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How Good is the Bayes Posterior in Deep Neural Networks Really?

, , , , , , , , , и . ICML, том 119 из Proceedings of Machine Learning Research, стр. 10248-10259. PMLR, (2020)

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Rodolphe Jenatton

Rodolphe Charrier

Rodolphe Buda

Rodolphe Gasché

Rodolphe Robert

 

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How Good is the Bayes Posterior in Deep Neural Networks Really?, , , , , , , , , и . (2020)cite arxiv:2002.02405.Proximal Methods for Hierarchical Sparse Coding., , , и . J. Mach. Learn. Res., (2011)Scaling Vision with Sparse Mixture of Experts., , , , , , , и . NeurIPS, стр. 8583-8595. (2021)Pi-DUAL: Using Privileged Information to Distinguish Clean from Noisy Labels., , , , , и . CoRR, (2023)Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning., , , , , , , , , и 14 other автор(ы). CoRR, (2021)The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks., , , , , , , , , и . ICML, том 119 из Proceedings of Machine Learning Research, стр. 9289-9299. PMLR, (2020)Scaling Vision Transformers to 22 Billion Parameters., , , , , , , , , и 32 other автор(ы). CoRR, (2023)Structured Sparsity through Convex Optimization, , , и . Statistical Science, 27 (4): 450--468 (ноября 2012)Transfer and Marginalize: Explaining Away Label Noise with Privileged Information., , , и . ICML, том 162 из Proceedings of Machine Learning Research, стр. 4219-4237. PMLR, (2022)When does Privileged information Explain Away Label Noise?, , , , , , и . ICML, том 202 из Proceedings of Machine Learning Research, стр. 26646-26669. PMLR, (2023)