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Devise: A deep visual-semantic embedding model

, , , , , , and . Advances in Neural Information Processing Systems, page 2121--2129. (2013)

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Emergent Abilities of Large Language Models., , , , , , , , , and 6 other author(s). Trans. Mach. Learn. Res., (2022)Building Machine Learning Systems that Understand.. SIGMOD Conference, page 1. ACM, (2016)Efficient Neural Architecture Search via Parameter Sharing., , , , and . ICML, volume 80 of Proceedings of Machine Learning Research, page 4092-4101. PMLR, (2018)Designs, Lessons and Advice from Building Large Distributed Systems. LADIS 2009 keynote. Dispon�vel online em http://www.cs.cornell.edu/projects/ladis2009/talks/dean\-keynote\-ladis2009.pdf, (2009)A Hierarchical Model for Device Placement., , , , , and . ICLR (Poster), OpenReview.net, (2018)Efficiently Scaling Transformer Inference., , , , , , , , and . MLSys, mlsys.org, (2023)Devise: A deep visual-semantic embedding model, , , , , , and . Advances in Neural Information Processing Systems, page 2121--2129. (2013)Chip Placement with Deep Reinforcement Learning., , , , , , , , , and 12 other author(s). CoRR, (2020)Brainformers: Trading Simplicity for Efficiency., , , , , , , , , and 5 other author(s). ICML, volume 202 of Proceedings of Machine Learning Research, page 42531-42542. PMLR, (2023)Deep Learning for Solving Important Problems.. WWW, page 1. ACM, (2019)