<div dir="ltr"><div dir="ltr"><div class="gmail_default" style="font-size:small"><div class="gmail_default"><p style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;line-height:normal;margin:0px"><font face="arial, sans-serif" color="#000000"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><b>When:</b> </font></font><font style="vertical-align:inherit"><font style="vertical-align:inherit"> Monday, March 8th at<b> 11:10 am CT</b></font></font><br></font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif" color="#000000"> </font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><font style="color:rgb(0,0,0);vertical-align:inherit"><font style="vertical-align:inherit"><b>Where:</b> </font></font><font color="#000000">Zoom Virtual Talk (</font><font color="#0000ff"><b><a href="https://uchicagogroup.zoom.us/webinar/register/WN_Yxm8COaqRmigedIoz6CZuQ" target="_blank">register in advance here</a></b></font><font color="#000000">)</font></font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif" color="#000000"> </font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif" color="#000000"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><b>Who: </b> </font></font></font>Yi Zhang, Princeton University</p><font color="#000000"><br></font></div><div class="gmail_default"><font color="#000000"><br></font></div><div class="gmail_default"><font face="arial, sans-serif"><span style="color:rgb(60,64,67);letter-spacing:0.2px;white-space:pre-wrap"><b>Title: </b>Advancing Deep Learning by Integrating Theory and Empirics
<b>Abstract: </b>In sharp contrast to its remarkable empirical success stands a mathematical understanding of deep learning that is still in its infancy. Various puzzling behaviors of deep neural nets remain unexplained, and many widely deployed deep learning systems lack theoretical guarantees. This talk offers a perspective on the resemblance between deep learning research and natural sciences, especially modern physics at its formative stage where reciprocal interactions between theoretical and experimental studies fueled the growth. As the central object of study, deep neural networks are to deep learning as unknown particles are to physics. We can make significant progress by building theories out of inspiring empirical observations and verifying hypotheses with carefully designed experiments.</span></font></div><div class="gmail_default"><font face="arial, sans-serif"><span style="color:rgb(60,64,67);letter-spacing:0.2px;white-space:pre-wrap"><br></span></font></div><div class="gmail_default"><font face="arial, sans-serif"><span style="color:rgb(60,64,67);letter-spacing:0.2px;white-space:pre-wrap">In this talk, I will present several of my representative works that follow this research philosophy. Firstly, I will introduce a finite-sample analysis of Generative Adversarial Networks that predicts the existence of degenerate solutions (i.e. mode collapse), which we confirm empirically using a principled test. Then I will present how the empirically identified 'noise stability' of deep neural networks trained on real-life data leads to a substantially stronger generalization measure for deep learning. Finally, I will describe our recent work on designing a simple test for measuring how much deep learning has overfitted to standard datasets.
<b>BIO: </b>Yi Zhang is a Ph.D. Candidate at Princeton University where he is advised by Sanjeev Arora. His research interests are broadly in machine learning, with a focus on understanding the empirical success of deep learning from a theoretical perspective. He is the recipient of the Wallace Memorial Fellowship in Engineering (an Honorific Fellowship Award) from Princeton University. </span><font color="#000000"><br></font></font></div><div class="gmail_default"><span style="color:rgb(60,64,67);letter-spacing:0.2px;white-space:pre-wrap"><font face="arial, sans-serif"><br></font></span></div><div class="gmail_default"><span style="color:rgb(60,64,67);letter-spacing:0.2px;white-space:pre-wrap"><font face="arial, sans-serif"><br></font></span></div><div class="gmail_default"><span style="color:rgb(60,64,67);letter-spacing:0.2px;white-space:pre-wrap"><font face="arial, sans-serif"><b>Host:</b> <a href="mailto:mcallester@ttic.edu" target="_blank">David McAllester</a></font></span></div><div class="gmail_default"><span style="color:rgb(60,64,67);letter-spacing:0.2px;white-space:pre-wrap"><br></span></div><div class="gmail_default"><span style="color:rgb(60,64,67);letter-spacing:0.2px;white-space:pre-wrap"><br></span></div><div class="gmail_default"><span style="color:rgb(60,64,67);letter-spacing:0.2px;white-space:pre-wrap"><br></span></div><div class="gmail_default"><span style="color:rgb(60,64,67);letter-spacing:0.2px;white-space:pre-wrap"><br></span></div><div class="gmail_default"><span style="color:rgb(60,64,67);letter-spacing:0.2px;white-space:pre-wrap"><br></span></div></div><div><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><font face="arial, helvetica, sans-serif">Mary C. Marre</font><div><font face="arial, helvetica, sans-serif">Faculty Administrative Support</font></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6"><b>Toyota Technological Institute</b></font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6">6045 S. Kenwood Avenue</font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6">Room 517</font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6">Chicago, IL 60637</font></i></div><div><i><font face="arial, helvetica, sans-serif">p:(773) 834-1757</font></i></div><div><i><font face="arial, helvetica, sans-serif">f: (773) 