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Found 3 papers, 1 papers with code
AdamMCMC: Combining Metropolis Adjusted Langevin with Momentum-based Optimization
Uncertainty estimation is a key issue when considering the application of deep neural network methods in science and engineering.
Statistical guarantees for stochastic Metropolis-Hastings
A Metropolis-Hastings step is widely used for gradient-based Markov chain Monte Carlo methods in uncertainty quantification.
A PAC-Bayes oracle inequality for sparse neural networks
We study the Gibbs posterior distribution for sparse deep neural nets in a nonparametric regression setting.
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