f-dangel / curvlinops
PyTorch linear operators for curvature matrices (Hessian, Fisher/GGN, KFAC, ...)
☆31Updated this week
Alternatives and similar repositories for curvlinops:
Users that are interested in curvlinops are comparing it to the libraries listed below
- IVON optimizer for neural networks based on variational learning.☆59Updated 3 months ago
- Laplace Redux -- Effortless Bayesian Deep Learning☆41Updated last year
- Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)☆20Updated 5 years ago
- Normalizing Flows using JAX☆82Updated last year
- Parameter-Free Optimizers for Pytorch☆113Updated 9 months ago
- ☆53Updated 6 months ago
- Contains code for the NeurIPS 2020 paper by Pan et al., "Continual Deep Learning by FunctionalRegularisation of Memorable Past"☆44Updated 4 years ago
- Agustinus' very opiniated publication-ready plotting library☆61Updated 2 weeks ago
- Hessian spectral density estimation in TF and Jax☆121Updated 4 years ago
- Sketched matrix decompositions for PyTorch☆69Updated 3 weeks ago
- Code for the article "What if Neural Networks had SVDs?", to be presented as a spotlight paper at NeurIPS 2020.☆72Updated 6 months ago
- {KFAC,EKFAC,Diagonal,Implicit} Fisher Matrices and finite width NTKs in PyTorch☆211Updated this week
- Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"☆33Updated 2 years ago
- Bayesianize: A Bayesian neural network wrapper in pytorch☆88Updated 9 months ago
- Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks☆9Updated 2 years ago
- Sampling with gradient-based Markov Chain Monte Carlo approaches☆97Updated 10 months ago
- Riemannian Optimization Using JAX☆48Updated last year
- ASDL: Automatic Second-order Differentiation Library for PyTorch☆183Updated 2 months ago
- [TMLR 2022] Curvature access through the generalized Gauss-Newton's low-rank structure: Eigenvalues, eigenvectors, directional derivative…☆17Updated last year
- ☆98Updated 3 years ago
- Source code for my PhD thesis: Backpropagation Beyond the Gradient☆20Updated last year
- Algorithms for computations on random manifolds made easier☆87Updated last year
- Code for Knowledge-Adaptation Priors based on the NeurIPS 2021 paper by Khan and Swaroop.☆16Updated 3 years ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 5 years ago
- ☆67Updated 5 years ago
- Lightweight library of stochastic gradient MCMC algorithms written in JAX.☆100Updated last year
- [ICML 2024] SINGD: KFAC-like Structured Inverse-Free Natural Gradient Descent (http://arxiv.org/abs/2312.05705)☆21Updated 3 months ago
- All You Need is a Good Functional Prior for Bayesian Deep Learning (JMLR 2022)☆21Updated 2 years ago
- Simple (and cheap!) neural network uncertainty estimation☆61Updated last week
- Supporing code for the paper "Bayesian Model Selection, the Marginal Likelihood, and Generalization".☆35Updated 2 years ago