Awesome
Sparsity-indexed-ODE
[ICML2023] Neural Pruning via Sparsity-indexed ODE: A Continuous Sparsity Viewpoint<br> Zhanfeng Mo<sup>1</sup>, Haosen Shi<sup>1,2</sup>, Sinno Jialin Pan<sup>1,3</sup><br> <sup>1</sup> <sub>School of Computer Science and Engineering, Nanyang Technological University</sub><br /> <sup>2</sup> <sub>Continental-NTU Corporate Lab, Nanyang Technological University</sub><br /> <sup>3</sup> <sub>Department of Computer Science and Engineering, Chinese University of Hong Kong. </sub><br />
Official implementation of "Neural Pruning via Sparsity-indexed ODE: A Continuous Sparsity Viewpoint, ICML 2023".
Environment
We provide a NVIDIA-Docker image to facilitate researchers in reproducing the results reported in our paper and conducting further studies using our code.
- Build docker image from our Dockerfile
bash ./build_docker_com.sh
- Two scripts
run_docker_(summary/tem).share provided for running the experiments and summarizing the results. Change the dirname in these two scripts to your local dirname.
bash ./run_docker_tem.sh
- More scripts are provided in
Scripts/(datasetname)/
Update
- upload the link of paper
15/06/2023 init readme and code
Contact
If you have any questions about this work, please feel easy to contact us (ZHANFENG001 (AT) ntu.edu.sg).
Thanks
This code is heavily borrowed from [SynFlow].
Citation
If you use this code for your research, please cite our paper, "Neural Pruning via Sparsity-indexed ODE: A Continuous Sparsity Viewpoint".