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Randomized Automatic Differentiation.

, , , , и . ICLR, OpenReview.net, (2021)

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Bayesian Optimization for Machine Learning. (2014)Learning the Parameters of Determinantal Point Process Kernels., , , и . ICML, том 32 из JMLR Workshop and Conference Proceedings, стр. 1224-1232. JMLR.org, (2014)Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks., и . ICML, том 37 из JMLR Workshop and Conference Proceedings, стр. 1861-1869. JMLR.org, (2015)Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems., , , , , и . AISTATS, том 54 из Proceedings of Machine Learning Research, стр. 914-922. PMLR, (2017)Computational caches., , , , , , и . SYSTOR, стр. 8:1-8:7. ACM, (2013)Firefly Monte Carlo: Exact MCMC with Subsets of Data., и . UAI, стр. 543-552. AUAI Press, (2014)Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters., , , и . NeurIPS, (2020)Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability., , , , , и . NeurIPS, стр. 25502-25515. (2021)On Warm-Starting Neural Network Training., и . NeurIPS, (2020)Learning Composable Energy Surrogates for PDE Order Reduction., , , , и . NeurIPS, (2020)