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A Large-Scale Study on Regularization and Normalization in GANs., , , , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 3581-3590. PMLR, (2019)Self-Supervised Learning of Video-Induced Visual Invariances., , , , , , and . CoRR, (2019)Precision-Recall Curves Using Information Divergence Frontiers., , , , , and . AISTATS, volume 108 of Proceedings of Machine Learning Research, page 2550-2559. PMLR, (2020)Underspecification Presents Challenges for Credibility in Modern Machine Learning., , , , , , , , , and 30 other author(s). J. Mach. Learn. Res., (2022)Video OWL-ViT: Temporally-consistent open-world localization in video., , , , , , , and . ICCV, page 13756-13765. IEEE, (2023)Self-Supervised Learning of Video-Induced Visual Invariances., , , , , , and . CVPR, page 13803-13812. Computer Vision Foundation / IEEE, (2020)Self-Supervised GANs via Auxiliary Rotation Loss., , , , and . CVPR, page 12154-12163. Computer Vision Foundation / IEEE, (2019)Computational and Statistical Tradeoffs via Data Summarization.. ETH Zurich, Zürich, Switzerland, (2017)base-search.net (ftethz:oai:www.research-collection.ethz.ch:20.500.11850/220255).On Mutual Information Maximization for Representation Learning., , , , and . ICLR, OpenReview.net, (2020)Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations., , , , , and . CoRR, (2018)