VITA-Group / Structure-LTHLinks
[ICML 2022] "Coarsening the Granularity: Towards Structurally Sparse Lottery Tickets" by Tianlong Chen, Xuxi Chen, Xiaolong Ma, Yanzhi Wang, Zhangyang Wang.
☆33Updated 2 years ago
Alternatives and similar repositories for Structure-LTH
Users that are interested in Structure-LTH are comparing it to the libraries listed below
Sorting:
- [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
- [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
- ☆43Updated last year
- [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
- Reproducing RigL (ICML 2020) as a part of ML Reproducibility Challenge 2020☆28Updated 3 years ago
- Code repo for the paper BiT Robustly Binarized Multi-distilled Transformer☆111Updated 2 years ago
- [ICLR 2022] "Unified Vision Transformer Compression" by Shixing Yu*, Tianlong Chen*, Jiayi Shen, Huan Yuan, Jianchao Tan, Sen Yang, Ji Li…☆54Updated last year
- DropIT: Dropping Intermediate Tensors for Memory-Efficient DNN Training (ICLR 2023)☆31Updated 2 years ago
- Soft Threshold Weight Reparameterization for Learnable Sparsity☆91Updated 2 years ago
- A library for researching neural networks compression and acceleration methods.☆139Updated 2 weeks ago
- [ICLR'23] Trainability Preserving Neural Pruning (PyTorch)☆34Updated 2 years ago
- [IJCAI'22 Survey] Recent Advances on Neural Network Pruning at Initialization.☆59Updated last year
- [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
- [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
- ☆214Updated 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
- ☆10Updated 3 years ago
- A research library for pytorch-based neural network pruning, compression, and more.☆162Updated 2 years ago
- Code for ICML 2022 paper "SPDY: Accurate Pruning with Speedup Guarantees"☆20Updated 2 years ago
- Neuron Merging: Compensating for Pruned Neurons (NeurIPS 2020)☆43Updated 4 years ago
- Code accompanying the NeurIPS 2020 paper: WoodFisher (Singh & Alistarh, 2020)☆53Updated 4 years ago
- Code for Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot☆42Updated 4 years ago
- [ICLR 2022] The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training by Shiwei Liu, Tianlo…☆76Updated 2 years ago
- ☆19Updated 3 years ago
- [ICLR 2021] "Long Live the Lottery: The Existence of Winning Tickets in Lifelong Learning" by Tianlong Chen*, Zhenyu Zhang*, Sijia Liu, S…☆25Updated 3 years ago
- [NeurIPS 2022 Spotlight] This is the official PyTorch implementation of "EcoFormer: Energy-Saving Attention with Linear Complexity"☆74Updated 2 years ago
- Factorized Neural Layers☆30Updated 2 years ago
- ☆59Updated last year
- Code for reproducing "AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks" (NeurIPS 2021)☆23Updated 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 week