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..
Sparse Training via Boosting Pruning Plasticity with Neuroregeneration
Authors: Shiwei Liu, Tianlong Chen, Xiaohan Chen, Zahra Atashgahi, Lu Yin, Huanyu Kou, Li Shen, Mykola Pechenizkiy, Zhangyang Wang, Decebal Constantin Mocanu
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We design a novel gradual magnitude pruning (GMP) method, named gradual pruning with zerocost neuroregeneration (Gra Net), that advances state of the art. Perhaps most impressively, its sparse-to-sparse version for the first time boosts the sparse-tosparse training performance over various dense-to-sparse methods with Res Net50 on Image Net without extending the training time. We release all codes in https://github.com/Shiweiliuiiiiiii/Gra Net. |
| Researcher Affiliation | Collaboration | 1Eindhoven University of Technology, 2University of Texas at Austin 3University of Twente,4University of Leeds,5JD Explore Academy, 6University of Jyväskylä |
| Pseudocode | Yes | See Appendix B.1 for the pseudocode of Gra Net. |
| Open Source Code | Yes | We release all codes in https://github.com/Shiweiliuiiiiiii/Gra Net. |
| Open Datasets | Yes | We choose two commonly used architectures to study pruning plasticity, VGG-19 [58] with batch normalization on CIFAR-10 [27], and Res Net-20 [20] on CIFAR-10. ... Res Net-50 on Image Net |
| Dataset Splits | No | The paper specifies training epochs and learning rate schedules in Table 1 but does not explicitly detail validation dataset splits or how validation was performed in the main text. It primarily reports test accuracy. |
| Hardware Specification | Yes | All accuracies are in line with the baselines reported in the references [8, 11, 67, 9, 37]. We use standard implementations and hyperparameters available online, with the exception of the small batch size for the Res Net-50 on Image Net due to the limited hardware resources (2 Tesla V100). |
| Software Dependencies | No | The paper mentions "reproduced by our implementation with Py Torch" but does not specify the version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | Table 1: Summary of the architectures and hyperparameters we study in this paper. Model Data #Epoch Batch Size LR LR Decay, Epoch Weight Decay Test Accuracy Res Net-20 CIFAR-10 160 128 0.1 (β = 0.9) 10 , [80, 120] 0.0005 92.41 0.04 |