Graph Representation Learning under Uncertainty

Abstract

This talk will give an overview of our incoming NeurIPS submission. We present a framework for learning to represent graph-structured data under uncertainty and evaluate it on a series of existing and novel tasks. The latter include Cellular Automata and a subset of Cora-full for mixed-class and few-shot learning, whereas the existing data comes from geometric and biological domains.

Date
May 26, 2020 1:15 PM — 2:15 PM
Location
Virtual
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Dr Cătălina Cangea
Senior Research Scientist

Senior Research Scientist at Google DeepMind, with a PhD in ML from the University of Cambridge, and inhaler of music :) Focus on generative music models, finding signals in data and human evaluation. Motivated by contributing ML-based knowledge and improvements to real-world systems!