<div dir="ltr">
<div style="font-size:12.8px;text-decoration-style:initial;text-decoration-color:initial"><font face="arial, helvetica, sans-serif"><b>When:</b><span style="font-weight:bold"> </span><span class="gmail-m_-4370858322044388147gmail-m_5098382079273635224gmail-aBn" style="border-bottom:1px dashed rgb(204,204,204)"><span class="gmail-m_-4370858322044388147gmail-m_5098382079273635224gmail-aQJ">Thursday, August 16th at 2:30 pm</span></span></font></div><div style="font-size:12.8px;text-decoration-style:initial;text-decoration-color:initial"><b><span class="gmail-m_-4370858322044388147gmail-m_5098382079273635224gmail-aBn" style="border-bottom:1px dashed rgb(204,204,204)"><span class="gmail-m_-4370858322044388147gmail-m_5098382079273635224gmail-aQJ"><font face="arial, helvetica, sans-serif"><br></font></span></span></b></div><div style="font-size:12.8px;text-decoration-style:initial;text-decoration-color:initial"><font face="arial, helvetica, sans-serif"><b>Where: <span> </span></b><span style="font-weight:bold"> </span><span class="gmail-m_-4370858322044388147gmail-m_5098382079273635224gmail-aBn" style="border-bottom:1px dashed rgb(204,204,204)"><span class="gmail-m_-4370858322044388147gmail-m_5098382079273635224gmail-aQJ">TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526</span></span></font></div><div style="font-size:12.8px;text-decoration-style:initial;text-decoration-color:initial"><b><span class="gmail-m_-4370858322044388147gmail-m_5098382079273635224gmail-aBn" style="border-bottom:1px dashed rgb(204,204,204)"><span class="gmail-m_-4370858322044388147gmail-m_5098382079273635224gmail-aQJ"><font face="arial, helvetica, sans-serif"><br></font></span></span></b></div><div style="font-size:12.8px;text-decoration-style:initial;text-decoration-color:initial"><font face="arial, helvetica, sans-serif"><b>Who: <span> </span></b>Shubhendu Trivedi, TTIC </font></div><div style="font-size:12.8px;text-decoration-style:initial;text-decoration-color:initial"><font face="arial, helvetica, sans-serif"><br></font></div><div style="font-size:12.8px;text-decoration-style:initial;text-decoration-color:initial"><font face="arial, helvetica, sans-serif"><br></font></div><div style="font-size:12.8px;text-decoration-style:initial;text-decoration-color:initial"><div style="background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial"><b>Title: </b>Discriminative Learning of Similarity and Group Equivariant Representations. </div><div style="background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial"><div><br></div><div><b><font face="arial, helvetica, sans-serif">Abstract:</font></b></div><div>One of the most fundamental problems in machine learning is to compare examples: Given a pair of objects we want to return a value which indicates degree of (dis)similarity. Similarity is often task specific, and pre-defined distances can perform poorly, leading to work in metric learning. However, being able to learn a similarity-sensitive distance function also presupposes access to a rich, discriminative representation for the objects at hand. In this talk we present contributions towards both ends. </div><div><br></div><div>In the first part of the thesis talk, assuming good representations for the data, we present a formulation for metric learning that makes a more direct attempt to optimize for the k-NN accuracy as compared to prior work. Our approach considers the choice of k neighbors as a discrete valued latent variable, and casts the metric learning problem as a large margin structured prediction problem. We present experiments comparing to a suite of popular metric learning methods. We also present extensions of this formulation to metric learning for kNN regression, and discriminative learning of Hamming distance. </div><div><br></div><div>In the second part, we consider a situation where we are on a limited computational budget i.e. optimizing over a space of possible metrics would be infeasible, but access to a label aware distance metric is still desirable. We present a simple, and computationally inexpensive approach for estimating a well motivated metric that relies only on gradient estimates, we also discuss theoretical as well as experimental results of using this approach in regression and multiclass settings. </div><div><br></div><div>In the final part, we address representational issues, considering group equivariant neural networks (GCNNs). Equivariance to symmetry transformations is explicitly encoded in GCNNs; a classical CNN being the simplest example. Following recent work by Kondor et. al., we present a SO(3)-equivariant neural network architecture for spherical data, that operates entirely in Fourier space, while using tensor products and the Clebsch-Gordan decomposition as the only source of non-linearity. We report state of the art results, and emphasize the wider applicability of our approach, in that it also provides a formalism for the design of fully Fourier neural networks that are equivariant to the action of any continuous compact group.</div></div><br></div><div style="font-size:12.8px;text-decoration-style:initial;text-decoration-color:initial"><br></div><span style="font-size:12.8px;text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline"><b>Thesis advisor:</b><span> </span>Gregory Shakhnarovich</span><span style="font-size:12.8px;text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline"> </span><div style="font-size:12.8px;text-decoration-style:initial;text-decoration-color:initial"><br></div><div style="font-size:12.8px;text-decoration-style:initial;text-decoration-color:initial"><br></div><br class="gmail-Apple-interchange-newline">
<br><div class="gmail_extra"><br clear="all"><div><div 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"><font face="arial, helvetica, sans-serif">Mary C. Marre</font><div><font face="arial, helvetica, sans-serif">Administrative Assistant</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 523</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>
