Saturday Morning Videos: #NIPS 2016 Workshop on Advances in Approximate Bayesian Inference

Dustin Tran just made available the videos of the NIPS 2016 Workshop on Advances in Approximate Bayesian Inference

Recorded talks & panels are available for the NIPS 2016 Workshop on Advances in Approximate Bayesian Inference https://t.co/WRDMMeKzdq

— Dustin Tran (@dustinvtran) 11 février 2017

Here they are, enjoy !

Surya Ganguli: Learning deep generative models by reversing diffusion 24:36

Guillaume Dehaene: Expectation Propagation performs smooth gradient descent 13:51

Matthew Johnson: Learning representations for efficient inference 29:54

NIPS 2016 AABI Workshop: Software Panel 42:18

James McInerney: Empirical Bayes by Meta-Model Monte Carlo 15:56

NIPS 2016 AABI Workshop: Poster Spotlights 19:02

Barbara Engelhardt: Approximate Bayesian inference in high dimensional applications 22:10

Jeffrey Regier: Learning an astronomical catalog of the visible universe 22:25

Matt Hoffman: Inference and Introspection in Deep Generative Models of Sparse Data 14:49

Jonathan Huggins: Coresets for Scalable Bayesian Inference 26:43

Matthew Graham: Continuously Tempered Hamiltonian Monte Carlo 18:02

NIPS 2016 AABI Workshop: Foundations & Future of Approximate Inference Panel 57:51

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