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..
DPaI: Differentiable Pruning at Initialization with Node-Path Balance Principle
Authors: Lichuan Xiang, Quan Nguyen-Tri, Lan-Cuong Nguyen, Hoang Pham, Khoat Than, Long Tran-Thanh, Hongkai Wen
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical results demonstrate that DPa I significantly outperforms current state-of-the-art Pa I methods on various architectures, such as Convolutional Neural Networks and Vision-Transformers. Code is available at https://github.com/Quan Nguyen-Tri/DPa I.git |
| Researcher Affiliation | Collaboration | 1University of Warwick, 2Hanoi University of Science and Technology, 3FPT Software AI Center, 4Collov Labs |
| Pseudocode | Yes | Algorithm 1 Differentiable Pruning at Initialization (DPa I) |
| Open Source Code | Yes | Code is available at https://github.com/Quan Nguyen-Tri/DPa I.git |
| Open Datasets | Yes | Our main experiments are conducted with CIFAR-10, CIFAR-100, and Tiny-Image Net datasets, where: CIFAR-10 is augmented by normalizing per-channel, randomly flipping horizontally. CIFAR-100 is augmented by normalizing per-channel, randomly flipping horizontally. Tiny-Image Net is augmented by normalizing per channel, cropping to 64x64, and randomly flipping horizontally. We also perform experiments on Image Net-1K (Deng et al., 2009) to verify our methods work on large-scale dataset tasks. |
| Dataset Splits | Yes | Our main experiments are conducted with CIFAR-10, CIFAR-100, and Tiny-Image Net datasets, where: CIFAR-10 is augmented by normalizing per-channel, randomly flipping horizontally. CIFAR-100 is augmented by normalizing per-channel, randomly flipping horizontally. Tiny-Image Net is augmented by normalizing per channel, cropping to 64x64, and randomly flipping horizontally. We also perform experiments on Image Net-1K (Deng et al., 2009) to verify our methods work on large-scale dataset tasks. |
| Hardware Specification | Yes | We use Pytorch 1 library and conduct experiments on a single A5000. Each model was trained using three random seeds (0, 1, 2) to ensure robustness, and the model was trained on Nvidia A100. |
| Software Dependencies | No | We use Pytorch 1 library and conduct experiments on a single A5000. |
| Experiment Setup | Yes | For training on final sparse network, the hyperparameters are chosen as follows: Table 6: 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 |