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Graph Representation Learning under Uncertainty (WIP)

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If you have a question about this talk, please contact Mateja Jamnik.


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.

This talk is part of the Artificial Intelligence Research Group Talks (Computer Laboratory) series.

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