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 5 years ago
Alternatives and similar repositories for mc-dropout-mnist:
Users that are interested in mc-dropout-mnist are comparing it to the libraries listed below
- Uncertainty quantification using Bayesian neural networks in classification (MIDL 2018, CSDA)☆136Updated 5 years ago
- 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☆155Updated 2 years ago
- My implementation of the paper "Simple and Scalable Predictive Uncertainty estimation using Deep Ensembles"☆137Updated 7 years ago
- Code for MC Dropout and Model Ensembling Uncertainty Estimate experiments☆68Updated 5 years ago
- A pytorch implementation of MCDO(Monte-Carlo Dropout methods)☆56Updated 6 years ago
- Dropout as Regularization and Bayesian Approximation☆58Updated 6 years ago
- Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)☆75Updated last year
- This repository reimplemented "MC Dropout" by tensorflow 2.0 Eager Extension.☆18Updated 2 years ago
- Code for "Well-calibrated Model Uncertainty with Temperature Scaling for Dropout Variational Inference" (NeurIPS Bayesian Deep Learning W…☆24Updated 5 years ago
- Use Gaussian processes to estimate CNN classification uncertainty☆12Updated 7 years ago
- Code for the paper 'Understanding Measures of Uncertainty for Adversarial Example Detection'☆60Updated 6 years ago
- Learning error bars for neural network predictions☆70Updated 5 years ago
- ☆236Updated 4 years ago
- Epistemic Uncertainty Estimation with Monte Carlo Dropout☆8Updated 5 years ago
- Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference, Gal et al. 2015☆36Updated 6 years ago
- ☆24Updated 5 years ago
- A tutorial for the 2018 paper Accurate Uncertainties for Deep Learning Using Calibrated Regression by Kuleshov et al.☆50Updated 5 years ago
- Utilities to perform Uncertainty Quantification on Keras Models☆115Updated last year
- The code for the cycle wasserstein regression generative adversarial network model for semi supervised bi-directional regression.☆24Updated 6 years ago
- Work on Evidential Deep Learning to Quantify Classification Uncertainty☆60Updated 6 years ago
- TensorFlow Probability Tutorial☆37Updated 5 years ago
- Code associated with ACM-CHIL 21 paper 'T-DPSOM - An Interpretable Clustering Method for Unsupervised Learning of Patient Health States'☆68Updated 4 years ago
- ☆25Updated 2 years ago
- Pytorch implementation of SOM-VAE: INTERPRETABLE DISCRETE REPRESENTATION LEARNING ON TIME SERIES https://arxiv.org/pdf/1806.02199v7.pdf☆32Updated 5 years ago
- Replication of the paper "Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference" - Yarin Gal, Zoubin Gh…☆10Updated 7 years ago
- Reproducing the results of the paper "Bayesian Recurrent Neural Networks" by Fortunato et al.☆40Updated 7 years ago
- Notes and codes of the topic "Bayesian deep learning"☆56Updated 6 years ago
- Uncertainty estimation on Mnist dataset☆23Updated 7 years ago
- Bayesian Neural Network in PyTorch☆86Updated 11 months ago