#

mc-dropout

Here are 10 public repositories matching this topic...

JavierAntoran / Bayesian-Neural-Networks

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

deep-learning reproducible-research regression pytorch uncertainty classification uncertainty-neural-networks bayesian-inference mcmc variational-inference hmc bayesian-neural-networks langevin-dynamics approximate-inference local-reparametrization-trick kronecker-factored-approximation mc-dropout bayes-by-backprop out-of-distribution-detection sgld

Updated Oct 20, 2023
Jupyter Notebook

alexrakowski / mc-dropout-mnist

Implementation of the MNIST experiment for Monte Carlo Dropout from http://mlg.eng.cam.ac.uk/yarin/PDFs/NIPS_2015_bayesian_convnets.pdf

deep-learning keras mnist uncertainty-neural-networks mc-dropout bayesian-convnets

Updated Jan 15, 2020
Python

kenya-sk / mc_dropout_tensorflow

This repository reimplemented "MC Dropout" by tensorflow 2.0 Eager Extension.

tensorflow-eager mc-dropout tensorflow2

Updated Mar 25, 2023
Jupyter Notebook

ronaldseoh / ronald_bdl

An experimental Python package for learning Bayesian Neural Network.

deep-learning pytorch vi variational-inference hmc hamiltonian-monte-carlo pyro bayesian-deep-learning mc-dropout monte-carlo-dropout bayesian-neural-network

Updated Jul 6, 2020
Python

ronaldseoh / DropoutUncertaintyExps

(Forked Version) Experiments used in "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning"

deep-learning keras jupyter-notebook dropout reproducibility bayesian-deep-learning mc-dropout monte-carlo-dropout bayesian-neural-network

Updated Feb 26, 2020
Jupyter Notebook

cplou99 / BayesianDL

We provide two notebooks that enable users to explore and experiment with some BDL techniques as Ensembles, MC Dropout and Laplace Approximation. In this way, they allow you to intuitively visualize the main differences among them in a Simulated Dataset and Boston Dataset.

uncertainty ensembles bayesian-deep-learning mc-dropout laplace-approximation

Updated Jun 1, 2023
Jupyter Notebook

knowledge-surrogate-opt

eismont21 / knowledge-surrogate-opt

A transformative approach to manufacturing optimization, focusing on the textile forming process. This research synergizes domain-specific knowledge with simulation modeling and introduces Bayesian optimization for efficient parameter space exploration.

data-science deep-learning bayesian-optimization simulation-modeling surrogate-models mc-dropout surrogate-based-optimization domain-knowledge-integration manufacturing-optimization fibre-draping

Updated Jan 12, 2024
Python

lolloloschi97 / Data-Drift

Data Drift Analysis and Anomaly detection tools

deep-learning sklearn pytorch pca vae psi anomaly-detection kolmogorov-smirnov mc-dropout data-drift

Updated Oct 17, 2022
Jupyter Notebook

tenacious-toucan

Kaleidophon / tenacious-toucan

Master thesis for the MSc. Artificial Intelligence at the Universiteit van Amsterdam, 2019

master-thesis uncertainty lstm rnn gradients interventions bayesian-neural-networks bayesian-deep-learning mc-dropout diagnostic-classifiers

Updated Apr 1, 2020
Python

akapet00 / neural-bhte

Numerical solution and uncertainty quantification of Pennes' bioheat transfer equation in 1-D using deep neural network solver.

uncertainty-quantification mc-dropout neural-pde bioheat-transfer-equation

Updated Jan 2, 2021
Jupyter Notebook

Improve this page

Add a description, image, and links to the mc-dropout topic page so that developers can more easily learn about it.

Curate this topic

Add this topic to your repo

To associate your repository with the mc-dropout topic, visit your repo's landing page and select "manage topics."