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Celeste: Variational inference for a generative model of astronomical images.

, , , , , , , и . ICML, том 37 из JMLR Workshop and Conference Proceedings, стр. 2095-2103. JMLR.org, (2015)

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Klaus Regier

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Celeste: Variational inference for a generative model of astronomical images., , , , , , , и . ICML, том 37 из JMLR Workshop and Conference Proceedings, стр. 2095-2103. JMLR.org, (2015)A Gaussian Process Model of Quasar Spectral Energy Distributions., , , , , , , и . NIPS, стр. 2494-2502. (2015)Stochastic Cubic Regularization for Fast Nonconvex Optimization., , , , и . NeurIPS, стр. 2904-2913. (2018)Information Constraints on Auto-Encoding Variational Bayes., , , и . NeurIPS, стр. 6117-6128. (2018)Sequential Monte Carlo for Inclusive KL Minimization in Amortized Variational Inference., , и . AISTATS, том 238 из Proceedings of Machine Learning Research, стр. 4312-4320. PMLR, (2024)Variational Inference with Coverage Guarantees in Simulation-Based Inference., , , , и . ICML, OpenReview.net, (2024)A deep generative model for gene expression profiles from single-cell RNA sequencing., , , и . CoRR, (2017)Deep generative modeling for single-cell transcriptomics, , , , и . Nature Methods, 15 (12): 1053--1058 (2018)Fast Black-box Variational Inference through Stochastic Trust-Region Optimization., , и . NIPS, стр. 2402-2411. (2017)Variational Inference for Deblending Crowded Starfields., , , и . J. Mach. Learn. Res., (2023)