pmorerio / Dl Uncertainty
Licence: mit
"What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?", NIPS 2017 (unofficial code).
Stars: ✭ 130
Programming Languages
python
139335 projects - #7 most used programming language
Projects that are alternatives of or similar to Dl Uncertainty
Quicknat pytorch
PyTorch Implementation of QuickNAT and Bayesian QuickNAT, a fast brain MRI segmentation framework with segmentation Quality control using structure-wise uncertainty
Stars: ✭ 74 (-43.08%)
Mutual labels: bayesian
Gauss Naive Bayes
Gauss Naive Bayes in Python From Scratch.
Stars: ✭ 22 (-83.08%)
Mutual labels: bayesian
Deepbayes 2018
Seminars DeepBayes Summer School 2018
Stars: ✭ 1,021 (+685.38%)
Mutual labels: bayesian
Bayesian Stats Modelling Tutorial
How to do Bayesian statistical modelling using numpy and PyMC3
Stars: ✭ 480 (+269.23%)
Mutual labels: bayesian
Statistical Rethinking
An interactive online reading of McElreath's Statistical Rethinking
Stars: ✭ 123 (-5.38%)
Mutual labels: bayesian
Matlabstan
Matlab interface to Stan, a package for Bayesian inference
Stars: ✭ 59 (-54.62%)
Mutual labels: bayesian
Gpstuff
GPstuff - Gaussian process models for Bayesian analysis
Stars: ✭ 106 (-18.46%)
Mutual labels: bayesian
Scikit Stan
A high-level Bayesian analysis API written in Python
Stars: ✭ 22 (-83.08%)
Mutual labels: bayesian
Autoppl
C++ template library for probabilistic programming
Stars: ✭ 34 (-73.85%)
Mutual labels: bayesian
Ml code
A repository for recording the machine learning code
Stars: ✭ 75 (-42.31%)
Mutual labels: bayesian
Rhat ess
Rank-normalization, folding, and localization: An improved R-hat for assessing convergence of MCMC
Stars: ✭ 19 (-85.38%)
Mutual labels: bayesian
Gen.jl
A general-purpose probabilistic programming system with programmable inference
Stars: ✭ 1,595 (+1126.92%)
Mutual labels: bayesian
Hbayesdm
Hierarchical Bayesian modeling of RLDM tasks, using R & Python
Stars: ✭ 124 (-4.62%)
Mutual labels: bayesian
Lda Topic Modeling
A PureScript, browser-based implementation of LDA topic modeling.
Stars: ✭ 91 (-30%)
Mutual labels: bayesian
Uncertainty in Deep Learning
Some code (TensorFlow) based on the paper:
A Kendall, Y Gal, “What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?”, NIPS 2017 arXiv
DISCLAIMER: This is NOT the official repo. It is just based on my understanding of the paper. Any feedback is welcome.
I am training a simple autoencoder (regression) to reconstruct MNIST digits.
Getting MNIST
Download MNIST:
./download.sh
Rescale and save in a python dictionary (possibly resize):
python prepro.py
License
This repository is released under the MIT LICENSE.
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].