{"id":1081,"date":"2018-02-13T08:55:51","date_gmt":"2018-02-13T08:55:51","guid":{"rendered":"https:\/\/e-nns.org\/icann2018\/?page_id=1081"},"modified":"2018-02-13T08:55:51","modified_gmt":"2018-02-13T08:55:51","slug":"workshops","status":"publish","type":"page","link":"https:\/\/e-nns.org\/icann2020\/conference-programme\/workshops\/","title":{"rendered":"Workshops"},"content":{"rendered":"\n<h4 class=\"wp-block-heading\">W1. 2nd International Workshop on Reservoir Computing<br>(RC 2020)<\/h4>\n\n\n\n<p><a rel=\"noreferrer noopener\" aria-label=\" (opens in a new tab)\" href=\"https:\/\/sites.google.com\/view\/reservoircomputing2020\/\" target=\"_blank\">Workshop homepage<\/a><\/p>\n\n\n\n<p><strong>Description<\/strong>: Reservoir Computing (RC) denotes a class of recurrent neural models  whose dynamics are left unadapted after initialization. The approach is  appealing for several reasons, among which fast training, neuromorphic  hardware implementations, and a natural propensity to edge computing.<br>In the wake of the success of the first edition, the 2nd International  Workshop on Reservoir Computing (RC 2020) intends to bring together  researchers to discuss the state-of-the-art and open challenges in the  field of RC, in all its declinations. These include, among the others,  new models of Echo State Networks and Liquid State Machines,  non-conventional hardware (e.g., photonic) implementations of RC  systems, applications to problems of AI size with human-level  performance, emerging paradigms (e.g., conceptors), RC for structured  data, deep RC, hybrid RC\/fully trained RNN models, and many more. The  workshop provides an open forum for researchers to meet and present  recent contributions and ideas in a fervid and highly interdisciplinary  environment. Industrial contributions are welcome. <br>Potential topics of interest for the workshop include (without being limited to) the following:<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>Echo State Networks, Liquid State Machines<\/li><li>Neuromorphic computing and Non-conventional hardware implementations of RC<\/li><li>Theoretical analysis of Reservoir Computing<\/li><li>Hybrids of fully-trained and RC models<\/li><li>Deep Reservoir Computing<\/li><li>Reservoir Computing for structured data (trees, graphs, networks, \u2026)<\/li><li>Conceptors<\/li><li>Time-delay Reservoir Computing<\/li><li>Reservoir Computing in Neuroscience<\/li><li>Reservoir Computing for AI applications (e.g., vision, natural language processing, etc.)<\/li><li>Statistical Learning Theory of Reservoir Computing networks<\/li><li>Ensemble learning and Reservoir Computing<\/li><li>Reservoir dimensionality reduction, efficient reservoir hyper-parameter search and learning <\/li><\/ul>\n\n\n\n<p><strong>Organizers<\/strong><br>Claudio Gallicchio (University of Pisa, Italy) <br>Alessio Micheli (University of Pisa, Italy) <br>Simone Scardapane (La Sapienza University of Rome, Italy) <br>Miguel C. Soriano (University of the Balearic Islands, Spain) <br>Gouhei Tanaka (The University of Tokyo, Japan) <\/p>\n","protected":false},"excerpt":{"rendered":"<p>W1. 2nd International Workshop on Reservoir Computing(RC 2020) Workshop homepage Description: Reservoir Computing (RC) denotes a class of recurrent neural models whose dynamics are left unadapted after initialization. The approach is appealing for several reasons, among which fast training, neuromorphic hardware implementations, and a natural propensity to edge computing.In the wake of the success of &hellip; <a href=\"https:\/\/e-nns.org\/icann2020\/conference-programme\/workshops\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Workshops&#8221;<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":68,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1081","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/e-nns.org\/icann2020\/wp-json\/wp\/v2\/pages\/1081","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/e-nns.org\/icann2020\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/e-nns.org\/icann2020\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/e-nns.org\/icann2020\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/e-nns.org\/icann2020\/wp-json\/wp\/v2\/comments?post=1081"}],"version-history":[{"count":0,"href":"https:\/\/e-nns.org\/icann2020\/wp-json\/wp\/v2\/pages\/1081\/revisions"}],"up":[{"embeddable":true,"href":"https:\/\/e-nns.org\/icann2020\/wp-json\/wp\/v2\/pages\/68"}],"wp:attachment":[{"href":"https:\/\/e-nns.org\/icann2020\/wp-json\/wp\/v2\/media?parent=1081"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}