hoonyyhoon / Synflow_SNIP_GraSPLinks
Comparison of method "Pruning at initialization prior to training" (Synflow/SNIP/GraSP) in PyTorch
☆17Updated last year
Alternatives and similar repositories for Synflow_SNIP_GraSP
Users that are interested in Synflow_SNIP_GraSP are comparing it to the libraries listed below
Sorting:
- [IJCAI'22 Survey] Recent Advances on Neural Network Pruning at Initialization.☆59Updated last year
- Soft Threshold Weight Reparameterization for Learnable Sparsity☆91Updated 2 years ago
- [Neurips 2021] Sparse Training via Boosting Pruning Plasticity with Neuroregeneration☆31Updated 2 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
- [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☆29Updated 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
- Code for "Picking Winning Tickets Before Training by Preserving Gradient Flow" https://openreview.net/pdf?id=SkgsACVKPH☆106Updated 5 years ago
- 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]☆33Updated 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
- Code accompanying the NeurIPS 2020 paper: WoodFisher (Singh & Alistarh, 2020)☆53Updated 4 years ago
- A generic code base for neural network pruning, especially for pruning at initialization.☆31Updated 3 years ago
- SNIP: SINGLE-SHOT NETWORK PRUNING BASED ON CONNECTION SENSITIVITY☆115Updated 6 years ago
- ☆227Updated last year
- ☆32Updated 3 years ago
- ☆14Updated 4 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
- Prospect Pruning: Finding Trainable Weights at Initialization Using Meta-Gradients☆32Updated 3 years ago
- [ICLR-2020] Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers.☆31Updated 5 years ago
- Official PyTorch Implementation of HELP: Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning (NeurIPS 2021 Spotlight…☆63Updated last year
- Implementation of Continuous Sparsification, a method for pruning and ticket search in deep networks☆33Updated 3 years ago
- Implementation of Effective Sparsification of Neural Networks with Global Sparsity Constraint☆31Updated 3 years ago
- Code release for "Adversarial Robustness vs Model Compression, or Both?"☆91Updated 4 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
- Data-Free Network Quantization With Adversarial Knowledge Distillation PyTorch☆30Updated 4 years ago
- Any-Precision Deep Neural Networks (AAAI 2021)☆61Updated 5 years ago
- Model compression by constrained optimization, using the Learning-Compression (LC) algorithm☆72Updated 3 years ago
- Generic Neural Architecture Search via Regression (NeurIPS'21 Spotlight)☆36Updated 3 years ago
- Implementation for the paper "Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization"☆74Updated 5 years ago