Hi, @he-y, very thanks for your great list from which I learned a lot!

I am also working on pruning and I would like to contribute the full list of pruning papers and their codes at ICML 2023 (10 in total) as follows:

1. UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers | F | PyTorch(Author)
2. Gradient-Free Structured Pruning with Unlabeled Data | F |
3. Reconstructive Neuron Pruning for Backdoor Defense | F | PyTorch(Author)
4. UPSCALE: Unconstrained Channel Pruning | F | PyTorch(Author)
5. SparseGPT: Massive Language Models Can be Accurately Pruned in One-Shot | W | PyTorch(Author)
6. Why Random Pruning Is All We Need to Start Sparse | W | PyTorch(Author)
7. Fast as CHITA: Neural Network Pruning with Combinatorial Optimization | W | PyTorch(Author)
8. A Three-regime Model of Network Pruning | W | PyTorch(Author)
9. Instant Soup: Cheap Pruning Ensembles in A Single Pass Can Draw Lottery Tickets from Large Models | W | PyTorch(Author)
10. Pruning via Sparsity-indexed ODE: a Continuous Sparsity Viewpoint | W | PyTorch(Author)

Could you please review this pr? Please let me know if there is any further information I should provide for a final merge.