357-6970</font></i></div><div><b><i><a href="mailto:mmarre@ttic.edu" target="_blank"><font face="arial, helvetica, sans-serif">mmarre@ttic.edu</font></a></i></b></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div><br></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Wed, Mar 3, 2021 at 4:31 PM Mary Marre <<a href="mailto:mmarre@ttic.edu">mmarre@ttic.edu</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div style="font-size:small"><p style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;line-height:normal;margin:0px"><font face="arial, sans-serif" color="#000000"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><b>When:</b> </font></font><font style="vertical-align:inherit"><font style="vertical-align:inherit"> Monday, March 8th at<b> 11:10 am CT</b></font></font><br></font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif" color="#000000"> </font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><font style="color:rgb(0,0,0);vertical-align:inherit"><font style="vertical-align:inherit"><b>Where:</b> </font></font><font color="#000000">Zoom Virtual Talk (</font><font color="#0000ff"><b><a href="https://uchicagogroup.zoom.us/webinar/register/WN_Yxm8COaqRmigedIoz6CZuQ" target="_blank">register in advance here</a></b></font><font color="#000000">)</font></font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif" color="#000000"> </font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif" color="#000000"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><b>Who: </b> </font></font></font>Yi Zhang, Princeton University</p><font color="#000000"><br></font></div><div style="font-size:small"><font color="#000000"><br></font></div><div><font face="arial, sans-serif"><span style="color:rgb(60,64,67);letter-spacing:0.2px;white-space:pre-wrap"><b>Title: </b>Advancing Deep Learning by Integrating Theory and Empirics
<b>Abstract: </b>In sharp contrast to its remarkable empirical success stands a mathematical understanding of deep learning that is still in its infancy. Various puzzling behaviors of deep neural nets remain unexplained, and many widely deployed deep learning systems lack theoretical guarantees. This talk offers a perspective on the resemblance between deep learning research and natural sciences, especially modern physics at its formative stage where reciprocal interactions between theoretical and experimental studies fueled the growth. As the central object of study, deep neural networks are to deep learning as unknown particles are to physics. We can make significant progress by building theories out of inspiring empirical observations and verifying hypotheses with carefully designed experiments.</span></font></div><div><font face="arial, sans-serif"><span style="color:rgb(60,64,67);letter-spacing:0.2px;white-space:pre-wrap"><br></span></font></div><div><font face="arial, sans-serif"><span style="color:rgb(60,64,67);letter-spacing:0.2px;white-space:pre-wrap">In this talk, I will present several of my representative works that follow this research philosophy. Firstly, I will introduce a finite-sample analysis of Generative Adversarial Networks that predicts the existence of degenerate solutions (i.e. mode collapse), which we confirm empirically using a principled test. Then I will present how the empirically identified 'noise stability' of deep neural networks trained on real-life data leads to a substantially stronger generalization measure for deep learning. Finally, I will describe our recent work on designing a simple test for measuring how much deep learning has overfitted to standard datasets.
<b>BIO: </b>Yi Zhang is a Ph.D. Candidate at Princeton University where he is advised by Sanjeev Arora. His research interests are broadly in machine learning, with a focus on understanding the empirical success of deep learning from a theoretical perspective. He is the recipient of the Wallace Memorial Fellowship in Engineering (an Honorific Fellowship Award) from Princeton University. </span><font color="#000000"><br></font></font></div><div><span style="color:rgb(60,64,67);letter-spacing:0.2px;white-space:pre-wrap"><font face="arial, sans-serif"><br></font></span></div><div><span style="color:rgb(60,64,67);letter-spacing:0.2px;white-space:pre-wrap"><font face="arial, sans-serif"><br></font></span></div><div><span style="color:rgb(60,64,67);letter-spacing:0.2px;white-space:pre-wrap"><font face="arial, sans-serif"><b>Host:</b> <a href="mailto:mcallester@ttic.edu" target="_blank">David McAllester</a></font></span></div><div style="font-size:small"><br></div><div style="font-size:small"><br></div><div style="font-size:small"><br></div><div style="font-size:small"><br></div><div><div dir="ltr"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><font face="arial, helvetica, sans-serif">Mary C. Marre</font><div><font face="arial, helvetica, sans-serif">Faculty Administrative Support</font></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6"><b>Toyota Technological Institute</b></font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6">6045 S. Kenwood Avenue</font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6">Room 517</font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6">Chicago, IL 60637</font></i></div><div><i><font face="arial, helvetica, sans-serif">p:(773) 834-1757</font></i></div><div><i><font face="arial, helvetica, sans-serif">f: (773) 357-6970</font></i></div><div><b><i><a href="mailto:mmarre@ttic.edu" target="_blank"><font face="arial, helvetica, sans-serif">mmarre@ttic.edu</font></a></i></b></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>
</blockquote></div></div>