Kyushik / Predictive-Uncertainty-Estimation-using-Deep-Ensemble

Related projects ⓘ

Alternatives and complementary repositories for Predictive-Uncertainty-Estimation-using-Deep-Ensemble

• cpark321 / uncertainty-deep-learning
  ☆226Updated 4 years ago
• vvanirudh / deep-ensembles-uncertainty
  My implementation of the paper "Simple and Scalable Predictive Uncertainty estimation using Deep Ensembles"
  ☆135Updated 6 years ago
• SamsungLabs / pytorch-ensembles
  Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning, ICLR 2020
  ☆235Updated last year
• hutec / UncertaintyNN
  Implementation and evaluation of different approaches to get uncertainty in neural networks
  ☆140Updated 6 years ago
• TeaPearce / Bayesian_NN_Ensembles
  AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'
  ☆86Updated 4 years ago
• y0ast / deterministic-uncertainty-quantification
  Code for "Uncertainty Estimation Using a Single Deep Deterministic Neural Network"
  ☆268Updated 2 years ago
• JakobCode / UncertaintyInNeuralNetworks_Resources
  ☆97Updated 3 years ago
• sungyubkim / MCDO
  A pytorch implementation of MCDO(Monte-Carlo Dropout methods)
  ☆56Updated 5 years ago
• facebookresearch / SingleModelUncertainty
  Learning error bars for neural network predictions
  ☆68Updated 4 years ago
• ykwon0407 / UQ_BNN
  Uncertainty quantification using Bayesian neural networks in classification (MIDL 2018, CSDA)
  ☆135Updated 5 years ago
• nitarshan / bayes-by-backprop
  PyTorch implementation of "Weight Uncertainty in Neural Networks"
  ☆170Updated 2 years ago
• huyng / incertae
  Code for MC Dropout and Model Ensembling Uncertainty Estimate experiments
  ☆59Updated 4 years ago
• cambridge-mlg / DUN
  Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)
  ☆72Updated last year
• y0ast / DUE
  Code for "On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty".
  ☆111Updated 2 years ago
• pmorerio / dl-uncertainty
  "What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?", NIPS 2017 (unofficial code).
  ☆207Updated 4 years ago
• 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
  ☆30Updated 4 years ago
• yaringal / DropoutUncertaintyExps
  Experiments used in "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning"
  ☆555Updated 2 years ago
• wjmaddox / swa_gaussian
  Code repo for "A Simple Baseline for Bayesian Uncertainty in Deep Learning"
  ☆454Updated last year
• ThirstyScholar / bayes-by-backprop
  " Weight Uncertainty in Neural Networks"
  ☆45Updated 6 years ago
• AlaaLab / deep-learning-uncertainty
  Literature survey, paper reviews, experimental setups and a collection of implementations for baselines methods for predictive uncertaint…
  ☆611Updated 2 years ago
• giannifranchi / LP_BNN
  This repository contains an official implementation of LPBNN.
  ☆39Updated last year
• anassinator / bnn
  Bayesian Neural Network in PyTorch
  ☆80Updated 6 months ago
• SaynaEbrahimi / UCB
  Original PyTorch implementation of Uncertainty-guided Continual Learning with Bayesian Neural Networks, ICLR 2020
  ☆73Updated 3 years ago
• saxena-mayur / Weight-Uncertainty-in-Neural-Networks
  ☆38Updated 5 years ago
• sharpenb / Posterior-Network
  Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts (Neurips 2020)
  ☆74Updated 2 years ago
• xuwd11 / Dropout_Tutorial_in_PyTorch
  Dropout as Regularization and Bayesian Approximation
  ☆57Updated 6 years ago
• KaosEngineer / PriorNetworks
  ☆65Updated 4 years ago
• ruqizhang / csgmcmc
  Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning
  ☆92Updated 4 years ago
• omegafragger / DDU
  Code for Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty
  ☆129Updated last year
• AnthonyRentsch / calibrated_regression
  A tutorial for the 2018 paper Accurate Uncertainties for Deep Learning Using Calibrated Regression by Kuleshov et al.
  ☆50Updated 4 years ago