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..
REVISITING PRUNING AT INITIALIZATION THROUGH THE LENS OF RAMANUJAN GRAPH
Authors: Duc N.M Hoang, Shiwei Liu, Radu Marculescu, Zhangyang Wang
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we examine our earlier claims with empirical results and see how they fare; furthermore, with supporting evidence, we answer questions regarding relationships between Ramanujan to performance, randomness to performance, and Ramanujan to randomness. Finally, we point out intuitions and what they imply for Pa I under the lens of the Ramanujan perspective. Experimental settings. We conduct our experiments with two different DNN architectures: Resnet34 (He et al., 2016) and Vgg-16 (Simonyan & Zisserman, 2014) on CIFAR-10 (Krizhevsky, 2009). |
| Researcher Affiliation | Academia | Duc Hoang, Shiwei Liu, Radu Marculescu & Zhangyang Wang Department of Electrical and Computer Engineering University of Texas at Austin, Austin, TX 78712, USA EMAIL EMAIL |
| Pseudocode | No | The paper does not contain any explicit pseudocode blocks or algorithms. |
| Open Source Code | Yes | Our code is available at: https://github.com/VITA-Group/ramanujan-on-pai. |
| Open Datasets | Yes | Experimental settings. We conduct our experiments with two different DNN architectures: Resnet34 (He et al., 2016) and Vgg-16 (Simonyan & Zisserman, 2014) on CIFAR-10 (Krizhevsky, 2009). We include additional results on CIFAR-100 in the Appendix. |
| Dataset Splits | No | The paper mentions using CIFAR-10 and CIFAR-100, which have standard train/test splits, but does not explicitly describe how data was partitioned for training, validation, and testing (e.g., 80/10/10 split or specific sample counts for each set) nor does it mention a validation set explicitly being used. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used, such as GPU/CPU models, memory specifications, or cloud computing instances. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | Table 1: Summary of architectures and hyperparameters that we study in this paper. Model Data #Epoch Batch Size Optimizer LR LR Decay, Epoch Weight Decay Resnet-34 CIFAR-10 250 256 SGD 0.1 10 , [160, 180] 0.0005 VGG-16 CIFAR-10 250 256 SGD 0.1 10 , [160, 180] 0.0005 |