<br><div class="gmail_quote">On Tue, Aug 14, 2018 at 8:44 AM, Mary Marre <span dir="ltr"><<a href="mailto:mmarre@ttic.edu" target="_blank">mmarre@ttic.edu</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr">
<div style="font-size:12.8px;text-decoration-style:initial;text-decoration-color:initial"><font face="arial, helvetica, sans-serif"><b>When:</b><span style="font-weight:bold"> </span><span class="m_-4370858322044388147gmail-m_5098382079273635224gmail-aBn" style="border-bottom:1px dashed rgb(204,204,204)"><span class="m_-4370858322044388147gmail-m_5098382079273635224gmail-aQJ">Thursday, August 16th at 2:30 pm</span></span></font></div><div style="font-size:12.8px;text-decoration-style:initial;text-decoration-color:initial"><b><span class="m_-4370858322044388147gmail-m_5098382079273635224gmail-aBn" style="border-bottom:1px dashed rgb(204,204,204)"><span class="m_-4370858322044388147gmail-m_5098382079273635224gmail-aQJ"><font face="arial, helvetica, sans-serif"><br></font></span></span></b></div><div style="font-size:12.8px;text-decoration-style:initial;text-decoration-color:initial"><font face="arial, helvetica, sans-serif"><b>Where: <span> </span></b><span style="font-weight:bold"> </span><span class="m_-4370858322044388147gmail-m_5098382079273635224gmail-aBn" style="border-bottom:1px dashed rgb(204,204,204)"><span class="m_-4370858322044388147gmail-m_5098382079273635224gmail-aQJ">TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526</span></span></font></div><div style="font-size:12.8px;text-decoration-style:initial;text-decoration-color:initial"><b><span class="m_-4370858322044388147gmail-m_5098382079273635224gmail-aBn" style="border-bottom:1px dashed rgb(204,204,204)"><span class="m_-4370858322044388147gmail-m_5098382079273635224gmail-aQJ"><font face="arial, helvetica, sans-serif"><br></font></span></span></b></div><div style="font-size:12.8px;text-decoration-style:initial;text-decoration-color:initial"><font face="arial, helvetica, sans-serif"><b>Who: </b>Shubhendu Trivedi, TTIC </font></div><div style="font-size:12.8px;text-decoration-style:initial;text-decoration-color:initial"><font face="arial, helvetica, sans-serif"><br></font></div><div style="font-size:12.8px;text-decoration-style:initial;text-decoration-color:initial"><font face="arial, helvetica, sans-serif"><br></font></div><div style="font-size:12.8px;text-decoration-style:initial;text-decoration-color:initial">
<div style="background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial"><b>Title: </b>Discriminative Learning of Similarity and Group Equivariant Representations. </div><div style="background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial"><div><br></div><div><b><font face="arial, helvetica, sans-serif">Abstract:</font></b></div><div>One of the most fundamental problems in machine learning is to compare examples: Given a pair of objects we want to return a value which indicates degree of (dis)similarity. Similarity is often task specific, and pre-defined distances can perform poorly, leading to work in metric learning. However, being able to learn a similarity-sensitive distance function also presupposes access to a rich, discriminative representation for the objects at hand. In this talk we present contributions towards both ends. </div><div><br></div><div>In the first part of the thesis talk, assuming good representations for the data, we present a formulation for metric learning that makes a more direct attempt to optimize for the k-NN accuracy as compared to prior work. Our approach considers the choice of k neighbors as a discrete valued latent variable, and casts the metric learning problem as a large margin structured prediction problem. We present experiments comparing to a suite of popular metric learning methods. We also present extensions of this formulation to metric learning for kNN regression, and discriminative learning of Hamming distance. </div><div><br></div><div>In the second part, we consider a situation where we are on a limited computational budget i.e. optimizing over a space of possible metrics would be infeasible, but access to a label aware distance metric is still desirable. We present a simple, and computationally inexpensive approach for estimating a well motivated metric that relies only on gradient estimates, we also discuss theoretical as well as experimental results of using this approach in regression and multiclass settings. </div><div><br></div><div>In the final part, we address representational issues, considering group equivariant neural networks (GCNNs). Equivariance to symmetry transformations is explicitly encoded in GCNNs; a classical CNN being the simplest example. Following recent work by Kondor et. al., we present a SO(3)-equivariant neural network architecture for spherical data, that operates entirely in Fourier space, while using tensor products and the Clebsch-Gordan decomposition as the only source of non-linearity. We report state of the art results, and emphasize the wider applicability of our approach, in that it also provides a formalism for the design of fully Fourier neural networks that are equivariant to the action of any continuous compact group.</div></div>
<br></div><div style="font-size:12.8px;text-decoration-style:initial;text-decoration-color:initial"><br></div>
<span style="font-size:12.8px;text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline"><b>Thesis advisor:</b> Gregory Shakhnarovich</span> <div><br></div><div><br></div><div><br></div><div><br></div><div>
<br clear="all"><div><div class="m_-4370858322044388147gmail_signature" data-smartmail="gmail_signature"><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">Administrative Assistant</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 523</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>
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