Fwd: RI PhD Thesis Proposal: Sibi Venkatesan

[Artur Dubrawski]

Team,

Tomorrow morning we will have our own Sibi Venkatesan present his thesis
proposal.
Come and join us on zoom to enjoy this highly interesting talk!

Cheers,
Artur


---------- Forwarded message ---------
From: Suzanne Lyons Muth <lyonsmuth at cmu.edu>
Date: Wed, Jun 24, 2020 at 8:55 PM
Subject: RI PhD Thesis Proposal: Sibi Venkatesan
To: ri-people at lists.andrew.cmu.edu <ri-people at lists.andrew.cmu.edu>


Date:  01 July 2020

Time:  9:00 a.m.

Place: *Virtual Presentation*
https://cmu.zoom.us/j/93286511839?pwd=RnBsSUJ5Qk4rSzhTSnQvbzJCUE9xQT09

Type:  Ph.D. Thesis Proposal

Who:  Sibi Venkatesan

Title:  Understanding, Exploiting and Improving Inter-view Relationship


Abstract:
Multi-view machine learning has received substantial attention in various
applications over recent years. 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. 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.

In this proposal, we look at multi-view learning approaches which try to
model these inter-view interactions explicitly. 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. We distinguish between global relationships, which are shared across
all views, and local relationships, which are only shared between a subset
of views 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.

We consider three main aspects of modeling such inter-view
relationships. First,
we look at *understanding* relationships within multi-view data. 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.

Next, we look at *exploiting* 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.

Finally, we discuss *improving* 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.



Thesis Committee Members:

Artur Dubrawski, Chair

Jeff Schneider

Srinivasa Narasimhan

Junier Oliva, University of North Carolina, Chapel Hill


A copy of the thesis document is available at:

www.andrew.cmu.edu/user/sibiv/Thesis_Proposal.pdf

<https://www.dropbox.com/sh/7nftxjkc34y9ff3/AAC2ONqluruEbsKcQguvhCRca?dl=0>
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