Pinned Repositories
Deep-Probabilistic-Models-Course
Deep Probabalistic Models: Materials for my course for the Australian Mathematical Sciences Institute (AMSI) Winter School 2021
AutomaticDifferentiation-Workshop
Material from Workshop on Automatic Differentiation (Autograd/Pytorch)
simpleStochasticGradient
Simple class-based Python implementation of stochastic gradient methods - RADAM, ADAM, ADADELTA, and SGD (with momentum). Easy to plug into and use for Stochastic Gradient Variational Inference.
NS-SMC
Code for Phase Transition experiment in "Unbiased and Consistent Nested Sampling via Sequential Monte Carlo" by Salomone et al., (2023)
SpectralSubsamplingMCMC
Code for the paper "Spectral Subsampling MCMC for Stationary Time Series", ICML 2020.
DeepRegressionEnsembles-CRISPRon
Code for simple implementation of Deep Ensembles (Lakshminarayanan et al., 2017) for uncertainty quantification in the GLM with univariate response setting (Normal, Beta, or Gamma), and implementation of the CRISPRon (Xiang et al., 2021) architecture in Pytorch.
robsalomone.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
pyro
Deep universal probabilistic programming with Python and PyTorch
Programs
Programs
SMC-NS
Unbiased and Consistent Nested Sampling via Sequential Monte Carlo
robsalomone's Repositories
robsalomone/robsalomone.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
robsalomone/DeepRegressionEnsembles-CRISPRon
Code for simple implementation of Deep Ensembles (Lakshminarayanan et al., 2017) for uncertainty quantification in the GLM with univariate response setting (Normal, Beta, or Gamma), and implementation of the CRISPRon (Xiang et al., 2021) architecture in Pytorch.
robsalomone/NS-SMC
Code for Phase Transition experiment in "Unbiased and Consistent Nested Sampling via Sequential Monte Carlo" by Salomone et al., (2023)
robsalomone/SpectralSubsamplingMCMC
Code for the paper "Spectral Subsampling MCMC for Stationary Time Series", ICML 2020.
robsalomone/Deep-Probabilistic-Models-Course
Deep Probabalistic Models: Materials for my course for the Australian Mathematical Sciences Institute (AMSI) Winter School 2021
robsalomone/AutomaticDifferentiation-Workshop
Material from Workshop on Automatic Differentiation (Autograd/Pytorch)
robsalomone/simpleStochasticGradient
Simple class-based Python implementation of stochastic gradient methods - RADAM, ADAM, ADADELTA, and SGD (with momentum). Easy to plug into and use for Stochastic Gradient Variational Inference.