team-approx-bayes / fromp
Contains code for the NeurIPS 2020 paper by Pan et al., "Continual Deep Learning by FunctionalRegularisation of Memorable Past"
☆44Updated 4 years ago
Alternatives and similar repositories for fromp:
Users that are interested in fromp are comparing it to the libraries listed below
- Code for Knowledge-Adaptation Priors based on the NeurIPS 2021 paper by Khan and Swaroop.☆16Updated 3 years ago
- Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"☆33Updated 2 years ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 5 years ago
- ☆53Updated 6 months ago
- Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'☆27Updated 3 years ago
- Code to implement the AND-mask and geometric mean to do gradient based optimization, from the paper "Learning explanations that are hard …☆39Updated 4 years ago
- Laplace Redux -- Effortless Bayesian Deep Learning☆41Updated last year
- PyTorch implementation of Stein Variational Gradient Descent☆42Updated last year
- AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'☆88Updated 4 years ago
- ☆34Updated 3 years ago
- Toy datasets to evaluate algorithms for domain generalization and invariance learning.☆42Updated 3 years ago
- Repository for theory and methods for Out-of-Distribution (OoD) generalization☆63Updated 2 years ago
- Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)☆81Updated 4 years ago
- Distributional and Outlier Robust Optimization (ICML 2021)☆26Updated 3 years ago
- PyTorch implementation of FIM and empirical FIM☆58Updated 6 years ago
- Simple data balancing baselines for worst-group-accuracy benchmarks.☆41Updated last year
- Implementation of the models and datasets used in "An Information-theoretic Approach to Distribution Shifts"☆25Updated 3 years ago
- Code for Accelerated Linearized Laplace Approximation for Bayesian Deep Learning (ELLA, NeurIPS 22')☆16Updated 2 years ago
- Code for "Function Space Particle Optimization for Bayesian Neural Networks"☆17Updated 2 years ago
- Stochastic Gradient Langevin Dynamics for Bayesian learning☆30Updated 3 years ago
- Coresets via Bilevel Optimization☆65Updated 4 years ago
- ☆31Updated 2 years ago
- Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)☆20Updated 5 years ago
- Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019☆41Updated 2 years ago
- Rethinking Bias-Variance Trade-off for Generalization of Neural Networks☆49Updated 3 years ago
- Implementation of the Functional Neural Process models☆43Updated 4 years ago
- Official Release of "Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling"☆49Updated 4 years ago
- Natural Gradient, Variational Inference☆29Updated 5 years ago
- Code for the paper "Bayesian Neural Network Priors Revisited"☆56Updated 3 years ago
- Code to accompany the paper Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning☆33Updated 4 years ago