Reservoir Computing: Fast Deep Learning for Sequences
Recurrent Neural Networks (RNNs) represent the reference class of Deep Learning models for learning from sequential data. Despite the widespread success, a major downside of RNNs and commonly derived ‘gating’ variants (LSTM, GRU) is given by the high cost of the involved training algorithms. In this context, an increasingly popular alternative is the Reservoir Computing (RC) approach, which enables limiting the training algorithm to operate only on a restricted set of (output) parameters. RC is appealing for several reasons, including the amenability of being implemented in low-powerful edge devices, enabling adaptation and personalization in IoT and cyber-physical systems applications.This webinar will introduce Reservoir Computing from scratch, covering all the fundamental design topics as well as good practices. It is targeted to both researchers and practitioners that are interested in setting up fastly-trained Deep Learning models for sequential data.
About Prof. Claudio Gallicchio
Claudio Gallicchio (University of Pisa, Italy) is an Assistant Professor of Machine Learning at the Department of Computer Science of the University of Pisa, Italy. His research is based on the fusion of concepts from Deep Learning, Recurrent Neural Networks, and Randomized Neural Systems. He is involved in the organization of several events (tutorials, workshops) at major international conferences on ML. Currently, he is the leader of the AI work package of the H2020 TEACHING project.
Claudio in Google Scholar: https://scholar.google.com/citations?user=HmMXdB4AAAAJ&hl=en
The seminar will be held online via Zoom on June 11th at 14.30 CET.
To attend the seminar please send an email to [email protected]