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Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data., , , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 3165-3176. PMLR, (2020)Semi-Supervised Learning with Normalizing Flows., , , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 4615-4630. PMLR, (2020)Large Language Models Are Zero-Shot Time Series Forecasters., , , and . CoRR, (2023)Residual Pathway Priors for Soft Equivariance Constraints., , and . NeurIPS, page 30037-30049. (2021)PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization., , , , , and . NeurIPS, (2022)Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints., , and . NeurIPS, (2020)Learning Invariances in Neural Networks from Training Data., , , and . NeurIPS, (2020)Semi-Supervised Learning with Normalizing Flows, , , and . (2019)cite arxiv:1912.13025.There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average., , , and . ICLR (Poster), OpenReview.net, (2019)A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups., , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 3318-3328. PMLR, (2021)