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The Brains Behind AI: Roger Grosse
DLRLSS 2019 - Bayesian Deep Learning - Roger Grosse
An Intro to Quantum Natural Gradient Descent
Free-form variational inference I
Natural Gradients
Nikolas Nüsken - On the Geometry of Stein Variational Gradient Descent
Black-box Stochastic Variational Inference in a Deep Bayesian Neural Network
Evaluating Lossy Compression Rates of Deep Generative Models - Roger Grosse
Variational Inference by Automatic Differentiation in TensorFlow Probability
Variational Inference 1 by Andrés R. Masegosa, Helge Langseth & Thomas D. Nielsen
Non-Smooth Optimization with Noisy Gradient-Free Oracle
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Last Updated: October 3, 2026
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DALI 2018 Workshop on Generative Models for Reinforcement Learning Speaker: Thank you Dave So I'll be talking about optimizing neural networks using structured probabilistic models of the Neural networks have revolutionized areas such as computer vision and This video was recorded as part of CIS 522 - Deep Learning at the University of Pennsylvania. The course material, including the ... An example of fitting a factorized Gaussian We find a surrogate posterior by maximizing the Evidence Lower Bound (ELBO). With a proposal distribution, this can be solved ... Nordic Probabilistic AI School (ProbAI) 2022 Materials: github.com/probabilisticai/probai-2022/
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