novel application to time series clustering.
eng
Redes neuronales (Informática)
Sistemas autoorganizativos
Challenges in Reservoir Computing.
conference output
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URL
https://riuma.uma.es/bitstreams/b32f78cf-e70e-4777-83fb-9ddade164697/download
File
MD5
aa8a7960b002a478a67bc1af4b93003a
37850
application/pdf
Abstract_Dauphine_Talk.pdf