Deep Bayes Reading Club

2017

Date Time Room Article Discussant

Jun 12

16:00-17:00 C3.211 VAE with a VampPrior Jakub Tomczak

Apr 3

16:00-17:00 C3.211 Learning structured weight uncertainty in Bayesian neural networks Mijung Park

Mar 20

16:00-17:00 C3.211 Approximate Bayesian inference with the weighted likelihood Bootstrap  Matthias Reisser

Mar 13

16:00-17:00 C3.211  Generalization and Equilibrium in Generative Adversarial Nets  Peter O'Connor

Mar 6

 16:00-17:00 C3.211 Improved generator objectives for GANs Mijung Park

Feb 6

16:00-17:00 C3.211 Wasserstein GAN Mijung Park

Jan 30

16:00-17:00 C3.211 Learning in implicit generative models Christos Louizos

Jan 23

16:00-17:00 C3.211 Hierarchical Variational Models Christos Louizos

Jan 16

16:00-17:00 C3.265 Boosting Variational Inference Mijung Park

2016

Date Time Room Article Discussant

Nov 28

16:00-17:00 C3.265  Optimizing Neural Networks with Kronecker-factored Approximate Curvature  Changyong Oh

Nov 21

16:00-17:00 C3.265 Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm Christos Louizos

Nov 14

 16:00-17:00 C3.265 Adversarial Autoencoders   Jakub Tomczak

Nov 07

16:00-17:00 C3.265  Why does deep and cheap learning work so well? Thomas Kipf

Oct 17

16:00-17:00 C3.265 A complete recipe for stochastic gradient MCMC Mijung Park

Jul 11

16:00-17:00 C3.265 A variational analysis of stochastic gradient algorithms Mijung Park

Jun 06

16:00-17:00 C3.265 Auxiliary deep generative models Changyong Oh

May 30

16:00-17:00 C3.265 Deconstructing the ladder network architecture Thomas Kipf

May 12

16:00-17:00 C3.211 Semi-supervised learning with ladder networks Thomas Kipf

May 02

16:00-17:00 C3.211 Deep Gaussian processes Thomas Kipf

Apr 25

16:00-17:00 C3.211  The variational fair auto encoder and Variational autoencoded deep Gaussian processes  Christos Louizos