Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"
☆33Oct 19, 2022Updated 3 years ago
Alternatives and similar repositories for bnn_covariate_shift
Users that are interested in bnn_covariate_shift are comparing it to the libraries listed below. We may earn a commission when you buy through links labeled 'Ad' on this page.
Sorting:
- Supporing code for the paper "Bayesian Model Selection, the Marginal Likelihood, and Generalization".☆37Jun 16, 2022Updated 4 years ago
- A simple implementation of Hamiltonian Monte Carlo in JAX.☆20Feb 8, 2024Updated 2 years ago
- Interactive textbook on state space models☆12Apr 11, 2022Updated 4 years ago
- Code repository of the paper "Alleviating Adversarial Attacks on Variational Autoencoders with MCMC" published at NeurIPS 2022. https://a…☆10Dec 14, 2022Updated 3 years ago
- ☆47Jan 11, 2021Updated 5 years ago
- Managed Database hosting by DigitalOcean • AdPostgreSQL, MySQL, MongoDB, Kafka, Valkey, and OpenSearch available. Automatically scale up storage and focus on building your apps.
- On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification☆21Apr 1, 2022Updated 4 years ago
- Public Codebase for Rethinking Parameter Counting: Effective Dimensionality Revisited☆37Dec 27, 2022Updated 3 years ago
- ☆257Dec 27, 2022Updated 3 years ago
- Bayesian inference with Python and Jax.☆35Nov 23, 2022Updated 3 years ago
- ☆28Jan 31, 2022Updated 4 years ago
- Belief matching framework official implementation☆41Mar 24, 2023Updated 3 years ago
- ☆15Dec 7, 2021Updated 4 years ago
- AISTATS 2019: Reference-based Adversarial Sampling & Its applications to Soft Q-learning☆15Jan 21, 2019Updated 7 years ago
- Jax SSM Library☆48Nov 24, 2022Updated 3 years ago
- Deploy on Railway without the complexity - Free Credits Offer • AdConnect your repo and Railway handles the rest with instant previews. Quickly provision container image services, databases, and storage volumes.
- Code Repo for "Subspace Inference for Bayesian Deep Learning"☆83Jun 17, 2024Updated 2 years ago
- Repository containing code for getting statistical guarantees on properties of BNNs☆13Apr 24, 2019Updated 7 years ago
- ☆31May 1, 2025Updated last year
- ☆22Jun 15, 2022Updated 4 years ago
- Code for the paper "Bayesian Neural Network Priors Revisited"☆61Jul 1, 2021Updated 5 years ago
- Experiments for the NeurIPS 2021 paper "Cockpit: A Practical Debugging Tool for the Training of Deep Neural Networks"☆13Oct 25, 2021Updated 4 years ago
- Code repo for "Function-Space Distributions over Kernels"☆32Jan 21, 2021Updated 5 years ago
- Variance Networks: When Expectation Does Not Meet Your Expectations, ICLR 2019☆39Jan 31, 2020Updated 6 years ago
- ☆28Oct 26, 2022Updated 3 years ago
- Managed hosting for WordPress and PHP on Cloudways • AdManaged hosting for WordPress, Magento, Laravel, or PHP apps, on multiple cloud providers. Deploy in minutes on Cloudways by DigitalOcean.
- Library for Bayesian Neural Networks in PyTorch (first version as published in ProbProg2020)☆42Oct 5, 2021Updated 4 years ago
- Spurious Features Everywhere - Large-Scale Detection of Harmful Spurious Features in ImageNet☆32Aug 22, 2023Updated 2 years ago
- Pretrained models for Jax/Haiku; MobileNet, ResNet, VGG, Xception.☆24Apr 22, 2022Updated 4 years ago
- Exploiting Domain-Specific Features to Enhance Domain Generalization (NeurIPS 2021).☆29Apr 26, 2022Updated 4 years ago
- This is an article about using variational autoencoders for the generation of new data. It contains the code for generating the plots and…☆12Feb 15, 2021Updated 5 years ago
- Code for the papers: "Stop Throwing Away Discriminators! Re-using Adversaries for Test-Time Training", Valvano et al., DART 2021; and "Re…☆10Jan 20, 2022Updated 4 years ago
- [ICASSP 2020] Code release of paper 'Heterogeneous Domain Generalization via Domain Mixup'☆26Aug 3, 2020Updated 6 years ago
- The pytorch implementation of paper: A Graph-Enhanced Click Model for Web Search☆15Nov 17, 2021Updated 4 years ago
- Official PyTorch implementation of NeurIPS 2022 paper "Invertible Monotone Operators for Normalizing Flows"