boone891214 / MEST
[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
Alternatives and similar repositories for MEST:
Users that are interested in MEST 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…☆46Updated last year
- [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
- A generic code base for neural network pruning, especially for pruning at initialization.☆30Updated 2 years ago
- [ICLR-2020] Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers.☆31Updated 5 years ago
- [Neurips 2021] Sparse Training via Boosting Pruning Plasticity with Neuroregeneration☆32Updated 2 years ago
- [ICML2022] Training Your Sparse Neural Network Better with Any Mask. Ajay Jaiswal, Haoyu Ma, Tianlong Chen, ying Ding, and Zhangyang Wang☆27Updated 2 years ago
- This is the official implementation of the ICML 2023 paper - Can Forward Gradient Match Backpropagation ?☆12Updated last year
- [ICML 2021] "Efficient Lottery Ticket Finding: Less Data is More" by Zhenyu Zhang*, Xuxi Chen*, Tianlong Chen*, Zhangyang Wang☆25Updated 3 years ago
- Reproducing RigL (ICML 2020) as a part of ML Reproducibility Challenge 2020☆28Updated 3 years ago
- Implementation of Continuous Sparsification, a method for pruning and ticket search in deep networks☆33Updated 2 years ago
- Comparison of method "Pruning at initialization prior to training" (Synflow/SNIP/GraSP) in PyTorch☆15Updated 10 months ago
- ☆30Updated 3 years ago
- ☆23Updated 3 years ago
- Code for Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot☆42Updated 4 years ago
- [Preprint] Why is the State of Neural Network Pruning so Confusing? On the Fairness, Comparison Setup, and Trainability in Network Prunin…☆40Updated 2 years ago
- Data-free knowledge distillation using Gaussian noise (NeurIPS paper)☆15Updated last year
- [ICML 2021] "Selfish Sparse RNN Training" by Shiwei Liu, Decebal Constantin Mocanu, Yulong Pei, Mykola Pechenizkiy☆10Updated 3 years ago
- Prospect Pruning: Finding Trainable Weights at Initialization Using Meta-Gradients☆31Updated 2 years ago
- Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection☆21Updated 4 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 Oral] "CATE: Computation-aware Neural Architecture Encoding with Transformers" by Shen Yan, Kaiqiang Song, Fei Liu, Mi Zhang☆19Updated 3 years ago
- [IJCAI'22 Survey] Recent Advances on Neural Network Pruning at Initialization.☆59Updated last year
- Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples [NeurIPS 2021]☆31Updated 3 years ago
- [ICLR'23] Trainability Preserving Neural Pruning (PyTorch)☆32Updated last year
- ☆25Updated 3 years ago
- ICLR 2022 (Spolight): Continual Learning With Filter Atom Swapping☆16Updated last year
- [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
- [ICLR 2023] 'Revisiting Pruning At Initialization Through The Lens of Ramanujan Graph" by Duc Hoang, Shiwei Liu, Radu Marculescu, Atlas W…☆12Updated last year
- [ICML 2022] "Coarsening the Granularity: Towards Structurally Sparse Lottery Tickets" by Tianlong Chen, Xuxi Chen, Xiaolong Ma, Yanzhi Wa…☆32Updated last year
- Soft Threshold Weight Reparameterization for Learnable Sparsity☆88Updated 2 years ago