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JavierAntoran
Bayesian-Neural-Networks JavierAntoran
302
1.8k
mit
27
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
hmc mcmc sgld pytorch mc-dropout regression uncertainty deep-learning classification bayes-by-backprop langevin-dynamics bayesian-inference approximate-inference reproducible-research variational-inference bayesian-neural-networks uncertainty-neural-networks local-reparametrization-trick out-of-distribution-detection kronecker-factored-approximation
Created 2019-03-11
86 commits to master branch, last one about a year ago
SHI-Yu-Zhe
generative-modeling-explained SHI-Yu-Zhe
6
51
cc0-1.0
4
Ying Nian Wu's UCLA Statistical Machine Learning Tutorial on generative modeling.
mcmc-sampling diffusion-model langevin-dynamics bayesian-inference energy-based-model generative-modeling adversarial-learning equilibrium-sampling variational-inference non-equilibrium-sampling score-based-generative-modeling
Created 2022-08-17
137 commits to main branch, last one about a year ago
bahjat-kawar
snips_torch bahjat-kawar
6
50
mit
1
[NeurIPS 2021] SNIPS: Solving Noisy Inverse Problems Stochastically
diffusion deblurring inverse-problems super-resolution langevin-dynamics compressed-sensing
Created 2021-05-29
7 commits to main branch, last one 2 years ago
konstantinos-p
Bayesian-Neural-Networks-Reading-List konstantinos-p
3
49
unknown
2
A primer on Bayesian Neural Networks. The aim of this reading list is to facilitate the entry of new researchers into the field of Bayesian Deep Learning, by providing an overview of key papers. More ...
hmc mcmc sgld uncertainty deep-learning bayes-by-backprop langevin-dynamics bayesian-inference approximate-inference variational-inference bayesian-neural-networks uncertainty-neural-networks local-reparametrization-trick kronecker-factored-approximation
Created 2023-01-16
18 commits to main branch, last one about a year ago