<div dir="ltr">Team,<div><br></div><div>Tomorrow morning we will have our own Sibi Venkatesan present his thesis proposal.</div><div>Come and join us on zoom to enjoy this highly interesting talk!</div><div><br></div><div>Cheers,</div><div>Artur</div><div><br><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">---------- Forwarded message ---------<br>From: <strong class="gmail_sendername" dir="auto">Suzanne Lyons Muth</strong> <span dir="auto"><<a href="mailto:lyonsmuth@cmu.edu">lyonsmuth@cmu.edu</a>></span><br>Date: Wed, Jun 24, 2020 at 8:55 PM<br>Subject: RI PhD Thesis Proposal: Sibi Venkatesan<br>To: <a href="mailto:ri-people@lists.andrew.cmu.edu">ri-people@lists.andrew.cmu.edu</a> <<a href="mailto:ri-people@lists.andrew.cmu.edu">ri-people@lists.andrew.cmu.edu</a>><br></div><br><br>
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<span style="font-family:"Calibri Light","Helvetica Light",sans-serif,serif,EmojiFont;font-size:11pt"><span style="font-size:11pt;font-family:"Calibri Light","Helvetica Light",sans-serif">Date: 01 July</span><span style="font-size:11pt;font-family:"Calibri Light","Helvetica Light",sans-serif"> 2020</span></span><span style="font-family:Calibri,sans-serif,serif,EmojiFont"></span></p>
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<span style="font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt">Time: 9:00 a.m.</span><span style="font-family:Calibri,sans-serif,serif,EmojiFont"></span></p>
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<span style="font-family:"Calibri Light","Helvetica Light",sans-serif,serif,EmojiFont;font-size:11pt"><span style="font-size:11pt;font-family:"Calibri Light","Helvetica Light",sans-serif">Place: *Virtual Presentation* </span></span><a href="https://cmu.zoom.us/j/93286511839?pwd=RnBsSUJ5Qk4rSzhTSnQvbzJCUE9xQT09" rel="noopener noreferrer" id="m_-6398216867174847601LPlnk100674" style="font-family:wf_segoe-ui_normal,"Segoe UI","Segoe WP",Tahoma,Arial,sans-serif,serif,EmojiFont;font-size:15px" target="_blank"><span style="font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt">https://cmu.zoom.us/j/93286511839?pwd=RnBsSUJ5Qk4rSzhTSnQvbzJCUE9xQT09</span></a></p>
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<span style="font-family:"Calibri Light","Helvetica Light",sans-serif,serif,EmojiFont;font-size:11pt"><span style="font-size:11pt"><span style="font-size:11pt;font-family:"Calibri Light","Helvetica Light",sans-serif">Type: Ph.D. Thesis</span><span style="font-size:11pt;font-family:"Calibri Light","Helvetica Light",sans-serif"> </span></span><span style="font-size:11pt;font-family:"Calibri Light","Helvetica Light",sans-serif">Proposal</span></span><span style="font-family:Calibri,sans-serif,serif,EmojiFont"></span></p>
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<span style="font-size:11pt"><span style="font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt">Who: </span><span style="font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt">Sibi Venkatesan</span></span><font face="Calibri Light, Helvetica Light, sans-serif, serif, EmojiFont"></font></p>
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<span style="font-family:"Calibri Light","Helvetica Light",sans-serif,serif,EmojiFont;font-size:11pt"><span style="font-size:11pt;font-family:"Calibri Light","Helvetica Light",sans-serif">Title:</span><span style="font-size:11pt;font-family:"Calibri Light","Helvetica Light",sans-serif"> </span></span><span style="color:rgb(33,33,33);font-family:"Calibri Light","Helvetica Light",sans-serif,serif,EmojiFont;font-size:11pt"></span><span style="color:rgb(33,33,33);font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt">Understanding,
Exploiting and Improving Inter-view Relationship</span></p>
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<span style="font-size:11pt;font-family:"Calibri Light","Helvetica Light",sans-serif">Abstract</span><font face="Calibri, sans-serif, serif, EmojiFont" size="3"><span style="font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt">:</span></font></p>
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<span style="font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt">Multi-view machine learning has received substantial attention in various applications over recent years. </span><span style="font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt">These
applications typically involve learning on data obtained from multiple sources of information, such as, for example, in multi-sensor systems such as self-driving cars and patient bed-side monitoring. </span><span style="font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt">Learning
models for such applications can often benefit from leveraging not only the information from individual sources, but also the interactions and relationships between these sources.</span><br>
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<span style="font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt">In this proposal, we look at multi-view learning approaches which try to model these inter-view interactions explicitly.
