team-approx-bayes / kpriors
Code for Knowledge-Adaptation Priors based on the NeurIPS 2021 paper by Khan and Swaroop.
☆16Updated 3 years ago
Alternatives and similar repositories for kpriors:
Users that are interested in kpriors are comparing it to the libraries listed below
- Contains code for the NeurIPS 2020 paper by Pan et al., "Continual Deep Learning by FunctionalRegularisation of Memorable Past"☆44Updated 4 years ago
- Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"☆33Updated 2 years ago
- Code for Accelerated Linearized Laplace Approximation for Bayesian Deep Learning (ELLA, NeurIPS 22')☆16Updated 2 years ago
- Distributional and Outlier Robust Optimization (ICML 2021)☆26Updated 3 years ago
- ☆15Updated 2 years ago
- ☆53Updated 6 months ago
- Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)☆20Updated 5 years ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 5 years ago
- Principled learning method for Wasserstein distributionally robust optimization with local perturbations (ICML 2020)☆21Updated last year
- Pytorch implementation of neural processes and variants☆27Updated 6 months ago
- Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods☆18Updated 3 years ago
- ☆20Updated 4 years ago
- Bayesian active learning with EPIG data acquisition☆28Updated 2 weeks ago
- Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'☆27Updated 3 years ago
- Laplace Redux -- Effortless Bayesian Deep Learning☆41Updated last year
- Monotone operator equilibrium networks☆51Updated 4 years ago
- Simple data balancing baselines for worst-group-accuracy benchmarks.☆41Updated last year
- PyTorch linear operators for curvature matrices (Hessian, Fisher/GGN, KFAC, ...)☆31Updated this week
- Code accompanying VarGrad: A Low-Variance Gradient Estimator for Variational Inference☆12Updated 4 years ago
- Stochastic Gradient Langevin Dynamics for Bayesian learning☆30Updated 3 years ago
- PyTorch implementation of the paper "Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization" (ICLR 2021)☆23Updated 2 years ago
- ☆23Updated 3 years ago
- Official Release of "Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling"☆49Updated 4 years ago
- Code for the paper "Bayesian Neural Network Priors Revisited"☆56Updated 3 years ago
- Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks☆9Updated 2 years ago
- Featurized Density Ratio Estimation☆20Updated 3 years ago
- csl: PyTorch-based Constrained Learning☆12Updated 2 years ago
- Sinkhorn Barycenters via Frank-Wolfe algorithm☆24Updated 5 years ago
- ☆31Updated 4 years ago
- Limitations of the Empirical Fisher Approximation☆47Updated 4 years ago