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Bayesian Model Selection, the Marginal Likelihood, and Generalization.

, , , , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 14223-14247. PMLR, (2022)

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Semi-Supervised Learning with Normalizing Flows, , , and . (2019)cite arxiv:1912.13025.Learning Invariances in Neural Networks from Training Data., , , and . NeurIPS, (2020)Evaluating Approximate Inference in Bayesian Deep Learning., , , , , , , , , and . NeurIPS (Competition and Demos), volume 176 of Proceedings of Machine Learning Research, page 113-124. PMLR, (2021)Does Knowledge Distillation Really Work?, , , , and . NeurIPS, page 6906-6919. (2021)Bayesian Model Selection, the Marginal Likelihood, and Generalization., , , , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 14223-14247. PMLR, (2022)Simple and Fast Group Robustness by Automatic Feature Reweighting., , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 28448-28467. PMLR, (2023)Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision., , , , , , , , , and 2 other author(s). CoRR, (2023)FlexiViT: One Model for All Patch Sizes., , , , , , , , , and . CoRR, (2022)There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average., , , and . ICLR (Poster), OpenReview.net, (2019)FlexiViT: One Model for All Patch Sizes., , , , , , , , , and . CVPR, page 14496-14506. IEEE, (2023)