<html xmlns:v="urn:schemas-microsoft-com:vml" xmlns:o="urn:schemas-microsoft-com:office:office" xmlns:w="urn:schemas-microsoft-com:office:word" xmlns:m="http://schemas.microsoft.com/office/2004/12/omml" xmlns="http://www.w3.org/TR/REC-html40">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
<meta name="Generator" content="Microsoft Word 15 (filtered medium)">
<style><!--
/* Font Definitions */
@font-face
{font-family:"Cambria Math";
panose-1:2 4 5 3 5 4 6 3 2 4;}
@font-face
{font-family:Calibri;
panose-1:2 15 5 2 2 2 4 3 2 4;}
/* Style Definitions */
p.MsoNormal, li.MsoNormal, div.MsoNormal
{margin:0cm;
margin-bottom:.0001pt;
font-size:11.0pt;
font-family:"Calibri",sans-serif;
mso-fareast-language:EN-US;}
a:link, span.MsoHyperlink
{mso-style-priority:99;
color:#0563C1;
text-decoration:underline;}
a:visited, span.MsoHyperlinkFollowed
{mso-style-priority:99;
color:#954F72;
text-decoration:underline;}
span.EmailStyle17
{mso-style-type:personal-compose;
font-family:"Calibri",sans-serif;
color:windowtext;}
p.Default, li.Default, div.Default
{mso-style-name:Default;
margin:0cm;
margin-bottom:.0001pt;
text-autospace:none;
font-size:12.0pt;
font-family:"Calibri",sans-serif;
color:black;
mso-fareast-language:EN-US;}
.MsoChpDefault
{mso-style-type:export-only;
font-family:"Calibri",sans-serif;
mso-fareast-language:EN-US;}
@page WordSection1
{size:612.0pt 792.0pt;
margin:72.0pt 72.0pt 72.0pt 72.0pt;}
div.WordSection1
{page:WordSection1;}
--></style><!--[if gte mso 9]><xml>
<o:shapedefaults v:ext="edit" spidmax="1026" />
</xml><![endif]--><!--[if gte mso 9]><xml>
<o:shapelayout v:ext="edit">
<o:idmap v:ext="edit" data="1" />
</o:shapelayout></xml><![endif]-->
</head>
<body lang="en-CH" link="#0563C1" vlink="#954F72">
<div class="WordSection1">
<p class="MsoNormal"><span lang="en-CH" style="font-size:12.0pt">Dear colleagues<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="en-CH" style="font-size:12.0pt"><o:p> </o:p></span></p>
<p class="Default"><span lang="en-CH">We are excited to announce <a href="https://annpr2020.ch/">
ANNPR 2020</a>, the 9th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition, which will be held from September 2nd-4th, 2020, at
</span>Zurich University of Applied Sciences ZHAW<span lang="en-CH"> in Winterthur</span>, Switzerland<span lang="en-CH">.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="en-CH" style="font-size:12.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="en-CH" style="font-size:12.0pt">The workshop will act as a major forum for international researchers and practitioners working in all areas of neural network- and machine learning-based pattern recognition to present and discuss
the latest research, results, and ideas in these areas. ANNPR is the biannual workshop organized by the <a href="http://iapr-tc3.diism.unisi.it/" target="_blank">Technical Committe 3 (IAPR TC3)</a> on Neural Networks & Computational Intelligence of the <a href="http://www.iapr.org/" target="_blank">International
Association for Pattern Recognition (IAPR)</a><a href="http://www.iapr.org/" target="_blank">.</a><o:p></o:p></span></p>
<p class="MsoNormal"><span lang="en-CH" style="font-size:12.0pt"><o:p> </o:p></span></p>
<p class="Default"><span lang="en-CH">Among the previous editions of the workshop were ANNPR 2018 (Siena, Italy), ANNPR 2016 (Ulm, Germany), ANNPR 2014 (Montreal, Canada), ANNPR 2012 (Trento, Italy), ANNPR 2010 (Cairo, Egypt), ANNPR 2008 (Paris, France) and
ANNPR 2006 (Ulm, Germany). <o:p></o:p></span></p>
<p class="Default"><b><span lang="en-CH"><o:p> </o:p></span></b></p>
<p class="Default"><b><span lang="en-CH">Program </span></b><span lang="en-CH"><o:p></o:p></span></p>
<p class="Default"><span lang="en-CH"><o:p> </o:p></span></p>
<p class="Default"><span lang="en-CH">The workshop will consist of keynote talks, several sessions for presentations of accepted papers, and a poster session. Keynote speakers are
<b>Jürgen Schmidhuber </b>(tentative, The Swiss AI Lab IDSIA, Lugano, Switzerland),
<b>Naftali Tishby </b>(Hebrew University of Jerusalem, Israel) and <b>Bernd Feisleben
