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.
*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...
S. Wang, L. Hu, Y. Wang, X. He, Q. Sheng, M. Orgun, L. Cao, F. Ricci, and P. Yu. (2021)cite arxiv:2105.06339Comment: Accepted by IJCAI 2021 Survey Track, copyright is owned to IJCAI. The first systematic survey on graph learning based recommender systems. arXiv admin note: text overlap with arXiv:2004.11718.
M. Paris, and R. Jäschke. Proceedings of the 14th International Conference on Knowledge Science, Engineering and Management, volume 12816 of Lecture Notes in Artificial Intelligence, page 1--14. Springer, (2021)
M. Dacrema, P. Cremonesi, and D. Jannach. (2019)cite arxiv:1907.06902Comment: Source code available at: https://github.com/MaurizioFD/RecSys2019_DeepLearning_Evaluation.
P. Xia, S. Wu, and B. Van Durme. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), page 7516--7533. Association for Computational Linguistics, (November 2020)