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Inference amortization methods share information across multiple posterior-inference problems, allowing each to be carried out more efficiently.
Learning stochastic feedforward networks
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Annealed Importance Sampling (technical report 9805 (revised))
Neal, Radford M · 1998
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Amortized inference in probabilistic reasoning
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Auto-encoding variational bayes
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Adam: A method for stochastic optimization
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Semi-supervised learning with deep generative models
Kingma, Diederik P, Mohamed, Shakir, Jimenez Rezende, Danilo, and Welling, Max · 2014
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Deep temporal sigmoid belief networks for sequence modeling
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Reinterpreting importance-weighted autoencoders
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Categorical reparameterization with Gumbel-softmax
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Improving variational inference with Inverse Autoregressive Flow
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Deep amortized inference for probabilistic programs
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Auto-encoding Sequential Monte Carlo
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Variational Sequential Monte Carlo
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Tighter variational bounds are not necessarily better
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