[Lisa_seminaires] [Tea Talk] Petar Veličković (Cambridge / Mila) August 32018 10:30 AA3195

Michael Noukhovitch mnoukhov at gmail.com
Mar 31 Juil 15:58:56 EDT 2018


This week we have *Petar Veličković * from * Cambridge/Mila * giving a talk
on *August 3rd 2018* at *10:30* in room *AA3195*

Will this talk be streamed <https://mila.bluejeans.com/1365483656/webrtc>?
yes

Come to the talk and make sure to pay attention, because this talk should
be great to connect the dots!
Michael

*TITLE* Keeping our graphs attentive

*KEYWORDS *graph structured, attention, computational biology

*ABSTRACT*
A multitude of important real-world datasets (especially in biology) come
together with some form of graph structure: social networks, citation
networks, protein-protein interactions, brain connectome data, etc.
Extending neural networks to be able to properly deal with this kind of
data is therefore a very important direction for machine learning research,
but one that has received comparatively rather low levels of attention
until very recently. Attentional mechanisms represent a very promising
direction for extending the established convolutional operator on images to
work on arbitrary graphs, as they satisfy many of the desirable features
for a convolutional operator. Through this talk, I will focus on my work on
Graph Attention Networks (GATs), where these theoretical properties have
been further validated by solid results on transductive as well as
inductive node classification benchmarks. I will also outline some of the
earlier efforts towards deploying attention-style operators on graph
structures, as well as very exciting recent work that expands on GATs and
deploys them in more general circumstances (such as EAGCN, DeepInf, and
applications to solving the Travelling Salesman Problem). Time permitting,
I will also present some of the relevant related graph-based work in the
computational biology and medical imaging domains that I have been involved
in.

*BIO*
Petar Veličković is currently a final-year PhD student in Machine Learning
and Bioinformatics at the Department of Computer Science and Technology of
the University of Cambridge. He also holds a BA degree in Computer Science
from Cambridge, having completed the Computer Science Tripos in 2015. In
addition, he has been involved in research placements at Nokia Bell Labs
(working with Nicholas Lane) and the Montréal Institute of Learning
Algorithms (working with Adriana Romero and Yoshua Bengio). His current
research interests broadly involve devising neural network architectures
that operate on nontrivially structured data (such as graphs), and their
applications in bioinformatics and medicine. He has published his work in
these areas at both machine learning venues (ICLR, NIPS ML4H, ICML WCB) and
biomedical venues and journals (Bioinformatics, PervasiveHealth)
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