Today’s clip is from episode 138 of the podcast, with Mélodie Monod, François-Xavier Briol and Yingzhen Li.
During this live show at Imperial College London, Alex and his guests delve into the complexities and advancements in Bayesian deep learning, focusing on uncertainty quantification, the integration of machine learning tools, and the challenges faced in simulation-based inference.
The speakers discuss their current projects, the evolution of Bayesian models, and the need for better computational tools in the field.
Get the full discussion here.
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Transcript
This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.
