Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Towards Data-Agnostic Pruning At Initialization: What Makes a Good Sparse Mask?
Authors: Hoang Pham, The Anh Ta, Shiwei Liu, Lichuan Xiang, Dung Le, Hongkai Wen, Long Tran-Thanh
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | By conducting extensive experiments across different architectures and datasets, our results demonstrate that our approach outperforms state-of-the-art Pa I methods while it is able to discover subnetworks that have much lower inference FLOPs (up to 3.4 ). |
| Researcher Affiliation | Collaboration | Hoang Pham1, The-Anh Ta2, Shiwei Liu3,6, Lichuan Xiang4, Dung D. Le5, Hongkai Wen4, Long Tran-Thanh4 1 FPT Software AI Center, 2 CSIRO s Data61, 3 University of Texas at Austin, 4 University of Warwick, 5 Vin University 6 Eindhoven University of Technology |
| Pseudocode | Yes | We describe our method in Algorithm 1 and the pseudo code for optimizer in Appendix C. |
| Open Source Code | Yes | Code is available at: https://github.com/pvh1602/NPB. |
| Open Datasets | Yes | We conduct experiments on three standard datasets: CIFAR-10, CIFAR-100, and Tiny-Imagenet. |
| Dataset Splits | No | The paper mentions using CIFAR-10, CIFAR-100, and Tiny-Imagenet datasets, but does not explicitly provide information on training/validation/test splits by percentages or counts. |
| Hardware Specification | Yes | We use Pytorch 2 library and conduct experiments on a single GTX 3090Ti or A100 (depend on their available). |
| Software Dependencies | Yes | We use Pytorch 2 library and conduct experiments on a single GTX 3090Ti or A100 (depend on their available). ... We use the default mixed integer programming solver in CVXPY library |
| Experiment Setup | Yes | Table 2: Summary of the architectures, datasets, and hyperparameters used in experiments. Network Dataset Epochs Batch Optimizer Momentum LR LR Drop, Epoch Weight Decay VGG-19 CIFAR-100 160 128 SGD 0.9 0.1 10x, [60,120] 0.0001 Res Net-20 CIFAR-10 160 128 SGD 0.9 0.1 10x, [60,120] 0.0001 Res Net-18 Tiny-Image Net 100 128 SGD 0.9 0.01 10x, [30,60,80] 0.0001 |