Learning with Molecules beyond Graph Neural Networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00347311" target="_blank" >RIV/68407700:21230/20:00347311 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21230/22:00356307

Result on the web

<a href="https://ml4molecules.github.io/papers2020/ML4Molecules_2020_paper_24.pdf" target="_blank" >https://ml4molecules.github.io/papers2020/ML4Molecules_2020_paper_24.pdf</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Learning with Molecules beyond Graph Neural Networks

Original language description

In this paper we demonstrate a deep learning framework which is inherently based in the highly expressive language of relational logic, enabling to, among other things, capture arbitrarily complex graph structures. We show how GNNs and similar models can be easily covered in the framework by specifying the underlying propagation rules in the relational logic. The declarative nature of the used language then allows to easily modify and extend the propagation schemes into complex structures, such as the molecular rings which we choose for a short demonstration in this paper.

Czech name

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Czech description

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Type

O - Miscellaneous

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GJ20-19104Y" target="_blank" >GJ20-19104Y: Generative Relational Models</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů