<div dir="ltr"><span style="font-size:12.8px">Hi everyone!</span><div style="font-size:12.8px"><br></div><div><p class="MsoNormal" style="background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><span style="font-size:10pt;line-height:15.3333px;font-family:Arial,sans-serif">This week at the<span class="gmail-m_1601899354938303629gmail-m_-9138486641497174048gmail-m_-2314642548440317959gmail-m_-5235992900627905237gmail-m_-5397734049088859634gmail-m_-8409739297008502631gmail-m_-5223976695426397292gmail-m_-8239203266232787201gmail-m_-3131546366916696156gmail-apple-converted-space"> </span></span><span style="font-size:12.8px">Applied Statistics </span><span style="font-size:12.8px;line-height:14.72px"><font face="Arial, sans-serif"><span style="font-size:10pt">Worksho<wbr>p we will be welcoming</span></font><span class="gmail-m_1601899354938303629gmail-m_-9138486641497174048gmail-m_-2314642548440317959gmail-m_-5235992900627905237gmail-m_-5397734049088859634gmail-m_-8409739297008502631gmail-m_-5223976695426397292gmail-m_-8239203266232787201gmail-m_-3131546366916696156gmail-apple-converted-space" style="font-family:Arial,sans-serif;font-size:10pt"> <b><i>Michael Bronstein</i></b></span><font face="Arial, sans-serif"><span style="font-size:10pt">, Radcliffe Fellow at the Harvard Institute for Advanced Study</span></font></span><font face="Arial, sans-serif" style="font-size:12.8px"><span style="font-size:10pt">. He will be presenting work entitled</span></font><b style="font-size:10pt;font-family:Arial,sans-serif"><i> </i></b><font face="Arial, sans-serif" style="font-size:12.8px"><span style="font-size:13.3333px"><b><i>Deep Learning on Graphs: Going Beyond Euclidean Data</i></b></span><span style="font-size:10pt">.  Please find the abstract below and on the Applied Stats website <a href="https://projects.iq.harvard.edu/applied.stats.workshop-gov3009" target="_blank">here</a>.</span></font></p><p class="MsoNormal" style="font-size:12.8px;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><span style="font-size:10pt;line-height:15.3333px;font-family:Arial,sans-serif"> </span></p><p class="MsoNormal" style="font-size:12.8px;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><span style="font-size:10pt;line-height:15.3333px;font-family:Arial,sans-serif">As usual, we will meet at noon in CGIS Knafel Room 354 and lunch will be provided.  See you all there!<br clear="all"></span></p><div><div class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><br><div>-- Dana Higgins</div></div></div></div>
</div><p style="font-size:12.8px"></p><p class="MsoNormal" style="font-size:12.8px;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><span style="font-size:10pt;line-height:15.3333px;font-family:Arial,sans-serif"> </span></p><p class="MsoNormal" style="font-size:12.8px"><br></p><p class="MsoNormal" style="font-size:12.8px"><span style="line-height:15.3333px"><b style="font-family:Arial,sans-serif;font-size:10pt"><span style="background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial">Title:</span></b><span class="gmail-m_1601899354938303629gmail-m_-9138486641497174048gmail-m_-2314642548440317959gmail-m_-5235992900627905237gmail-m_-5397734049088859634gmail-m_-8409739297008502631gmail-m_-5223976695426397292gmail-m_-8239203266232787201gmail-m_-3131546366916696156gmail-apple-converted-space" style="font-family:Arial,sans-serif;font-size:10pt"><span style="background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"> <u>Deep Learning on Graphs: Going Beyond Euclidean Data</u></span></span></span></p><p class="MsoNormal"><span style="font-size:12.8px;line-height:15.3333px"><br><b style="font-family:Arial,sans-serif;font-size:10pt"><span style="background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial">Abstract:</span></b><span class="gmail-m_1601899354938303629gmail-m_-9138486641497174048gmail-m_-2314642548440317959gmail-m_-5235992900627905237gmail-m_-5397734049088859634gmail-m_-8409739297008502631gmail-m_-5223976695426397292gmail-m_-8239203266232787201gmail-m_-3131546366916696156gmail-apple-converted-space" style="font-family:Arial,sans-serif;font-size:10pt"><span style="background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"> </span></span></span><span style="font-size:12.8px"> </span>In the past decade, deep learning methods have achieved unprecedented performance on a broad range of problems in various fields from computer vision to speech recognition. So far research has mainly focused on developing deep learning methods for Euclidean-structured data. However, many important applications have to deal with non-Euclidean structured data, such as graphs and networks. Such data are becoming increasingly important in computer graphics and 3D vision, sensor networks, drug design, biomedicine, high energy physics, recommendation systems, and web applications. The adoption of deep learning in these fields has been lagging behind until recently, primarily since the non-Euclidean nature of objects dealt with makes the very definition of basic operations used in deep networks rather elusive. In this talk, I will introduce the emerging field of geometric deep learning on graphs, overview existing solutions and applications for this class of problems, and outline the key difficulties and future research directions.<br></p>
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