Implementing spatio-temporal GNN in IGNNITION
GNNITIOn is a powerful library that allows the user to generate quick GNNs without needing to learn to use the deep learning libraries. STGNNs (Spati-Temporal GNNs) are a variation of the GNN that takes into account the variability of the data through the time. The goal of this master thesis is to l...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/403258 |
| Acceso en línea: | https://hdl.handle.net/2117/403258 |
| Access Level: | acceso abierto |
| Palabra clave: | Neural networks (Computer science) Neural Network GNN STGNN IGNNITION Xarxes neuronals (Informàtica) Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors |
| Sumario: | GNNITIOn is a powerful library that allows the user to generate quick GNNs without needing to learn to use the deep learning libraries. STGNNs (Spati-Temporal GNNs) are a variation of the GNN that takes into account the variability of the data through the time. The goal of this master thesis is to learn how the STGNNs work by developing one from literature and give IGNNITION the tools to be able to develop STGNNs. |
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