</b>(University of Marburg, Germany). <o:p></o:p></span></p>
<p class="MsoNormal"><span lang="en-CH" style="font-size:12.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="en-CH" style="font-size:12.0pt">In addition, there will be a dedicated
<b>industry session </b>featuring a sponsored keynote, applied research presentations and industry exhibits / booths / demos, which will provide networking opportunities. The social program consists of a welcome reception, an excursion and a conference dinner.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="en-CH" style="font-size:12.0pt"><o:p> </o:p></span></p>
<p class="Default"><b><span lang="en-CH">Topics <o:p></o:p></span></b></p>
<p class="Default"><span lang="en-CH"><o:p> </o:p></span></p>
<p class="Default"><span lang="en-CH">ANNPR 2020 invites papers that present original work in the areas of
<b>neural networks </b>and <b>machine learning </b>oriented to <b>pattern recognition</b>, focusing on their algorithmic, theoretical, and applied aspects. Topics of interest include, but are not limited to:
<o:p></o:p></span></p>
<p class="Default"><span lang="en-CH"><o:p> </o:p></span></p>
<p class="Default"><span lang="en-CH">Methodological Issues: <o:p></o:p></span></p>
<p class="Default"><span lang="en-CH">– Supervised, semi-supervised, unsupervised and reinforcement learning
<o:p></o:p></span></p>
<p class="Default"><span lang="en-CH">– Deep learning & deep reinforcement learning
<o:p></o:p></span></p>
<p class="Default"><span lang="en-CH">– Feed-forward, recurrent, and convolutional neural networks
<o:p></o:p></span></p>
<p class="Default"><span lang="en-CH">– Generative models <o:p></o:p></span></p>
<p class="Default"><span lang="en-CH">– Interpretability & explainability of neural networks
<o:p></o:p></span></p>
<p class="Default"><span lang="en-CH">– Robustness & generalization of neural networks
<o:p></o:p></span></p>
<p class="Default"><span lang="en-CH">– Meta-learning, Auto-ML <o:p></o:p></span></p>
<p class="Default"><span lang="en-CH"><o:p> </o:p></span></p>
<p class="Default"><span lang="en-CH">Applications to Pattern Recognition: <o:p></o:p></span></p>
<p class="Default"><span lang="en-CH">– Image classification and segmentation <o:p>
</o:p></span></p>
<p class="Default"><span lang="en-CH">– Object detection <o:p></o:p></span></p>
<p class="Default"><span lang="en-CH">– Document analysis, e.g. handwriting recognition
<o:p></o:p></span></p>
<p class="Default"><span lang="en-CH">– Sensor-fusion and multi-modal processing <o:p>
</o:p></span></p>
<p class="Default"><span lang="en-CH">– Biometrics, including speech and speaker recognition and segmentation
<o:p></o:p></span></p>
<p class="Default"><span lang="en-CH">– Data, text, and web mining <o:p></o:p></span></p>
<p class="Default"><span lang="en-CH">– Bioinformatics and medical applications <o:p>
</o:p></span></p>
<p class="Default"><span lang="en-CH">– Industrial applications, e.g. quality control and predictive maintenance
<o:p></o:p></span></p>
<p class="Default"><b><span lang="en-CH"><o:p> </o:p></span></b></p>
<p class="Default"><b><span lang="en-CH">Paper Submission (Deadline: May 1st, 2020)
</span></b><span lang="en-CH"><o:p></o:p></span></p>
<p class="Default"><span lang="en-CH"><o:p> </o:p></span></p>
<p class="Default"><span lang="en-CH">Papers are invited to be submitted via EasyChair. There will be a peer review process before acceptance. The page limit is 12 pages. For instructions, see
</span><span lang="en-CH" style="color:#0562C1"><a href="https://annpr2020.ch/cfp/">https://annpr2020.ch/cfp/</a>.
<o:p></o:p></span></p>
<p class="Default"><span lang="en-CH" style="color:#0562C1"><o:p> </o:p></span></p>
<p class="Default"><span lang="en-CH">Accepted papers will be published as a special volume of
<b>the Springer Lecture Notes in Artificial Intelligence (LNAI)</b> series. For previous editions, see
</span><span lang="en-CH" style="color:#0562C1"><a href="https://link.springer.com/conference/annpr">https://link.springer.com/conference/annpr</a></span><span lang="en-CH">.
