amirgholami / HessianFlow
☆82Updated 5 years ago
Alternatives and similar repositories for HessianFlow:
Users that are interested in HessianFlow are comparing it to the libraries listed below
- Code for "Picking Winning Tickets Before Training by Preserving Gradient Flow" https://openreview.net/pdf?id=SkgsACVKPH☆101Updated 5 years ago
- This repository is no longer maintained. Check☆81Updated 4 years ago
- Pytorch implementation of KFAC and E-KFAC (Natural Gradient).☆130Updated 5 years ago
- Repository containing Pytorch code for EKFAC and K-FAC perconditioners.☆141Updated last year
- Code for Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot☆42Updated 4 years ago
- ☆74Updated 5 years ago
- Code for "EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis" https://arxiv.org/abs/1905.05934☆112Updated 4 years ago
- Code for paper "SWALP: Stochastic Weight Averaging forLow-Precision Training".☆62Updated 5 years ago
- Code for the paper "Training Binary Neural Networks with Bayesian Learning Rule☆38Updated 3 years ago
- NTK reading group☆88Updated 5 years ago
- ☆70Updated 4 years ago
- ☆27Updated 6 years ago
- SNIP: SINGLE-SHOT NETWORK PRUNING BASED ON CONNECTION SENSITIVITY☆112Updated 5 years ago
- ☆67Updated 4 years ago
- Code accompanying the NeurIPS 2020 paper: WoodFisher (Singh & Alistarh, 2020)☆48Updated 3 years ago
- Reproduction and analysis of SNIP paper☆30Updated 5 years ago
- ☆58Updated last year
- Code release for "Adversarial Robustness vs Model Compression, or Both?"☆91Updated 3 years ago
- ☆156Updated 2 years ago
- hessian in pytorch☆186Updated 4 years ago
- Convolutional Neural Tangent Kernel☆109Updated 5 years ago
- Pytorch implementation of the paper "SNIP: Single-shot Network Pruning based on Connection Sensitivity" by Lee et al.☆106Updated 5 years ago
- ☆121Updated 8 months ago
- [JMLR] TRADES + random smoothing for certifiable robustness☆14Updated 4 years ago
- Lookahead: A Far-sighted Alternative of Magnitude-based Pruning (ICLR 2020)☆33Updated 4 years ago
- Measurements of Three-Level Hierarchical Structure in the Outliers in the Spectrum of Deepnet Hessians (ICML 2019)☆17Updated 5 years ago
- DeepHoyer: Learning Sparser Neural Network with Differentiable Scale-Invariant Sparsity Measures☆31Updated 4 years ago
- Lipschitz Neural Networks described in "Sorting Out Lipschitz Function Approximation" (ICML 2019).☆55Updated 4 years ago
- Code for Self-Tuning Networks (ICLR 2019) https://arxiv.org/abs/1903.03088☆53Updated 5 years ago
- ☆53Updated 5 years ago