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Normalizing Flows for Probabilistic Modeling and Inference., , , , and . CoRR, (2019)Information bottleneck through variational glasses., , , , , and . CoRR, (2019)Targeted free energy estimation via learned mappings, , , , , , , and . (2020)cite arxiv:2002.04913Comment: 10 pages, 5 figures.From data to functa: Your data point is a function and you can treat it like one., , , , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 5694-5725. PMLR, (2022)Introduction to Normalizing Flows for Lattice Field Theory, , , , , , , and . (2021)cite arxiv:2101.08176Comment: 38 pages, 5 numbered figures, Jupyter notebook included as ancillary file.Variational Inference for Monte Carlo Objectives., and . ICML, volume 48 of JMLR Workshop and Conference Proceedings, page 2188-2196. JMLR.org, (2016)Equivariant flow-based sampling for lattice gauge theory., , , , , , , and . CoRR, (2020)Neural Communication Systems with Bandwidth-limited Channel., , and . CoRR, (2020)Stochastic variational learning in recurrent spiking networks, and . Frontiers in Computational Neuroscience, (2014)Normalizing Flows for Probabilistic Modeling and Inference., , , , and . J. Mach. Learn. Res., (2021)