<o:p></o:p></span></p>
<p class="Default"><b><span lang="en-CH"><o:p> </o:p></span></b></p>
<p class="Default"><b><span lang="en-CH">Organization </span></b><span lang="en-CH"><o:p></o:p></span></p>
<p class="Default"><span lang="en-CH"><o:p> </o:p></span></p>
<p class="Default"><span lang="en-CH">The workshop is organized by <b>the Institute of Applied Information Technology (InIT)
</b>at the <b>ZHAW School of Engineering</b>. <o:p></o:p></span></p>
<p class="Default"><span lang="en-CH">The program committee is listed at </span><span lang="en-CH" style="color:#0562C1"><a href="https://annpr2020.ch/organisation/">https://annpr2020.ch/organisation/</a>
<o:p></o:p></span></p>
<p class="Default"><span lang="en-CH"><o:p> </o:p></span></p>
<p class="Default"><span lang="en-CH">Chairs: Dr. Frank-Peter Schilling (chair), Prof. Dr. Thilo Stadelmann (co-chair)
<o:p></o:p></span></p>
<p class="Default"><b><span lang="en-CH"><o:p> </o:p></span></b></p>
<p class="Default"><b><span lang="en-CH">Venue </span></b><span lang="en-CH"><o:p></o:p></span></p>
<p class="Default"><b><span lang="en-CH"><o:p> </o:p></span></b></p>
<p class="Default"><b><span lang="en-CH">ZHAW </span></b><span lang="en-CH">is one of the leading universities of applied sciences in Switzerland, with around 13 000 students and host to one of Europe’s first and largest dedicated research centers for Data
Science. ZHAW’s School of Engineering is one of the leading Engineering Faculties in Switzerland. Our 13 institutes and centres guarantee superior-quality education, research and development with an emphasis on the areas of energy, mobility, information and
health. <o:p></o:p></span></p>
<p class="MsoNormal"><b><span lang="en-CH" style="font-size:12.0pt"><o:p> </o:p></span></b></p>
<p class="MsoNormal"><b><span lang="en-CH" style="font-size:12.0pt">Winterthur </span>
</b><span lang="en-CH" style="font-size:12.0pt">is the sixth-largest city of Switzerland with around 110 000 inhabitants. It is located in eastern Switzerland, approximately 20 km from Zurich. The Zurich region is the hub of the digital economy in Switzerland,
hosting companies like Google, NVIDIA, IBM and Facebook, which have a high natural affinity to neural networks and pattern recognition. It is also host to world famous universities such as ETH Zurich and University of Zurich UZH, as well as to several universities
of applied sciences including the ZHAW. The Swiss AI Institute IDSIA in Lugano, as well as the Swiss Data Science Center SDSC by ETH Zurich and EPFL Lausanne complete the Artificial Intelligence and Machine Learning landscape in the region.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="en-CH" style="font-size:12.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="en-CH" style="font-size:12.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="en-CH" style="font-size:12.0pt">Best regards,<o:p></o:p></span></p>
<p class="Default"><span lang="en-CH"><o:p> </o:p></span></p>
<p class="Default"><span lang="en-CH">Dr. Frank-Peter Schilling (chair)<o:p></o:p></span></p>
<p class="Default"><span lang="en-CH">Prof. Dr. Thilo Stadelmann (co-chair) <o:p>
</o:p></span></p>
<p class="MsoNormal"><span lang="en-CH"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="en-CH"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="en-CH">--<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="en-CH">Dr. Frank-Peter Schilling<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="en-CH">ZHAW School of Engineering<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="en-CH">Institute of Applied Information Technology (InIT)<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="en-CH">Obere Kirchgasse 2 / TD O3.07 / Postfach / CH-8401 Winterthur<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="en-CH"><a href="mailto:scik@zhaw.ch"><span style="color:#0563C1">scik@zhaw.ch</span></a> / +41 58 934-6955 /
<a href="http://www.zhaw.ch/en/about-us/person/scik/"><span style="color:#0563C1">www.zhaw.ch/en/about-us/person/scik/</span></a><o:p></o:p></span></p>
<p class="MsoNormal"><span lang="en-CH"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="en-CH"><o:p> </o:p></span></p>
</div>
</body>
</html>