Workshop to create a sensor application over a WiFi Mesh network - GitHub - binnes/WiFiMeshRaspberryPi: Workshop to create a sensor application over a WiFi Mesh network
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.
J. Zhang, Y. Dong, Y. Wang, J. Tang, and M. Ding. Proceedings of the 28th International Joint Conference on Artificial Intelligence, page 4278–4284. AAAI Press, (Aug 10, 2019)
Z. Yang, D. Yang, C. Dyer, X. He, A. Smola, and E. Hovy. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, page 1480--1489. San Diego, California, Association for Computational Linguistics, (June 2016)
Y. Kim, K. Stratos, and D. Kim. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), page 643--653. Vancouver, Canada, Association for Computational Linguistics, (July 2017)
J. Lin, R. Nogueira, and A. Yates. (2020)cite arxiv:2010.06467Comment: Final preproduction version of volume in Synthesis Lectures on Human Language Technologies by Morgan & Claypool.
Q. Le, and T. Mikolov. Proceedings of the 31st International Conference on Machine Learning, volume 32 of Proceedings of Machine Learning Research, page 1188--1196. Bejing, China, PMLR, (June 2014)