<div dir="ltr"><div><span style="font-size:14.399999618530273px">Apologies in advance for multiple postings. </span><br></div><div><br style="font-size:14.399999618530273px"><span style="font-size:14.399999618530273px"><b>Call for papers: </b>S</span><span class="gmail-m_-9219601303630572263gmail-il" style="font-size:14.399999618530273px">pecial</span><span style="font-size:14.399999618530273px"> S</span><span class="gmail-m_-9219601303630572263gmail-il" style="font-size:14.399999618530273px">ession</span></div><div><b><span style="font-size:14.399999618530273px">"Statistical Physics of Learning and Inference" at </span><span class="gmail-m_-9219601303630572263gmail-il" style="font-size:14.399999618530273px">ESANN</span><span style="font-size:14.399999618530273px"> 2019</span><br style="font-size:14.399999618530273px"></b><br style="font-size:14.399999618530273px"><span style="font-size:14.399999618530273px">European Symposium on Artificial Neural Networks, Computational Intelligence </span></div><div><span style="font-size:14.399999618530273px">and Machine Learning (</span><span class="gmail-m_-9219601303630572263gmail-il" style="font-size:14.399999618530273px">ESANN</span><span style="font-size:14.399999618530273px">2019). </span><span style="font-size:14.399999618530273px">24-26 April 2019, Bruges, Belgium </span></div><div><a href="http://www.esann.org/" style="font-size:14.399999618530273px" target="_blank">http://www.<span class="gmail-m_-9219601303630572263gmail-il">esann</span>.org</a><br style="font-size:14.399999618530273px"><br style="font-size:14.399999618530273px">Description:<div><div style="font-size:14.399999618530273px">This special session is meant to attract researchers who exploit analogies and concepts <div>from statistical physics in the context of machine learning, inference, optimization, and </div><div>related fields.</div></div><div style="font-size:14.399999618530273px"><div><span style="font-size:13px">The exchange of </span><span style="font-size:13px">ideas between statistical physics and computer science has been </span></div><div><span style="font-size:13px">very fruitful </span><span style="font-size:13px">and is currently gaining momentum again as a consequence of the</span></div><div><span style="font-size:13px">revived </span><span style="font-size:13px">interest in neural networks, </span><span style="font-size:13px">machine learning and inference in general.</span></div></div><div style="font-size:14.399999618530273px"><span style="font-size:13px">Statistical physics methods complement</span><span style="font-size:13px"> </span><span style="font-size:13px">other approaches to the theoretical</span></div><div style="font-size:14.399999618530273px"><span style="font-size:13px">understanding </span><span style="font-size:13px">of machine learning processes and inference in stochasic modeling. </span></div><div style="font-size:14.399999618530273px"><span style="font-size:13px">They facilitate, for instance, </span><span style="font-size:13px">the study of dynamical and equilibrium</span><span style="font-size:13px"> properties of </span></div><div style="font-size:14.399999618530273px"><span style="font-size:13px">randomized training processes </span><span style="font-size:13px">in </span><span style="font-size:13px">model situations. At the same time, the approach</span></div><div style="font-size:14.399999618530273px"><span style="font-size:13px">inspires novel and efficient algorithms and facilitates interdisciplinary applications</span></div><div style="font-size:14.399999618530273px"><span style="font-size:13px">in a variety of scientific and technical disciplines. </span></div><div style="font-size:14.399999618530273px"><span style="font-size:13px">The tools and concepts</span><span style="font-size:13px"> applied in this </span><span style="font-size:13px">context include information theory,</span></div><div style="font-size:14.399999618530273px"><span style="font-size:13px">the mathematical analysis </span><span style="font-size:13px">of stochastic </span><span style="font-size:13px">differential </span><span style="font-size:13px">equati<wbr>ons, </span><span style="font-size:13px">methods </span><span style="font-size:13px">borrowed</span></div><div style="font-size:14.399999618530273px"><span style="font-size:13px">from </span><span style="font-size:13px">the statistical </span><span style="font-size:13px">mechanics </span><span style="font-size:13px">of disorder, </span><span style="font-size:13px">mean field theory, </span><span style="font-size:13px">variational calculus, </span></div><div style="font-size:14.399999618530273px"><span style="font-size:13px">renormalization group and other methods. </span></div><div style="font-size:14.399999618530273px"><span style="font-size:13px"> </span></div><div>Potential topics include, but are not limited to:<div style="font-size:18px"><div><ul><li