vvanirudh / deep-ensembles-uncertaintyLinks
My implementation of the paper "Simple and Scalable Predictive Uncertainty estimation using Deep Ensembles"
☆138Updated 7 years ago
Alternatives and similar repositories for deep-ensembles-uncertainty
Users that are interested in deep-ensembles-uncertainty are comparing it to the libraries listed below
Sorting:
- Implementation and evaluation of different approaches to get uncertainty in neural networks☆140Updated 7 years ago
- This repository is the code for Predictive Uncertainty Estimation using Deep Ensemble☆156Updated 2 years ago
- Code for the paper 'Understanding Measures of Uncertainty for Adversarial Example Detection'☆61Updated 7 years ago
- Deep convolutional gaussian processes.☆78Updated 5 years ago
- Learning error bars for neural network predictions☆70Updated 5 years ago
- PyTorch implementation of "Weight Uncertainty in Neural Networks"☆176Updated 3 years ago
- Code for Deep Bayesian Active Learning (ICML 2017)☆112Updated 7 years ago
- Original PyTorch implementation of Uncertainty-guided Continual Learning with Bayesian Neural Networks, ICLR 2020☆75Updated 4 years ago
- Dropout As A Bayesian Approximation: Code☆202Updated 9 years ago
- Code for the ICCV 2019 paper "Sampling-free Epistemic Uncertainty Estimation Using Approximated Variance Propagation"☆94Updated last year
- Sample code for running deterministic variational inference to train Bayesian neural networks☆100Updated 6 years ago
- " Weight Uncertainty in Neural Networks"☆49Updated 7 years ago
- Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference, Gal et al. 2015☆36Updated 7 years ago
- Multiplicative Normalizing Flow (MNF) posteriors for variational Bayesian neural networks☆65Updated 4 years ago
- Implementations of the ICML 2017 paper (with Yarin Gal)☆38Updated 7 years ago
- Demos demonstrating the difference between homoscedastic and heteroscedastic regression with dropout uncertainty.☆140Updated 9 years ago
- Active Learning on Image Data using Bayesian ConvNets☆138Updated 8 years ago
- Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors☆62Updated 5 years ago
- Code for ICML 2018 paper on "Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam" by Khan, Nielsen, Tangkaratt, Lin, …☆112Updated 6 years ago
- Code for Concrete Dropout as presented in https://arxiv.org/abs/1705.07832☆251Updated 6 years ago
- ☆53Updated 7 years ago
- Official implementation of "Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision", CVPR Workshops 2020.☆131Updated 4 years ago
- An implementation of "Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles" (http://arxiv.org/abs/1612.01474)☆34Updated 8 years ago
- Uncertainty quantification using Bayesian neural networks in classification (MIDL 2018, CSDA)☆137Updated 5 years ago
- Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)☆76Updated last year
- Experiments used in "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning"☆574Updated 3 years ago
- Deep Kernel Learning. Gaussian Process Regression where the input is a neural network mapping of x that maximizes the marginal likelihood☆93Updated 7 years ago
- Code that accompanies the paper Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance☆76Updated 6 years ago
- PyTorch Implementations of Dropout Variants☆87Updated 7 years ago
- Implementation of Bayesian Gradient Descent☆37Updated last year