mccrearyd / rigl-torchLinks
Lightweight torch implementation of rigl, a sparse-to-sparse optimizer.
☆57Updated 3 years ago
Alternatives and similar repositories for rigl-torch
Users that are interested in rigl-torch 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
- Code accompanying the NeurIPS 2020 paper: WoodFisher (Singh & Alistarh, 2020)☆52Updated 4 years ago
- Soft Threshold Weight Reparameterization for Learnable Sparsity☆91Updated 2 years ago
- ☆76Updated last year
- [IJCAI'22 Survey] Recent Advances on Neural Network Pruning at Initialization.☆59Updated last year
- Code for Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot☆42Updated 4 years ago
- Implementation of Continuous Sparsification, a method for pruning and ticket search in deep networks☆33Updated 3 years ago
- [ICML2022] Training Your Sparse Neural Network Better with Any Mask. Ajay Jaiswal, Haoyu Ma, Tianlong Chen, ying Ding, and Zhangyang Wang☆28Updated 2 years ago
- ☆14Updated 4 years ago
- Code release for REPAIR: REnormalizing Permuted Activations for Interpolation Repair☆47Updated last year
- [Neurips 2021] Sparse Training via Boosting Pruning Plasticity with Neuroregeneration☆31Updated 2 years ago
- [ICML 2021] "Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training" by Shiwei Liu, Lu Yin, De…☆45Updated last year
- Pytorch implementation of KFAC and E-KFAC (Natural Gradient).☆132Updated 5 years ago
- ☆68Updated 6 months ago
- ☆208Updated 2 years ago
- Identify a binary weight or binary weight and activation subnetwork within a randomly initialized network by only pruning and binarizing …☆52Updated 3 years ago
- Prospect Pruning: Finding Trainable Weights at Initialization Using Meta-Gradients☆31Updated 3 years ago
- ☆190Updated 4 years ago
- ☆226Updated 11 months ago
- PyTorch implementation of Mixer-nano (#parameters is 0.67M, originally Mixer-S/16 has 18M) with 90.83 % acc. on CIFAR-10. Training from s…☆32Updated 3 years ago
- Reproducing RigL (ICML 2020) as a part of ML Reproducibility Challenge 2020☆28Updated 3 years ago
- Pytorch implementation of the paper "SNIP: Single-shot Network Pruning based on Connection Sensitivity" by Lee et al.☆108Updated 6 years ago
- ☆67Updated 6 years ago
- Spartan is an algorithm for training sparse neural network models. This repository accompanies the paper "Spartan Differentiable Sparsity…☆24Updated 2 years ago
- [ICLR 2023] "Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together!" Shiwei Liu, Tianlong Chen, Zhenyu Zhang, Xuxi Chen…☆28Updated last year
- ☆36Updated 2 years ago
- [ICLR 2022] "Learning Pruning-Friendly Networks via Frank-Wolfe: One-Shot, Any-Sparsity, and No Retraining" by Lu Miao*, Xiaolong Luo*, T…☆30Updated 3 years ago
- ☆58Updated 2 years ago
- Train ImageNet *fast* in 500 lines of code with FFCV☆143Updated last year
- ☆17Updated 2 years ago