VITA-Group / GraNet
[Neurips 2021] Sparse Training via Boosting Pruning Plasticity with Neuroregeneration
☆31Updated 2 years ago
Alternatives and similar repositories for GraNet:
Users that are interested in GraNet are comparing it to the libraries listed below
- [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
- Reproducing RigL (ICML 2020) as a part of ML Reproducibility Challenge 2020☆28Updated 3 years ago
- [IJCAI'22 Survey] Recent Advances on Neural Network Pruning at Initialization.☆59Updated last year
- Soft Threshold Weight Reparameterization for Learnable Sparsity☆89Updated 2 years ago
- [ICLR-2020] Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers.☆31Updated 5 years ago
- ☆43Updated last year
- ☆30Updated 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
- A generic code base for neural network pruning, especially for pruning at initialization.☆30Updated 2 years ago
- [NeurIPS‘2021] "MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge", Geng Yuan, Xiaolong Ma, Yanzhi Wang et al…☆18Updated 3 years ago
- ☆26Updated 2 years ago
- Generic Neural Architecture Search via Regression (NeurIPS'21 Spotlight)☆36Updated 2 years ago
- Comparison of method "Pruning at initialization prior to training" (Synflow/SNIP/GraSP) in PyTorch☆16Updated 11 months ago
- Code for "Picking Winning Tickets Before Training by Preserving Gradient Flow" https://openreview.net/pdf?id=SkgsACVKPH☆103Updated 5 years ago
- Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples [NeurIPS 2021]☆31Updated 3 years ago
- Implementation of Continuous Sparsification, a method for pruning and ticket search in deep networks☆33Updated 2 years ago
- ☆25Updated 3 years ago
- ☆24Updated 3 years ago
- Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection☆21Updated 4 years ago
- Any-Precision Deep Neural Networks (AAAI 2021)☆60Updated 5 years ago
- Lightweight torch implementation of rigl, a sparse-to-sparse optimizer.☆56Updated 3 years ago
- [ICLR 2022] "Learning Pruning-Friendly Networks via Frank-Wolfe: One-Shot, Any-Sparsity, and No Retraining" by Lu Miao*, Xiaolong Luo*, T…☆29Updated 3 years ago
- [ICML 2021] "Double-Win Quant: Aggressively Winning Robustness of Quantized DeepNeural Networks via Random Precision Training and Inferen…☆13Updated 3 years ago
- Prospect Pruning: Finding Trainable Weights at Initialization Using Meta-Gradients☆31Updated 3 years ago
- [NeurIPS 2021] “Stronger NAS with Weaker Predictors“, Junru Wu, Xiyang Dai, Dongdong Chen, Yinpeng Chen, Mengchen Liu, Ye Yu, Zhangyang W…☆27Updated 2 years ago
- Implementation of Effective Sparsification of Neural Networks with Global Sparsity Constraint☆31Updated 3 years ago
- Code for ICCV23 paper "Automatic network pruning via Hilbert Schmidt independence criterion lasso under information bottleneck principle"☆18Updated last year
- ☆39Updated 2 years ago
- [ICLR 2023] 'Revisiting Pruning At Initialization Through The Lens of Ramanujan Graph" by Duc Hoang, Shiwei Liu, Radu Marculescu, Atlas W…☆13Updated last year
- This repository contains the publishable code for CVPR 2021 paper TransNAS-Bench-101: Improving Transferrability and Generalizability of …☆22Updated 2 years ago