szmikler / snip-pruningLinks
SNIP: SINGLE-SHOT NETWORK PRUNING
☆31Updated 7 months ago
Alternatives and similar repositories for snip-pruning
Users that are interested in snip-pruning are comparing it to the libraries listed below
Sorting:
- Code for "Picking Winning Tickets Before Training by Preserving Gradient Flow" https://openreview.net/pdf?id=SkgsACVKPH☆105Updated 5 years ago
- ☆143Updated 2 years ago
- Pytorch implementation of the paper "SNIP: Single-shot Network Pruning based on Connection Sensitivity" by Lee et al.☆111Updated 6 years ago
- ☆83Updated 5 years ago
- ☆227Updated last year
- SNIP: SINGLE-SHOT NETWORK PRUNING BASED ON CONNECTION SENSITIVITY☆115Updated 6 years ago
- ☆157Updated 3 years ago
- ☆193Updated 4 years ago
- Mode Connectivity and Fast Geometric Ensembles in PyTorch☆278Updated 3 years ago
- A PyTorch implementation of the paper "Decoupled Parallel Backpropagation with Convergence Guarantee"☆29Updated 7 years ago
- ☆69Updated 5 years ago
- Learning Sparse Neural Networks through L0 regularization☆244Updated 5 years ago
- A drop-in replacement for CIFAR-10.☆245Updated 4 years ago
- Code for the paper "Understanding Generalization through Visualizations"☆64Updated 4 years ago
- [ICLR 2020] Drawing Early-Bird Tickets: Toward More Efficient Training of Deep Networks☆139Updated 4 years ago
- Discovering Neural Wirings (https://arxiv.org/abs/1906.00586)☆136Updated 5 years ago
- Gradient Starvation: A Learning Proclivity in Neural Networks☆61Updated 4 years ago
- Code release for paper "Random Search and Reproducibility for NAS"☆167Updated 6 years ago
- Efficient PyTorch Hessian eigendecomposition tools!☆379Updated last year
- [ICLR 2020] NAS evaluation is frustratingly hard☆149Updated 2 years ago
- Code for "Supermasks in Superposition"☆124Updated 2 years ago
- PyTorch code for training neural networks without global back-propagation☆165Updated 6 years ago
- Code accompanying the NeurIPS 2020 paper: WoodFisher (Singh & Alistarh, 2020)☆53Updated 4 years ago
- ☆55Updated 5 years ago
- Compressing Neural Networks using the Variational Information Bottleneck☆66Updated 3 years ago
- ☆30Updated 5 years ago
- Code for "EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis" https://arxiv.org/abs/1905.05934☆113Updated 5 years ago
- Soft Threshold Weight Reparameterization for Learnable Sparsity☆90Updated 2 years ago
- Code release for "Adversarial Robustness vs Model Compression, or Both?"☆90Updated 4 years ago
- A Re-implementation of Fixed-update Initialization☆155Updated 6 years ago