Slice-and-Pack: Tailoring Deep Models for Customized Requirements
Authors: Ruice Rao, Dingwei Li, Ming Li
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical evaluations show that the S&P can generate highly accurate packed models and expand the market s capacity by many times. We conducted extensive experiments on various datasets for image classification and sentiment analysis. Our code was implemented by Py Torch and executed on an NVIDIA A100 40GB PCIe GPU with AMD EPYC 7H12 64-Core Processor. The implement details are provided in Appendix C. Experimental Results |
| Researcher Affiliation | Academia | 1National Key Laboratory for Novel Software Technology, Nanjing University, China 2School of Artificial Intelligence, Nanjing University, China EMAIL |
| Pseudocode | Yes | The algorithm is shown in Appendix A. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing code for the described methodology, nor does it include a link to a code repository. It only mentions implementation details and the tools used for execution. |
| Open Datasets | Yes | We conducted a series of experiments on four different datasets: CIFAR10, CIFAR100 (Krizhevsky, Hinton et al. 2009), TREC (Hovy et al. 2001), and SST-5 (Socher et al. 2013). |
| Dataset Splits | Yes | CIFAR10 and CIFAR100 consist of 50,000 images for training and 10,000 for testing. The TREC Question Classification dataset contains 5,500 sentences in the training set and another 500 in the test set, with 6 classes. SST-5 dataset consists of 8,544 sentences in the training set and another 2,210 in the test set, with 5 classes. |
| Hardware Specification | Yes | Our code was implemented by Py Torch and executed on an NVIDIA A100 40GB PCIe GPU with AMD EPYC 7H12 64-Core Processor. |
| Software Dependencies | No | Our code was implemented by Py Torch and executed on an NVIDIA A100 40GB PCIe GPU with AMD EPYC 7H12 64-Core Processor. While 'Py Torch' is mentioned, no specific version number is provided for the software dependency. |
| Experiment Setup | Yes | For each original model, we randomly sample k samples per class from corresponding dataset. More details and experiments with different datasets and architectures are in Appendix D. The experimental tables (e.g., Table 2) show results for specific k values (k = 5, k = 10, k = 20). |