stat-ml / dpp-dropout-uncertainty
Effective uncertainty estimation with decorellation and DPP mask for dropout
☆9Updated last year
Related projects: ⓘ
- Pytorch implementation of Variational Dropout Sparsifies Deep Neural Networks☆83Updated 2 years ago
- Library for active learning and uncertainty estimation in machine learning☆26Updated last year
- Code to accompany the paper Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning☆33Updated 4 years ago
- Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"☆33Updated last year
- Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors☆62Updated 4 years ago
- Models and code for the ICLR 2020 workshop paper "Towards Understanding Normalization in Neural ODEs"☆16Updated 4 years ago
- [AAAI 2020 Oral] Low-variance Black-box Gradient Estimates for the Plackett-Luce Distribution☆36Updated 3 years ago
- Implementation of the paper "Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory", Ron Amit and Ron Meir, ICML 2018☆17Updated 3 years ago
- The Deep Weight Prior, ICLR 2019☆44Updated 3 years ago
- Sliced Wasserstein Generator☆23Updated 5 years ago
- Code for MSID, a Multi-Scale Intrinsic Distance for comparing generative models, studying neural networks, and more!☆49Updated 5 years ago
- PyTorch implementation of Bidirectional Monte Carlo, Annealed Importance Sampling, and Hamiltonian Monte Carlo.☆52Updated 3 years ago
- Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'☆26Updated 3 years ago
- Supplementary code for the paper "Meta-Solver for Neural Ordinary Differential Equations" https://arxiv.org/abs/2103.08561☆25Updated 3 years ago
- Quadrature-based features for kernel approximation☆16Updated 5 years ago
- Code Repo for "Subspace Inference for Bayesian Deep Learning"☆79Updated 3 months ago
- Implementation of the models and datasets used in "An Information-theoretic Approach to Distribution Shifts"☆24Updated 2 years ago
- An official PyTorch implementation of "Regression Prior Networks" for effective runtime uncertainty estimation.☆35Updated 3 years ago
- ☆26Updated 5 years ago
- Exercises for the Tutorial on Approximate Bayesian Inference at the Data Science Summer School 2018☆22Updated 6 years ago
- Code for Augment & Reduce, a scalable stochastic algorithm for large categorical distributions☆10Updated 6 years ago
- Code for the paper 'Understanding Measures of Uncertainty for Adversarial Example Detection'☆57Updated 6 years ago
- Code for ICML 2018 paper on "Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam" by Khan, Nielsen, Tangkaratt, Lin, …☆109Updated 5 years ago
- ☆80Updated last year
- Pytorch implementation of the DWP with application to MRI segmentation☆9Updated 4 years ago
- Contains code for the NeurIPS 2020 paper by Pan et al., "Continual Deep Learning by FunctionalRegularisation of Memorable Past"☆43Updated 3 years ago
- Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019☆41Updated last year
- Pytorch implementation of the basic idea presented in https://arxiv.org/abs/2002.07101☆11Updated 4 years ago
- Pytorch version of "Deep Convolutional Networks as shallow Gaussian Processes" by Adrià Garriga-Alonso, Carl Rasmussen and Laurence Aitch…☆32Updated 4 years ago
- We got a stew going!☆27Updated 11 months ago