activatedgeek / understanding-bayesian-classificationLinks
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification
☆21Updated 3 years ago
Alternatives and similar repositories for understanding-bayesian-classification
Users that are interested in understanding-bayesian-classification are comparing it to the libraries listed below
Sorting:
- Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"☆33Updated 3 years ago
- Official implementation of Transformer Neural Processes☆78Updated 3 years ago
- Code for the paper "Bayesian Neural Network Priors Revisited"☆58Updated 4 years ago
- Code for the paper "Getting a CLUE: A Method for Explaining Uncertainty Estimates"☆35Updated last year
- Code for the paper 'Continual Learning via Sequential Function-Space Variational Inference'☆25Updated 2 years ago
- Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts (Neurips 2020)☆78Updated 3 years ago
- Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)☆78Updated 2 years ago
- General purpose library for BNNs, and implementation of OC-BNNs in our 2020 NeurIPS paper.☆38Updated 3 years ago
- Official code for the ICLR 2021 paper Neural ODE Processes☆75Updated 3 years ago
- Laplace Redux -- Effortless Bayesian Deep Learning☆44Updated 6 months ago
- Improving predictions of Bayesian neural nets via local linearization, AISTATS 2021☆16Updated 2 years ago
- Repo for our paper "Repulsive deep ensembles are Bayesian"☆19Updated 4 years ago
- [NeurIPS 2020] Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters (AHGP)☆23Updated 5 years ago
- [ICLR 2022] "Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How" by Yuning You, Yue Cao, Tianl…☆14Updated 3 years ago
- Principled learning method for Wasserstein distributionally robust optimization with local perturbations (ICML 2020)☆21Updated 2 years ago
- AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'☆90Updated 5 years ago
- Quantile risk minimization☆25Updated last year
- Repository for Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification (NeurIPS 2024)☆44Updated last year
- Bayesian active learning with EPIG data acquisition☆35Updated 3 months ago
- Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'☆26Updated 4 years ago
- ☆25Updated 2 years ago
- Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control☆72Updated last year
- Code for ICE-BeeM paper - NeurIPS 2020☆87Updated 4 years ago
- Contains code for the NeurIPS 2020 paper by Pan et al., "Continual Deep Learning by FunctionalRegularisation of Memorable Past"☆44Updated 5 years ago
- Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods☆21Updated 3 years ago
- Pytorch implementation of neural processes and variants☆29Updated last year
- Official repository of the paper "Efficient Neural Causal Discovery without Acyclicity Constraints"☆88Updated last year
- Code for experiments to learn uncertainty☆30Updated 2 years ago
- ☆15Updated 3 years ago
- Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)☆20Updated 6 years ago