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The premise of approximate MCMC in Bayesian deep learning (UCSB)
Talk on an approximate MCMC sampling approach to Bayesian deep learning.
Sep 26, 2022 3:30 PM — 4:30 PM University of California, Santa Barbara, Department of Computer Science
The premise of approximate MCMC in Bayesian deep learning (University of Warwick)
Talk on an approximate MCMC sampling approach to Bayesian deep learning.
Sep 8, 2022 9:10 AM — 9:35 AM University of Warwick, Department of Statistics
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Approximate MCMC for Bayesian deep learning
This project aims to develop approximate MCMC methods for Bayesian deep learning in order to quantify the uncertainty of predictions made by neural networks.
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Approximate MCMC for Bayesian deep learning
Challenges in Markov chain Monte Carlo for Bayesian neural networks
Markov chain Monte Carlo (MCMC) methods have not been broadly adopted in Bayesian neural networks (BNNs). This paper initially reviews …
Theodore Papamarkou, Jacob Hinkle, M. Todd Young, David Womble
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Challenges in Markov chain Monte Carlo for Bayesian neural networks
Talk on the predictive capacity of Bayesian marginalization for neural networks in the absence of MCMC convergence.
Apr 23, 2021 3:00 PM — 4:00 PM University of Glasgow, School of Mathematics and Statistics
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Wide neural networks with bottlenecks are deep Gaussian processes
There has recently been much work on the ‘wide limit’ of neural networks, where Bayesian neural networks (BNNs) are shown …
Devanshu Agrawal, Theodore Papamarkou, Jacob Hinkle
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