Pruning via Sparsity-indexed ODE: a Continuous Sparsity Viewpoint
Authors: Zhanfeng Mo, Haosen Shi, Sinno Jialin Pan
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical experiments show that PSO achieves either better or comparable performance compared to state-of-the-art baselines across various pruning settings. |
| Researcher Affiliation | Collaboration | 1School of Computer Science and Engineering, NTU, Singapore 2Continental-NTU Corporate Lab, NTU Singapore 3Department of Computer Science and Engineering, Chinese University of Hong Kong. |
| Pseudocode | Yes | Algorithm 1 Pruning via Sparsity-indexed ODE (PSO) |
| Open Source Code | Yes | Our implementations are now available on Git Hub1. 1https://github.com/mzf666/sparsity-indexed-ode |
| Open Datasets | Yes | CIFAR-10 / 100 benchmarks (Krizhevsky, 2009) using different model architectures (Res Net-20 (He et al., 2016), VGG16-bn (Simonyan & Zisserman, 2015) and WRN-20 (Zagoruyko & Komodakis, 2016)) |
| Dataset Splits | No | The paper uses standard public datasets (e.g., CIFAR-10/100, ImageNet) which have predefined train/test splits. However, it does not explicitly provide details about a specific validation set split (e.g., percentage or sample count) used during training or fine-tuning. |
| Hardware Specification | No | The paper mentions running experiments on various datasets and models but does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for these experiments. |
| Software Dependencies | No | The paper states that implementations are available on GitHub but does not list specific software dependencies with their version numbers within the text. |
| Experiment Setup | Yes | Table 6. Hyperparameter configurations for retraining procedure. VGG16-bn...Optimizer SGD-Momentum Training Epochs 100 Batch Size 64 Learning Rate 1e-2 Learning Rate Schedule Cosine Annealing Minimal Learning Rate and When to Reach 1e-6 last 10 epochs Evaluated Epochs last 10 epochs |