Kyushik / Predictive-Uncertainty-Estimation-using-Deep-EnsembleLinks
This repository is the code for Predictive Uncertainty Estimation using Deep Ensemble
☆156Updated 2 years ago
Alternatives and similar repositories for Predictive-Uncertainty-Estimation-using-Deep-Ensemble
Users that are interested in Predictive-Uncertainty-Estimation-using-Deep-Ensemble are comparing it to the libraries listed below
Sorting:
- ☆238Updated 5 years ago
- PyTorch implementation of "Weight Uncertainty in Neural Networks"☆176Updated 3 years ago
- My implementation of the paper "Simple and Scalable Predictive Uncertainty estimation using Deep Ensembles"☆138Updated 7 years ago
- Implementation and evaluation of different approaches to get uncertainty in neural networks☆140Updated 7 years ago
- Code for "Uncertainty Estimation Using a Single Deep Deterministic Neural Network"☆272Updated 3 years ago
- AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'☆89Updated 4 years ago
- Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)☆76Updated last year
- ☆108Updated 3 years ago
- Experiments used in "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning"☆574Updated 3 years ago
- Learning error bars for neural network predictions☆70Updated 5 years ago
- Dropout as Regularization and Bayesian Approximation☆58Updated 6 years ago
- Code for "On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty".☆112Updated 3 years ago
- A pytorch implementation of MCDO(Monte-Carlo Dropout methods)☆56Updated 6 years ago
- Implementation of the MNIST experiment for Monte Carlo Dropout from http://mlg.eng.cam.ac.uk/yarin/PDFs/NIPS_2015_bayesian_convnets.pdf☆30Updated 5 years ago
- Uncertainty quantification using Bayesian neural networks in classification (MIDL 2018, CSDA)☆137Updated 5 years ago
- Code for MC Dropout and Model Ensembling Uncertainty Estimate experiments☆69Updated 5 years ago
- Bayesian Neural Network in PyTorch☆89Updated last year
- " Weight Uncertainty in Neural Networks"☆49Updated 7 years ago
- Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020)☆205Updated 3 years ago
- Literature survey, paper reviews, experimental setups and a collection of implementations for baselines methods for predictive uncertaint…☆628Updated 2 years ago
- Code repo for "A Simple Baseline for Bayesian Uncertainty in Deep Learning"☆464Updated last year
- ☆38Updated 6 years ago
- Original PyTorch implementation of Uncertainty-guided Continual Learning with Bayesian Neural Networks, ICLR 2020☆75Updated 4 years ago
- A tutorial for the 2018 paper Accurate Uncertainties for Deep Learning Using Calibrated Regression by Kuleshov et al.☆51Updated 5 years ago
- Code to accompany the paper 'Improving model calibration with accuracy versus uncertainty optimization'.☆54Updated 2 years ago
- Pytorch implementation of Neural Processes for functions and images☆230Updated 3 years ago
- ☆241Updated 2 years ago
- Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts (Neurips 2020)☆77Updated 3 years ago
- This repository provides the code used to implement the framework to provide deep learning models with total uncertainty estimates as des…☆228Updated 11 months ago
- This repository contains an official implementation of LPBNN.☆38Updated last year