AamirRaihan / SWAT
Official implementation of Neurips 2020 "Sparse Weight Activation Training" paper.
☆27Updated 3 years ago
Alternatives and similar repositories for SWAT:
Users that are interested in SWAT are comparing it to the libraries listed below
- ☆43Updated last year
- [ICLR 2021 Spotlight] "CPT: Efficient Deep Neural Network Training via Cyclic Precision" by Yonggan Fu, Han Guo, Meng Li, Xin Yang, Yinin…☆30Updated last year
- Code for ICML 2021 submission☆35Updated 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
- Post-training sparsity-aware quantization☆34Updated 2 years ago
- Official PyTorch Implementation of HELP: Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning (NeurIPS 2021 Spotlight…☆62Updated 7 months ago
- Implementation for the paper "Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization"☆74Updated 5 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
- Official PyTorch Implementation of "Learning Architectures for Binary Networks" (ECCV2020)☆26Updated 4 years ago
- ☆10Updated 3 years ago
- Code accompanying the NeurIPS 2020 paper: WoodFisher (Singh & Alistarh, 2020)☆48Updated 4 years ago
- [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
- Soft Threshold Weight Reparameterization for Learnable Sparsity☆88Updated 2 years ago
- Generic Neural Architecture Search via Regression (NeurIPS'21 Spotlight)☆36Updated 2 years ago
- ☆25Updated 3 years ago
- Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples [NeurIPS 2021]☆31Updated 3 years ago
- Code for Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot☆42Updated 4 years ago
- [IJCAI'22 Survey] Recent Advances on Neural Network Pruning at Initialization.☆59Updated last year
- [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
- ☆75Updated 2 years ago
- [ICML 2021] "Double-Win Quant: Aggressively Winning Robustness of Quantized DeepNeural Networks via Random Precision Training and Inferen…☆13Updated 3 years ago
- Codes for Accepted Paper : "MetaQuant: Learning to Quantize by Learning to Penetrate Non-differentiable Quantization" in NeurIPS 2019☆54Updated 4 years ago
- [ICML 2022] "Coarsening the Granularity: Towards Structurally Sparse Lottery Tickets" by Tianlong Chen, Xuxi Chen, Xiaolong Ma, Yanzhi Wa…☆32Updated last year
- [Neurips 2021] Sparse Training via Boosting Pruning Plasticity with Neuroregeneration☆32Updated 2 years ago
- This is the pytorch implementation for the paper: Generalizable Mixed-Precision Quantization via Attribution Rank Preservation, which is…☆25Updated 3 years ago
- A generic code base for neural network pruning, especially for pruning at initialization.☆30Updated 2 years ago
- [ICLR 2022] "Sparsity Winning Twice: Better Robust Generalization from More Efficient Training" by Tianlong Chen*, Zhenyu Zhang*, Pengjun…☆39Updated 3 years ago
- PyTorch implementation of Towards Efficient Training for Neural Network Quantization☆15Updated 5 years ago
- Reproducing RigL (ICML 2020) as a part of ML Reproducibility Challenge 2020☆28Updated 3 years ago
- code for the paper "A Statistical Framework for Low-bitwidth Training of Deep Neural Networks"☆28Updated 4 years ago