Shiweiliuiiiiiii / In-Time-Over-ParameterizationLinks
[ICML 2021] "Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training" by Shiwei Liu, Lu Yin, Decebal Constantin Mocanu, Mykola Pechenizkiy
☆45Updated last year
Alternatives and similar repositories for In-Time-Over-Parameterization
Users that are interested in In-Time-Over-Parameterization are comparing it to the libraries listed below
Sorting:
- [Neurips 2021] Sparse Training via Boosting Pruning Plasticity with Neuroregeneration☆31Updated 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
- [IJCAI'22 Survey] Recent Advances on Neural Network Pruning at Initialization.☆59Updated last year
- Soft Threshold Weight Reparameterization for Learnable Sparsity☆90Updated 2 years ago
- Implementation of Continuous Sparsification, a method for pruning and ticket search in deep networks☆33Updated 2 years ago
- Reproducing RigL (ICML 2020) as a part of ML Reproducibility Challenge 2020☆28Updated 3 years ago
- [ICLR-2020] Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers.☆31Updated 5 years ago
- A generic code base for neural network pruning, especially for pruning at initialization.☆30Updated 2 years ago
- ☆30Updated 3 years ago
- Code and checkpoints of compressed networks for the paper titled "HYDRA: Pruning Adversarially Robust Neural Networks" (NeurIPS 2020) (ht…☆92Updated 2 years ago
- Code accompanying the NeurIPS 2020 paper: WoodFisher (Singh & Alistarh, 2020)☆52Updated 4 years ago
- Comparison of method "Pruning at initialization prior to training" (Synflow/SNIP/GraSP) in PyTorch☆16Updated last year
- [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
- [ICLR'23] Trainability Preserving Neural Pruning (PyTorch)☆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☆28Updated 2 years ago
- Prospect Pruning: Finding Trainable Weights at Initialization Using Meta-Gradients☆31Updated 3 years ago
- [ICLR'21] Neural Pruning via Growing Regularization (PyTorch)☆83Updated 3 years ago
- [ICLR 2022] The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training by Shiwei Liu, Tianlo…☆73Updated 2 years ago
- ☆43Updated last year
- Code for reproducing "AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks" (NeurIPS 2021)☆23Updated 3 years ago
- Generic Neural Architecture Search via Regression (NeurIPS'21 Spotlight)☆36Updated 2 years ago
- ☆24Updated 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
- In progress.☆64Updated last year
- Code for Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot☆42Updated 4 years ago
- Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples [NeurIPS 2021]☆32Updated 3 years ago
- ☆25Updated 3 years ago
- Implementation of Effective Sparsification of Neural Networks with Global Sparsity Constraint☆31Updated 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 2 years ago
- This is the official PyTorch implementation for "Sharpness-aware Quantization for Deep Neural Networks".☆43Updated 3 years ago