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Continuously-indexed flows (CIFs) have recently achieved improvements over baseline normalizing flows on a variety of density estimation tasks.
Gradient-based learning applied to document recognition
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An auxiliary variational method
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Density estimation by dual ascent of the log-likelihood
Esteban G Tabak, Eric Vanden-Eijnden, et al · 2010
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Auto-encoding variational Bayes
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Stochastic backpropagation and approximate inference in deep generative models
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Variational inference with normalizing flows
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Improved variational inference with inverse autoregressive flow
Durk P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
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Rajesh Ranganath, Dustin Tran, and David Blei · 2016
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Variational inference: A review for statisticians
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Dustin Tran, Rajesh Ranganath, and David Blei · 2017
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Compression with flows via local bits-back coding
Jonathan Ho, Evan Lohn, and Pieter Abbeel · 2019
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Integer discrete flows and lossless compression
Emiel Hoogeboom, Jorn Peters, Rianne van den Berg, and Max Welling · 2019
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Sum-of-squares polynomial flow
Priyank Jaini, Kira A Selby, and Yaoliang Yu · 2019
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Energy-inspired models: Learning with sampler-induced distributions
John Lawson, George Tucker, Bo Dai, and Rajesh Ranganath · 2019
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Sequential neural likelihood: Fast likelihood-free inference with autoregressive flows
George Papamakarios, David Sterratt, and Iain Murray · 2019
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Importance weighted hierarchical variational inference
Artem Sobolev and Dmitry P Vetrov · 2019
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Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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On nesting Monte Carlo estimators
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On the invertibility of invertible neural networks, 2020
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