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