MingSun-Tse / Awesome-Pruning-at-InitializationLinks
[IJCAI'22 Survey] Recent Advances on Neural Network Pruning at Initialization.
☆59Updated 2 years ago
Alternatives and similar repositories for Awesome-Pruning-at-Initialization
Users that are interested in Awesome-Pruning-at-Initialization are comparing it to the libraries listed below
Sorting:
- Soft Threshold Weight Reparameterization for Learnable Sparsity☆90Updated 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
- [Neurips 2021] Sparse Training via Boosting Pruning Plasticity with Neuroregeneration☆31Updated 2 years ago
- A generic code base for neural network pruning, especially for pruning at initialization.☆31Updated 3 years ago
- [ICLR'23] Trainability Preserving Neural Pruning (PyTorch)☆33Updated 2 years ago
- Reproducing RigL (ICML 2020) as a part of ML Reproducibility Challenge 2020☆29Updated 3 years ago
- ☆32Updated 3 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
- [ICML2022] Training Your Sparse Neural Network Better with Any Mask. Ajay Jaiswal, Haoyu Ma, Tianlong Chen, ying Ding, and Zhangyang Wang☆30Updated 3 years ago
- Pytorch implementation of the paper "SNIP: Single-shot Network Pruning based on Connection Sensitivity" by Lee et al.☆111Updated 6 years ago
- [ICLR 2022] The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training by Shiwei Liu, Tianlo…☆77Updated 2 years ago
- Comparison of method "Pruning at initialization prior to training" (Synflow/SNIP/GraSP) in PyTorch☆17Updated last year
- [ICLR-2020] Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers.☆31Updated 5 years ago
- In progress.☆66Updated last year
- Pytorch implementation of our paper accepted by TPAMI 2023 — Lottery Jackpots Exist in Pre-trained Models☆34Updated 2 years ago
- [ICLR 2022] "Unified Vision Transformer Compression" by Shixing Yu*, Tianlong Chen*, Jiayi Shen, Huan Yuan, Jianchao Tan, Sen Yang, Ji Li…☆53Updated last year
- Code for "Picking Winning Tickets Before Training by Preserving Gradient Flow" https://openreview.net/pdf?id=SkgsACVKPH☆105Updated 5 years ago
- Code for Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot☆41Updated 4 years ago
- [ICLR'21] Neural Pruning via Growing Regularization (PyTorch)☆82Updated 4 years ago
- ☆43Updated last year
- [ICLR 2022] "Learning Pruning-Friendly Networks via Frank-Wolfe: One-Shot, Any-Sparsity, and No Retraining" by Lu Miao*, Xiaolong Luo*, T…☆32Updated 3 years ago
- [Preprint] Why is the State of Neural Network Pruning so Confusing? On the Fairness, Comparison Setup, and Trainability in Network Prunin…☆40Updated last month
- Code accompanying the NeurIPS 2020 paper: WoodFisher (Singh & Alistarh, 2020)☆53Updated 4 years ago
- [ICML 2022] "Coarsening the Granularity: Towards Structurally Sparse Lottery Tickets" by Tianlong Chen, Xuxi Chen, Xiaolong Ma, Yanzhi Wa…☆33Updated 2 years ago
- Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection☆21Updated 4 years ago
- Official PyTorch Implementation of HELP: Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning (NeurIPS 2021 Spotlight…☆63Updated last year
- [ICLR 2023] "Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together!" Shiwei Liu, Tianlong Chen, Zhenyu Zhang, Xuxi Chen…☆28Updated 2 years ago
- Model compression by constrained optimization, using the Learning-Compression (LC) algorithm☆72Updated 3 years ago
- ☆25Updated 3 years ago
- Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples [NeurIPS 2021]☆33Updated 3 years ago