In the first stage, we run SG-MCMC with group sparse priors to draw an ensemble of samples from the posterior distribution of network parameters. In the second stage, we apply weight-pruning to each sampled network and then perform retraining over the remained connections. In this way of ...
SGMCMC requires calculating gradients of the log-likelihood and log-priors, which can be time consuming and error prone to perform by hand. The sgmcmc package calculates these gradients itself using automatic differentiation, making the implementation of these methods much easier. To do this, the ...
Simulations for sgmcmc package Simulations for the package sgmcmc available for R. The simulations are for the companion paper available from arXiv (see Section 5). Running the simulations To run the simulations simply run the script runSimulations.R. The plot output corresponding to each simulati...
SGMCMCJax is a lightweight library of stochastic gradient Markov chain Monte Carlo (SGMCMC) algorithms. The aim is to include both standard samplers (SGLD, SGHMC) as well as state of the art samplers while requiring only JAX to run. ...
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Multi-variance replica exchange SGMCMC for inverse and forward problems via Bayesian PINNReplica exchangeBayesian physics-informed neural networkInverse problemMachine learningDeep learningNeural networkPhysics-informed neural network (PINN) has been successfully applied in solving a variety of nonlinear non-...
Multi-Variance Replica Exchange SGMCMC for Inverse and Forward Problems Via Bayesian PINNdoi:10.2139/ssrn.3979582replica exchangeBayesian physics-informed neural networkMarkov Chain Monte CarloMachine LearningDeep learningNeural networkPhysics-informed neural network (PINN) has been successfully applied in ...