Learning Backward Compatible Embeddings-Reference-Cited by-同舟云学术

Learning Backward Compatible Embeddings

Published:2022-08-14 Issue: Volume: Page:
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Container-title:Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Hu Weihua¹,Bansal Rajas¹,Cao Kaidi¹,Rao Nikhil²,Subbian Karthik²,Leskovec Jure¹

Affiliation:

1. Stanford University, Stanford, CA, USA

2. Amazon, Palo Alto, CA, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3534678.3539194

Reference24 articles.

1. Learning principled bilingual mappings of word embeddings while preserving monolingual invariance

2. Goran Glavas , Robert Litschko , Sebastian Ruder , and Ivan Vulic . 2019. How to (properly) evaluate cross-lingual word embeddings: On strong baselines, comparative analyses, and some misconceptions. arXiv preprint arXiv:1902.00508 ( 2019 ). Goran Glavas, Robert Litschko, Sebastian Ruder, and Ivan Vulic. 2019. How to (properly) evaluate cross-lingual word embeddings: On strong baselines, comparative analyses, and some misconceptions. arXiv preprint arXiv:1902.00508 (2019).

3. Palash Goyal , Nitin Kamra , Xinran He , and Yan Liu . 2018 . Dyngem: Deep embedding method for dynamic graphs. arXiv preprint arXiv:1805.11273 (2018). Palash Goyal, Nitin Kamra, Xinran He, and Yan Liu. 2018. Dyngem: Deep embedding method for dynamic graphs. arXiv preprint arXiv:1805.11273 (2018).

4. node2vec

5. William L Hamilton Rex Ying and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In Advances in Neural Information Processing Systems (NeurIPS). 1025--1035. William L Hamilton Rex Ying and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In Advances in Neural Information Processing Systems (NeurIPS). 1025--1035.

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