</span><span style="font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt">Here, we define interactions and relationships between views in terms of the information which is shared across these views, i.e. information redundancy between
views. </span><span style="font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt">We distinguish between global relationships, which are shared across all views, and local relationships, which are only shared between a subset of views </span><span style="font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt">For
example, in a multi-camera system, we can think of global relationships to be defined over the part of a scene which is visible to all cameras, while local relationships may exist between a subset of views to be defined by the intersection of the fields of
view of only those cameras.</span><br>
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<span style="font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt">We consider three main aspects of modeling such inter-view relationships. </span><span style="font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt">First,
we look at</span><span style="font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt"> </span><b><span style="font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt">understanding</span></b><span style="font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt"> </span><span style="font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt">relationships
within multi-view data. </span><span style="font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt">We describe two methods which aim to uncover and model local relationships between views: (i) Robust Multi-view Auto-Encoder, which generalizes
the idea of drop-out to views as a whole and (ii) One-vs-Rest Embedding Learning, which explicitly models the local relationships by considering each view separately. We also propose extensions to these methods, as well as alternate approaches to understanding
inter-view relationships.</span><br>
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<span style="font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt">Next, we look at</span><span style="font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt"> </span><b><span style="font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt">exploiting</span></b><span style="font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt"> </span><span style="font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt">this
understanding to solve down-stream tasks and real-world problems. Here, we use our proposed models to tackle real-world problems, and demonstrate the effectiveness of explicitly modeling inter-view relationships. We also discuss how we can extend our approaches
to looking at special applications, such as dynamical systems and asynchronous multi-view data.</span><br>
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<span style="font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt">Finally, we discuss</span><span style="font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt"> </span><b><span style="font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt">improving</span></b><span style="font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt"> inter-view
relationships by facilitating favorable interactions between views in multi-view data. We first show how we can re-interpret individual views as data points, allowing us to apply traditional machine learning approaches to modeling inter-view relationships.
We then describe Scalable Active Search as a candidate approach for view-selection. We also propose additional methods to improve inter-view relationships using our view-as-data-point interpretation, and discuss ways for their online improvement.</span><br>
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<span style="font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt"> </span></p>
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<span style="font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt">Thesis Committee Members:</span><span style="font-family:Calibri,sans-serif,serif,EmojiFont"></span></p>
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<span style="font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt">Artur Dubrawski, Chair</span></p>
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<span style="font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt">Jeff Schneider</span></p>
<p style="margin:0in 0in 0.0001pt;font-size:medium;font-family:Calibri,sans-serif">
<span style="font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt">Srinivasa Narasimhan</span></p>
<p style="margin:0in 0in 0.0001pt"><font face="Calibri Light, Helvetica Light, sans-serif, serif, EmojiFont"><span style="font-size:11pt;font-family:"Calibri Light","Helvetica Light",sans-serif">Junier Oliva, University
of </span><span style="font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt">North</span><span style="font-size:11pt;font-family:"Calibri Light","Helvetica Light",sans-serif"> Carolina, Chapel Hill</span></font></p>
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<span style="font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt">A copy of the thesis document is available at:</span></p>
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<a href="http://www.andrew.cmu.edu/user/sibiv/Thesis_Proposal.pdf" rel="noopener noreferrer" style="font-family:wf_segoe-ui_normal,"Segoe UI","Segoe WP",Tahoma,Arial,sans-serif,serif,EmojiFont;font-size:15px" id="m_-6398216867174847601LPlnk404445" target="_blank"><span style="font-family:"Calibri Light","Helvetica Light",sans-serif;font-size:11pt">www.andrew.cmu.edu/user/sibiv/Thesis_Proposal.pdf</span></a><span style="font-family:"Calibri Light","Helvetica Light",sans-serif,serif,EmojiFont;font-size:11pt"><br>
</span></p>
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<a href="https://www.dropbox.com/sh/7nftxjkc34y9ff3/AAC2ONqluruEbsKcQguvhCRca?dl=0" rel="noopener noreferrer" id="m_-6398216867174847601LPlnk457523" title="https://www.dropbox.com/sh/7nftxjkc34y9ff3/AAC2ONqluruEbsKcQguvhCRca?dl=0
Cmd+Click or tap to follow the link" style="font-family:wf_segoe-ui_normal,"Segoe UI","Segoe WP",Tahoma,Arial,sans-serif,serif,EmojiFont;font-size:15px" target="_blank"><span style="font-family:"Calibri Light","Helvetica Light",sans-serif,serif,EmojiFont;font-size:11pt"></span></a></p>
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