Kyushik / Predictive-Uncertainty-Estimation-using-Deep-EnsembleLinks
This repository is the code for Predictive Uncertainty Estimation using Deep Ensemble
☆157Updated 3 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:
- ☆239Updated 5 years ago
- Code for "Uncertainty Estimation Using a Single Deep Deterministic Neural Network"☆275Updated 3 years ago
- PyTorch implementation of "Weight Uncertainty in Neural Networks"☆176Updated 3 years ago
- Experiments used in "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning"☆581Updated 3 years ago
- AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'☆90Updated 5 years ago
- My implementation of the paper "Simple and Scalable Predictive Uncertainty estimation using Deep Ensembles"☆140Updated 8 years ago
- " Weight Uncertainty in Neural Networks"☆49Updated 8 years ago
- Dropout as Regularization and Bayesian Approximation☆57Updated 7 years ago
- ☆110Updated 4 years ago
- Code for MC Dropout and Model Ensembling Uncertainty Estimate experiments☆73Updated 6 years ago
- Code for "On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty".☆114Updated 3 years ago
- Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020)☆207Updated 4 years ago
- Learning error bars for neural network predictions☆72Updated 6 years ago
- Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)☆78Updated 2 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 6 years ago
- Code repo for "A Simple Baseline for Bayesian Uncertainty in Deep Learning"☆478Updated 2 years ago
- A pytorch implementation of MCDO(Monte-Carlo Dropout methods)☆58Updated 7 years ago
- Implementation and evaluation of different approaches to get uncertainty in neural networks☆141Updated 7 years ago
- Bayesian Neural Network in PyTorch☆93Updated last year
- ☆41Updated 6 years ago
- Literature survey, paper reviews, experimental setups and a collection of implementations for baselines methods for predictive uncertaint…☆639Updated 3 years ago
- Code for paper: SDE-Net: Equipping Deep Neural network with Uncertainty Estimates☆117Updated 5 years ago
- A tutorial for the 2018 paper Accurate Uncertainties for Deep Learning Using Calibrated Regression by Kuleshov et al.☆52Updated 6 years ago
- Code that accompanies the paper Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance☆77Updated 7 years ago
- Building a Bayesian deep learning classifier☆493Updated 8 years ago
- Original PyTorch implementation of Uncertainty-guided Continual Learning with Bayesian Neural Networks, ICLR 2020☆76Updated 4 years ago
- Pytorch implementation of Neural Processes for functions and images☆236Updated 4 years ago
- Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts (Neurips 2020)☆78Updated 3 years ago
- ☆68Updated 6 years ago
- Calibration library and code for the paper: Verified Uncertainty Calibration. Ananya Kumar, Percy Liang, Tengyu Ma. NeurIPS 2019 (Spotlig…☆153Updated 3 years ago