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Oral
Tue 6:00 What Are Bayesian Neural Network Posteriors Really Like?
Pavel Izmailov · Sharad Vikram · Matthew Hoffman · Andrew Wilson
Spotlight
Tue 6:20 Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning
Alexander Immer · Matthias Bauer · Vincent Fortuin · Gunnar Ratsch · Khan Emtiyaz
Spotlight
Tue 6:25 Amortized Conditional Normalized Maximum Likelihood: Reliable Out of Distribution Uncertainty Estimation
Aurick Zhou · Sergey Levine
Spotlight
Tue 6:30 Deep kernel processes
Laurence Aitchison · Adam Yang · Sebastian Ober
Spotlight
Tue 6:35 Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes
Sebastian Ober · Laurence Aitchison
Spotlight
Tue 6:40 Bayesian Deep Learning via Subnetwork Inference
Erik Daxberger · Eric Nalisnick · James Allingham · Javier Antorán · Jose Miguel Hernandez-Lobato
Spotlight
Tue 6:45 Generative Particle Variational Inference via Estimation of Functional Gradients
Neale Ratzlaff · Jerry Bai · Fuxin Li · Wei Xu
Spotlight
Tue 7:35 Graph Mixture Density Networks
Federico Errica · Davide Bacciu · Alessio Micheli
Spotlight
Tue 7:45 Better Training using Weight-Constrained Stochastic Dynamics
Benedict Leimkuhler · Tiffany Vlaar · Timothée Pouchon · Amos Storkey
Poster
Tue 9:00 Better Training using Weight-Constrained Stochastic Dynamics
Benedict Leimkuhler · Tiffany Vlaar · Timothée Pouchon · Amos Storkey
Poster
Tue 9:00 Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes
Sebastian Ober · Laurence Aitchison
Poster
Tue 9:00 Deep kernel processes
Laurence Aitchison · Adam Yang · Sebastian Ober