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Challenges and Perspectives in Deep Generative Modeling (Dagstuhl Seminar 23072).

, , , , и . Dagstuhl Reports, 13 (2): 47-70 (февраля 2023)

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A Variational Analysis of Stochastic Gradient Algorithms., , и . ICML, том 48 из JMLR Workshop and Conference Proceedings, стр. 354-363. JMLR.org, (2016)Stochastic Learning on Imbalanced Data: Determinantal Point Processes for Mini-batch Diversification., , и . CoRR, (2017)Advances in Variational Inference., , , и . CoRR, (2017)Dynamic Word Embeddings via Skip-Gram Filtering., и . CoRR, (2017)Variable-Bitrate Neural Compression via Bayesian Arithmetic Coding., , и . CoRR, (2020)Diffusion Probabilistic Modeling for Video Generation., , и . CoRR, (2022)On the Challenges and Opportunities in Generative AI., , , , , , , , , и 15 other автор(ы). CoRR, (2024)Scalable Gaussian Process Variational Autoencoders., , , , , и . AISTATS, том 130 из Proceedings of Machine Learning Research, стр. 3511-3519. PMLR, (2021)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)Structured Stochastic Gradient MCMC., , и . ICML, том 162 из Proceedings of Machine Learning Research, стр. 414-434. PMLR, (2022)