2:36 p.m.
Petar Veličković
The Scalable Parallel Computing Lab's *SPCL_Bcast* seminar continues
with *Petar Veličković**of **DeepMind, and University of Cambridge*
presenting on *Capturing Computation with Algorithmic Alignment*.
Everyone is welcome to attend (over Zoom)!
*When:* Thursday, 21st March, 6PM CET
*Where:* Zoom
Join <https://spcl.inf.ethz.ch/Bcast/join>
*Abstract:* What makes a neural network better, or worse, at fitting
certain tasks? This question is arguably at the heart of neural network
architecture design, and it is remarkably hard to answer rigorously.
Over the past few years, there have been a plethora of attempts, using
various facets of advanced mathematics, to answer this question under
various assumptions. One of the most successful directions --
algorithmic alignment -- assumes that the target function, and a
mechanism for computing it, are completely well-defined and known (i.e.
the target is to learn to execute an algorithm). In this setting,
fitting a task is equated to capturing the computations of an algorithm,
inviting analyses from diverse branches of mathematics and computer
science. I will present some of my personal favourite works in
algorithmic alignment, along with their implications for building
intelligent systems of the future.
*Biography:* Petar is a Staff Research Scientist at Google DeepMind, an
Affiliated Lecturer at the University of Cambridge, and an Associate of
Clare Hall, Cambridge. He holds a PhD in Computer Science from the
University of Cambridge (Trinity College), obtained under the
supervision of Pietro Liò. His research concerns geometric deep
learning—devising neural network architectures that respect the
invariances and symmetries in data (a topic he’s co-written a proto-book
about). For his contributions, he is recognized as an ELLIS Scholar in
the Geometric Deep Learning Program. Particularly, he focuses on graph
representation learning and its applications in algorithmic reasoning
(featured in VentureBeat). He is the first author of Graph Attention
Networks—a popular convolutional layer for graphs—and Deep Graph
Infomax—a popular self-supervised learning pipeline for graphs (featured
in ZDNet). His research has been used in substantially improving
travel-time predictions in Google Maps (featured in CNBC, Endgadget,
VentureBeat, CNET, the Verge, and ZDNet), and guiding the intuition of
mathematicians towards new top-tier theorems and conjectures (featured
in Nature, Science, Quanta Magazine, New Scientist, The Independent, Sky
News, The Sunday Times, la Repubblica, and The Conversation).
More details & future talks <https://spcl.inf.ethz.ch/Bcast/>
Scalable Parallel Computing Lab (SPCL)
Department of Computer Science, ETH Zurich
Website <https://spcl.inf.ethz.ch> X(Twitter)
<https://twitter.com/spcl_eth> YouTube <https://www.youtube.com/@spcl>
GitHub <https://github.com/spcl>
