Talks and Videos

Last updated December 2015.

If you’re looking for the ICML’15 Deep Learning Workshop video recordings, click here.

2015-12-12: Invited Talk, Black Box Inference and Learning Workshop, NIPS’15, Montreal, Canada
Variational Auto-Encoders and Extensions
Presentation: [PDF]

2015-10-14: Invited Talk, University of Cambridge, U.K.
2015-10-21: Invited Talk, Columbia University, U.S.A.
Efficient Inference and Learning with Intractable Posteriors? Yes, Please.
Presentation: [PDF]

2014-12-9: Spotlight Talk (Deep Spotlights), NIPS’14, Montreal, Canada
Semi-Supervised Learning with Deep Generative Models
Presentation: [PDF]
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Visualisation of latent space of deep generative model of SVHN digits:
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2014-12-13: Invited Talk, NIPS’14 Workhop on Advances in Variational Inference, Montreal, Canada
Stochastic Backpropagation, Variational Inference, and Semi-Supervised Learning
Presentation: [PDF]

2014-09: Deep Probabilistic Models Workshop, Sheffield, U.K.
Deep Generative Models
Presentation: [PDF]

2014-06-27: Invited Talk, Tsinghua University, Beijing, China
Auto-Encoding Variational Bayes
Presentation: [PDF]

2014-06: International Conference on Maching Learning, Beijing, China (ICML 2014)
Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets
Presentation: [PDF] [ODP]

2014-04: International Conference on Learning Representations, Banff, Canada (ICLR 2014)
Auto-Encoding Variational Bayes
Presentation: [PDF] [ODP]

2014-03-26: Invited Talk, Google Deepmind, London, U.K.
Stochastic Gradient VB and the Variational Auto-Encoder
Paper: [PDF]

2014-01: IAS Talk, Univ. of Amsterdam, Netherlands
Stochastic Gradient VB. Intractable posterior distributions? Gradients to the rescue!
Presentation: [PDF] [ODP]

2013-07: CIFAR NCAP Summer School, Univ. of Toronto, Canada, hosted by Geoff Hinton
Speeding up Gradient-Based Inference and Learning in deep/recurrent Bayes Nets with Continuous Latent Variables
Presentation: [PDF] [ODP]

Misc Video’s

As the ODP files have been stripped from the embedded video’s, here are some of the video’s hosted on Youtube.

Learning generative models of MNIST digits

With L-BFGS:
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With Monte Carlo Expectation Maximization (MCEM):
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With Auto-Encoding Variational Bayes (AEVB):
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3D manifold of MNIST (Learned with AEVB):
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