In this work, we present HEAT, a neural model capable of representing typed and qualified hypergraphs, where each hyperedge explicitly qualifies how ...
Jan 28, 2022 · We present HEAT, a neural model capable of representing typed and qualified hypergraphs, where each hyperedge explicitly qualifies how participating nodes ...
Sep 13, 2022 · HEAT adopts the transformer arch to compute node and edge representations while modifying the multi-head attention based on the graph structure.
Sep 5, 2022 · In this work, we present HEAT, a neural model capable of representing typed and qualified hypergraphs, where each hyperedge explicitly qualifies ...
This work presents HEAT, a neural model capable of representing typed and qualified hypergraphs, where each hyperedge explicitly qualifies how participating ...
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We present HEAT, a neural model capable of representing typed and qualified hypergraphs, where each hyperedge explicitly qualifies how participating nodes ...
Sep 14, 2022 · HEAT: Hyperedge Attention Networks. Learning from structured data is a core machine learning task. Commonly, such data is represented as ...
Our research aims to teach machines to understand complex algorithms, combining methods from the programming languages and the machine learning communities.
HEAT: Hyperedge Attention Networks · no code implementations • 28 Jan 2022 • Dobrik Georgiev, Marc Brockschmidt, Miltiadis Allamanis. Learning from structured ...
Hypergraph convolution defines a basic convolutional operator in a hypergraph. It enables an efficient information propagation between vertices.