<html><head><meta http-equiv="Content-Type" content="text/html; charset=utf-8"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class=""><div class="">The SMILES (Sensorimotor Interaction, Language and Embodiment of Symbols) Workshop will take place both on site and virtually at the ICDL 2022 (International Conference on Developmental Learning).</div><div class=""><br class=""></div><div class="">* Call for abstracts :</div><div class="">- Deadline: July 18th</div><div class="">- Abstracts call: from 1/2 page to 2 pages (onsite and virtual participation are possible)</div><div class="">- Abstract format: same as ICDL conference <a href="https://www.ieee.org/conferences/publishing/templates.html" class="">https://www.ieee.org/conferences/publishing/templates.html</a></div><div class="">- Submissions: <a href="mailto:smiles.conf@gmail.com" class="">smiles.conf@gmail.com</a> + indicate if you will be onsite or online</div><div class="">- Workshop dates: September 12, 2022</div><div class="">- Venue onsite: Queen Mary University of London, UK.</div><div class="">- Venue online: via Zoom and Discord group.</div><div class=""><br class=""></div><div class="">Accepted abstract will be asked to make a short video or poster for the workshop.</div><div class=""><br class=""></div><div class="">* Workshop Short Description</div><div class="">On the one hand, models of sensorimotor interaction are embodied in the environment and in the interaction with other agents. On the other hand, recent Deep Learning development of Natural Language Processing (NLP) models allow to capture increasing language complexity (e.g. compositional representations, word embedding, long term dependencies). However, those NLP models are disembodied in the sense that they are learned from static datasets of text or speech. How can we bridge the gap from low-level sensorimotor interaction to high-level compositional symbolic communication? The SMILES workshop will address this issue through an interdisciplinary approach involving researchers from (but not limited to):</div><div class="">- Sensori-motor learning,</div><div class=""><span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);" class="">- Symbol grounding and symbol emergence,</span></div><div class="">- Emergent communication in multi-agent systems,</div><div class="">- Chunking of perceptuo-motor gestures (gestures in a general sense: motor, vocal, ...),</div><div class="">- Compositional representations for communication and action sequence,</div><div class="">- Hierarchical representations of temporal information,</div><div class="">- Language processing and language acquisition in brains and machines,</div><div class="">- Models of animal communication,</div><div class="">- Understanding composition and temporal processing in neural network models, and</div><div class="">- Enaction, active perception, perception-action loop.</div><div class=""><br class=""></div><div class="">* More info</div><div class="">- contact: <a href="mailto:smiles.conf@gmail.com" class="">smiles.conf@gmail.com</a></div><div class="">- organizers: Xavier Hinaut, Clément Moulin-Frier, Silvia Pagliarini, Joni Zhong, Michael Spranger, Tadahiro Taniguchi, Anne Warlaumont.</div><div class="">- invited speakers (coming soon)</div><div class="">- workshop website (updated regularly): <a href="https://sites.google.com/view/smiles-workshop/" class="">https://sites.google.com/view/smiles-workshop/</a></div><div class="">- ICDL conference website: <a href="https://icdl2022.qmul.ac.uk/" class="">https://icdl2022.qmul.ac.uk/</a></div><div class=""><br class=""></div><div class=""><br class=""></div><div class="">
<div dir="auto" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); letter-spacing: normal; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration: none; word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class=""><div dir="auto" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); letter-spacing: normal; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration: none; word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class=""><div dir="auto" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); letter-spacing: normal; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration: none; word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class=""><div dir="auto" style="text-align: start; text-indent: 0px; word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class=""><div dir="auto" style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class=""><div style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); letter-spacing: normal; text-transform: none; white-space: normal; word-spacing: 0px; text-decoration: none; -webkit-text-stroke-width: 0px; font-family: Helvetica; font-size: 12px; font-style: normal; font-variant-caps: normal; font-weight: normal; text-align: start; text-indent: 0px;">Xavier Hinaut<br class="">Inria Research Scientist</div><div style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); letter-spacing: normal; text-transform: none; white-space: normal; word-spacing: 0px; text-decoration: none; -webkit-text-stroke-width: 0px; font-family: Helvetica; font-size: 12px; font-style: normal; font-variant-caps: normal; font-weight: normal; text-align: start; text-indent: 0px;"><a href="http://www.xavierhinaut.com" class="">www.xavierhinaut.com</a> -- +33 5 33 51 48 01<br class="">Mnemosyne team, Inria, Bordeaux, France -- <a href="https://team.inria.fr/mnemosyne" class="">https://team.inria.fr/mnemosyne</a></div><div style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); letter-spacing: normal; text-transform: none; white-space: normal; word-spacing: 0px; text-decoration: none; -webkit-text-stroke-width: 0px; font-family: Helvetica; font-size: 12px; font-style: normal; font-variant-caps: normal; font-weight: normal; text-align: start; text-indent: 0px;">& LaBRI, Bordeaux University -- <a href="https://www4.labri.fr/en/formal-methods-and-models" class="">https://www4.labri.fr/en/formal-methods-and-models</a></div><div style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); letter-spacing: normal; text-transform: none; white-space: normal; word-spacing: 0px; text-decoration: none; -webkit-text-stroke-width: 0px; font-family: Helvetica; font-size: 12px; font-style: normal; font-variant-caps: normal; font-weight: normal; text-align: start; text-indent: 0px;">& IMN (Neurodegeneratives Diseases Institute) --<span class="Apple-converted-space"> </span><a href="http://www.imn-bordeaux.org/en" class="">http://www.imn-bordeaux.org/en</a></div><div style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); letter-spacing: normal; text-transform: none; white-space: normal; word-spacing: 0px; text-decoration: none; -webkit-text-stroke-width: 0px; font-family: Helvetica; font-size: 12px; font-style: normal; font-variant-caps: normal; font-weight: normal; text-align: start; text-indent: 0px;">---<br class="">Our new release of Reservoir Computing library: <a href="https://github.com/reservoirpy/reservoirpy" class="">https://github.com/reservoirpy/reservoirpy</a></div><div style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); letter-spacing: normal; text-transform: none; white-space: normal; word-spacing: 0px; text-decoration: none; -webkit-text-stroke-width: 0px; font-family: Helvetica; font-size: 12px; font-style: normal; font-variant-caps: normal; font-weight: normal; text-align: start; text-indent: 0px;"><br class=""></div><div style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); letter-spacing: normal; text-transform: none; white-space: normal; word-spacing: 0px; 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