Deep-Readout Random Recurrent Neural Networks for Real-World Temporal Data

Author:

Evanusa MatthewORCID,Shrestha Snehesh,Patil Vaishnavi,Fermüller Cornelia,Girvan Michelle,Aloimonos Yiannis

Funder

National Science Foundation

Publisher

Springer Science and Business Media LLC

Subject

General Medicine

Reference67 articles.

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3. Devlin J, Chang M-W, Lee K, Toutanova K. Bert: pre-training of deep bidirectional transformers for language understanding. 2018; arXiv preprint arXiv:1810.04805.

4. Douglas RJ, Martin KA. Recurrent neuronal circuits in the neocortex. Curr Biol. 2007;17(13):R496–500.

5. Lukoševičius M, Jaeger H. Reservoir computing approaches to recurrent neural network training. Comput Sci Rev. 2009;3(3):127–49.

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1. t-ConvESN: Temporal Convolution-Readout for Random Recurrent Neural Networks;Artificial Neural Networks and Machine Learning – ICANN 2023;2023

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