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Variational inference (VI) is a technique to approximate difficult to compute posteriors by optimization.
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R. Van Den Berg, L. Hasenclever, J. M. Tomczak, and M. Welling, “Sylvester normalizing flows for variational inference,” in 34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018 . Association For Uncertainty in Artificial Intelligence (AUAI), 2018, pp. 393–402
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2022
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L. Kook, L. Herzog, T. Hothorn, O. Dürr, and B. Sick, “Deep and interpretable regression models for ordinal outcomes,” Pattern Recognition , vol. 122, p. 108263, 2022
2022
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2022
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Y. Yao, A. Vehtari, and A. Gelman, “Stacking for non-mixing bayesian computations: The curse and blessing of multimodal posteriors,” Journal of Machine Learning Research , vol. 23, pp. 1–45, 2022
2022
Closest in time.