style="margin-left:15px;font-size:14.399999618530273px"><span style="font-size:small"> Probabilistic inference in, e.g., stochastic dynamical systems and complex networks </span></li><li style="margin-left:15px;font-size:14.399999618530273px"> Learning in Deep Networks and other architectures </li><li style="margin-left:15px;font-size:14.399999618530273px"> Complex optimization problems</li><li style="margin-left:15px;font-size:14.399999618530273px"> Emergent behavior in societies of agents</li><li style="margin-left:15px;font-size:14.399999618530273px"> Transient dynamics and equilibrium phenomena in machine learning</li><li style="margin-left:15px;font-size:14.399999618530273px"> The relation of statistical mechanics with information theory and mathematical statistics</li><li style="margin-left:15px;font-size:14.399999618530273px"><span style="font-size:small"> Applications, for instance in: </span></li></ul><span style="font-size:small"> - </span><span style="font-size:small">systems biology and bioinformatics</span></div><div><span style="font-size:small"> - neuroscience</span></div><div><span style="font-size:small"> - environmental modelling</span></div><div><span style="font-size:small"> - social systems </span></div><div><span style="font-size:small"> - signal processing </span></div></div></div></div><div style="font-size:18px"><span style="font-size:small"> - complex optimization</span></div><br style="font-size:14.399999618530273px"><br style="font-size:14.399999618530273px"><span style="font-size:14.399999618530273px">SUBMISSION:</span><br style="font-size:14.399999618530273px"><span style="font-size:14.399999618530273px">Authors must submit their paper through the </span><span class="gmail-m_-9219601303630572263gmail-il" style="font-size:14.399999618530273px">ESANN</span><span style="font-size:14.399999618530273px"> portal following the instructions </span></div><div><span style="font-size:14.399999618530273px">provided at</span></div><div><span style="font-size:14.399999618530273px"><br></span></div><div><span style="font-size:14.399999618530273px"> </span><a href="https://www.elen.ucl.ac.be/esann/index.php?pg=submission" style="font-size:14.399999618530273px" target="_blank">https://www.elen.ucl.ac.be/<span class="gmail-m_-9219601303630572263gmail-il"><wbr>esann</span>/index.php?pg=submission</a><span style="font-size:14.399999618530273px"> <wbr> </span></div><div><span style="font-size:14.399999618530273px"><br></span></div><div><div><span style="font-size:14.399999618530273px">We encourage authors </span><span style="font-size:14.399999618530273px">to contact the organizers of the session beforehand. </span></div></div><div><span style="font-size:14.399999618530273px">Each paper will undergo a peer reviewing process for its acceptance. </span></div><div><br style="font-size:14.399999618530273px"><span style="font-size:14.399999618530273px">IMPORTANT DATES:</span><br style="font-size:14.399999618530273px"><span style="font-size:14.399999618530273px">Submission of papers: 19 November 2018</span><br style="font-size:14.399999618530273px"><span style="font-size:14.399999618530273px">Notification of acceptance: 31 January 2019</span><br style="font-size:14.399999618530273px"><span class="gmail-m_-9219601303630572263gmail-il" style="font-size:14.399999618530273px">ESANN</span><span style="font-size:14.399999618530273px"> conference: 24 - 26 April 2019</span><br style="font-size:14.399999618530273px"><br style="font-size:14.399999618530273px"><span class="gmail-m_-9219601303630572263gmail-il" style="font-size:14.399999618530273px">SPECIAL</span><span style="font-size:14.399999618530273px"> </span><span class="gmail-m_-9219601303630572263gmail-il" style="font-size:14.399999618530273px">SESSION</span><span style="font-size:14.399999618530273px"> ORGANISERS:</span><br style="font-size:14.399999618530273px">Michael Biehl, University of Groningen, The Netherlands</div><div>Nestor Caticha, University of Sao Paulo, Brazil</div><div>Manfred Opper, Technical University Berlin, Germany</div><div>Thomas Villmann, University of Applied Sciences Mittweida, Germany</div><div><br></div><div><br></div>-- <br><div class="gmail-m_-9219601303630572263gmail_signature"><div dir="ltr"><div><div dir="ltr"><div dir="ltr"><div>------------------------------<wbr>----------------------------</div>
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<div>Prof. Dr. Michael Biehl</div>
<div>Bernoulli Institute for</div>
<div>Mathematics, Computer Science</div><div>and Artificial Intelligence</div>
<div>P.O. Box 407, 9700 AK Groningen</div>
<div>The Netherlands</div>
<div><br>Tel. +31 50 363 3997 <br><br></div>
<div><a href="http://www.cs.rug.nl/~biehl" target="_blank">www.cs.rug.nl/~biehl</a></div>
<div><a href="mailto:m.biehl@rug.nl" target="_blank">m.biehl@rug.nl</a> </div></div></div></div></div></div>
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