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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
DGMs for NLP. A roadmap.
mcmc
parsing
decoding
language-model
generative-text
text-generation
generative-model
graphical-models
generative-models
normalizing-flows
discrete-structures
gradient-estimation
approximate-inference
structured-prediction
variational-inference
latent-variable-models
markov-chain-monte-carlo
variational-autoencoders
natural-language-processing
generative-adversarial-networks
Created
2019-04-09
191 commits to master branch, last one 2 years ago
Probabilistic Programming with Gaussian processes in Julia
Created
2017-08-15
482 commits to master branch, last one about a year ago
Hashed Lookup Table based Matrix Multiplication (halutmatmul) - Stella Nera accelerator
Created
2022-03-07
780 commits to master branch, last one about a year ago
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 ...
Created
2023-01-16
18 commits to main branch, last one about a year ago