Engineer friends often ask me: Graph Deep Learning sounds great, but are there any big commercial success stories? Is it being deployed in practical applications? Besides the obvious ones–recommendation systems at Pinterest, Alibaba and Twitter–a slightly nuanced success story is the Transformer architecture, which has taken the NLP industry by storm. Through this post, I want to establish links between Graph Neural Networks (GNNs) and Transformers. I’ll talk about the intuitions behind model architectures in the NLP and GNN communities, make connections using equations and figures, and discuss how we could work together to drive progress.
*NOTE: These videos were recorded in Fall 2015 to update the Neural Nets portion of the class. MIT 6.034 Artificial Intelligence, Fall 2010 View the complete...
J. Zhang, Y. Dong, Y. Wang, J. Tang, и M. Ding. Proceedings of the 28th International Joint Conference on Artificial Intelligence, стр. 4278–4284. AAAI Press, (10.08.2019)
Z. Yang, D. Yang, C. Dyer, X. He, A. Smola, и E. Hovy. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, стр. 1480--1489. San Diego, California, Association for Computational Linguistics, (июня 2016)
Y. Kim, K. Stratos, и D. Kim. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), стр. 643--653. Vancouver, Canada, Association for Computational Linguistics, (июля 2017)
J. Lin, R. Nogueira, и A. Yates. (2020)cite arxiv:2010.06467Comment: Final preproduction version of volume in Synthesis Lectures on Human Language Technologies by Morgan & Claypool.
Q. Le, и T. Mikolov. Proceedings of the 31st International Conference on Machine Learning, том 32 из Proceedings of Machine Learning Research, стр. 1188--1196. Bejing, China, PMLR, (июня 2014)