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Learning Discrete Representations via Information Maximizing Self-Augmented Training.

, , , , and . ICML, volume 70 of Proceedings of Machine Learning Research, page 1558-1567. PMLR, (2017)

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Does Distributionally Robust Supervised Learning Give Robust Classifiers?, , , and . ICML, volume 80 of Proceedings of Machine Learning Research, page 2034-2042. PMLR, (2018)WILDS: A Benchmark of in-the-Wild Distribution Shifts., , , , , , , , , and 6 other author(s). CoRR, (2020)Research on J2EE Teaching Based on Mainstream Open Source Frameworks., and . CIT, page 2014-2017. IEEE Computer Society, (2010)WILDS: A Benchmark of in-the-Wild Distribution Shifts., , , , , , , , , and 13 other author(s). ICML, volume 139 of Proceedings of Machine Learning Research, page 5637-5664. PMLR, (2021)Embedded education for Computer Rank Examination., , , and . ICPADS, page 1-4. IEEE Computer Society, (2007)PyTorch Frame: A Modular Framework for Multi-Modal Tabular Learning., , , , , , , and . CoRR, (2024)Strategies for Pre-training Graph Neural Networks, , , , , , and . (2019)cite arxiv:1905.12265Comment: Accepted as a spotlight to ICLR 2020.Worst-case Redundancy of Optimal Binary AIFV Codes and their Extended Codes., , and . CoRR, (2016)How powerful are graph neural networks?, , , and . arXiv preprint arXiv:1810.00826, (2018)GraphCast: Learning skillful medium-range global weather forecasting, , , , , , , , , and 8 other author(s). (2022)cite arxiv:2212.12794Comment: GraphCast code and trained weights are available at: https://github.com/deepmind/graphcast.