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...
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In this project, we provide our implementations of CNN [Zeng et al., 2014] and PCNN [Zeng et al.,2015] and their extended version with sentence-level attention scheme [Lin et al., 2016] .
Neuroph is lightweight Java neural network framework to develop common neural network architectures. It contains well designed, open source Java library with small number of basic classes which correspond to basic NN concepts. Also has nice GUI neural network editor to quickly create Java neural network components. It has been released as open source under the LGPL license, and it's FREE for you to use it.
JavaNNS is the successor of SNNS. It is based on its computing kernel, with a newly developed, comfortable graphical user interface written in Java set on top of it. Hence the compatibility with SNNS is achieved, while the platform-independence is increa
J. Zhang, Y. Dong, Y. Wang, J. Tang, und M. Ding. Proceedings of the 28th International Joint Conference on Artificial Intelligence, Seite 4278–4284. AAAI Press, (10.08.2019)
M. Beyer, S. Gesper, A. Guntoro, G. Paya-Vaya, und H. Blume. Proceedings - 2023 IEEE 34th International Conference on Application-Specific Systems, Architectures and Processors, ASAP 2023, Seite 61--68. United States, Institute of Electrical and Electronics Engineers Inc., (2023)Funding Information: This work is supported by the German federal ministry of education and research (BMBF), project ZuSE-KI-AVF (grant no. 16ME0062).; 34th IEEE International Conference on Application-Specific Systems, Architectures and Processors, ASAP 2023 ; Conference date: 19-07-2023 Through 21-07-2023.
Z. Yang, D. Yang, C. Dyer, X. He, A. Smola, und E. Hovy. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Seite 1480--1489. San Diego, California, Association for Computational Linguistics, (Juni 2016)
Y. Kim, K. Stratos, und D. Kim. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Seite 643--653. Vancouver, Canada, Association for Computational Linguistics, (Juli 2017)