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Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling

, , , и . arXiv preprint arXiv:2110.06021, (2021)

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Colin Davenport

Genomic and metagenomic application of microbial genome signatures. MedH Hannover, (2009)

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Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling, , , и . arXiv preprint arXiv:2110.06021, (2021)Simple, Distributed, and Accelerated Probabilistic Programming., , , , , и . NeurIPS, стр. 7609-7620. (2018)Effect Handling for Composable Program Transformations in Edward2., и . CoRR, (2018)Meta-Learning MCMC Proposals., , , и . NeurIPS, стр. 4150-4160. (2018)tfp.mcmc: Modern Markov Chain Monte Carlo Tools Built for Modern Hardware., , , , , , , , , и . CoRR, (2020)Automatic Reparameterisation of Probabilistic Programs., , и . ICML, том 119 из Proceedings of Machine Learning Research, стр. 3648-3657. PMLR, (2020)Automatic structured variational inference., , , , , , и . AISTATS, том 130 из Proceedings of Machine Learning Research, стр. 676-684. PMLR, (2021)Training novel phonemic contrasts: a comparison of identification and oddity discrimination training., , и . SLaTE, стр. 121-124. ISCA, (2009)Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling., , , и . ICLR, OpenReview.net, (2022)Simple, Distributed, and Accelerated Probabilistic Programming., , , , , , , и . CoRR, (2018)