mariogeiger / hessianLinks
hessian in pytorch
☆187Updated 5 years ago
Alternatives and similar repositories for hessian
Users that are interested in hessian are comparing it to the libraries listed below
Sorting:
- Repository containing Pytorch code for EKFAC and K-FAC perconditioners.☆148Updated 2 years ago
- Efficient PyTorch Hessian eigendecomposition tools!☆381Updated last year
- Hypergradient descent☆147Updated last year
- PyTorch-SSO: Scalable Second-Order methods in PyTorch☆147Updated 2 years ago
- ☆133Updated 8 years ago
- Collection of algorithms for approximating Fisher Information Matrix for Natural Gradient (and second order method in general)☆141Updated 6 years ago
- This repository is no longer maintained. Check☆81Updated 5 years ago
- ☆157Updated 3 years ago
- Convolutional Neural Tangent Kernel☆112Updated 6 years ago
- paper lists and information on mean-field theory of deep learning☆78Updated 6 years ago
- Code for ICML 2018 paper on "Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam" by Khan, Nielsen, Tangkaratt, Lin, …☆112Updated 6 years ago
- Pytorch implementation of KFAC and E-KFAC (Natural Gradient).☆132Updated 6 years ago
- ☆36Updated 4 years ago
- Understanding normalizing flows☆132Updated 5 years ago
- Optimization with orthogonal constraints and on general manifolds☆131Updated 5 years ago
- A manifold optimization library for deep learning☆249Updated 4 years ago
- Code for Self-Tuning Networks (ICLR 2019) https://arxiv.org/abs/1903.03088☆54Updated 6 years ago
- Real NVP PyTorch a Minimal Working Example | Normalizing Flow☆142Updated 5 years ago
- Hessian spectral density estimation in TF and Jax☆124Updated 5 years ago
- Pytorch implementation of Block Neural Autoregressive Flow☆181Updated 4 years ago
- Optimizing control variates for black-box gradient estimation☆163Updated 6 years ago
- A PyTorch library for two-sample tests☆242Updated 2 years ago
- ☆171Updated last year
- Limitations of the Empirical Fisher Approximation☆48Updated 8 months ago
- Example code for the paper "Understanding deep learning requires rethinking generalization"☆178Updated 5 years ago
- ☆124Updated last year
- ☆181Updated 6 years ago
- Mode Connectivity and Fast Geometric Ensembles in PyTorch☆277Updated 3 years ago
- Sample code for running deterministic variational inference to train Bayesian neural networks☆100Updated 7 years ago
- Experiments for the paper "Exponential expressivity in deep neural networks through transient chaos"☆73Updated 9 years ago