Potential Energy to Improve Link Prediction With Relational Graph Neural Networks. Colombo, S., Alivanistos, D., & Cochez, M. In Martin, A., Hinkelmann, K., Fill, H., Gerber, A., Lenat, D., Stolle, R., & Harmelen, F. v., editors, Proceedings of the AAAI 2022 Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE 2022), Stanford University, Palo Alto, California, USA, March 21-23, 2022, volume 3121, of CEUR Workshop Proceedings, March, 2022. CEUR-WS.org.
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Paper bibtex 1 download
Paper bibtex 1 download @inproceedings{colombo_potential_2022,
series = {{CEUR} {Workshop} {Proceedings}},
title = {Potential {Energy} to {Improve} {Link} {Prediction} {With} {Relational} {Graph} {Neural} {Networks}},
volume = {3121},
url = {http://ceur-ws.org/Vol-3121/short2.pdf},
booktitle = {Proceedings of the {AAAI} 2022 {Spring} {Symposium} on {Machine} {Learning} and {Knowledge} {Engineering} for {Hybrid} {Intelligence} ({AAAI}-{MAKE} 2022), {Stanford} {University}, {Palo} {Alto}, {California}, {USA}, {March} 21-23, 2022},
publisher = {CEUR-WS.org},
author = {Colombo, Simone and Alivanistos, Dimitrios and Cochez, Michael},
editor = {Martin, Andreas and Hinkelmann, Knut and Fill, Hans-Georg and Gerber, Aurona and Lenat, Doug and Stolle, Reinhard and Harmelen, Frank van},
month = mar,
year = {2022},
}Downloads: 1
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