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..
Balanced Column-Wise Block Pruning for Maximizing GPU Parallelism
Authors: Cheonjun Park, Mincheol Park, Hyun Jae Oh, Minkyu Kim, Myung Kuk Yoon, Suhyun Kim, Won Woo Ro
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that BCBP is superior to previous pruning methods through comprehensive experiments. and Experiments Datasets and Models We evaluate the performance of BCBP using IWSLT English-Vietnamese (Luong and Manning 2016), SQu AD (Rajpurkar, Jia, and Liang 2018), and Image Net dataset (Deng et al. 2009). |
| Researcher Affiliation | Collaboration | Cheonjun Park1, Mincheol Park1,2, Hyun Jae Oh3, Minkyu Kim1, Myung Kuk Yoon4, Suhyun Kim2, and Won Woo Ro1* 1Yonsei University 2Korea Institute of Science and Technology 3Samsung Electronics 4Ewha Womans University |
| Pseudocode | No | The paper describes the process of BCBP in text but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The footnote '1https://github.com/cheonjun-park/appendix1' refers to Appendix 1 for 'additional verification of the following DNN models' but does not explicitly state that the source code for the BCBP methodology itself is provided at this link or elsewhere. |
| Open Datasets | Yes | Datasets and Models We evaluate the performance of BCBP using IWSLT English-Vietnamese (Luong and Manning 2016), SQu AD (Rajpurkar, Jia, and Liang 2018), and Image Net dataset (Deng et al. 2009). |
| Dataset Splits | No | Image Net contains 1.28M training images and 50K test images. The paper mentions training and testing sets, but does not explicitly describe a separate validation split with specific details for reproducibility. |
| Hardware Specification | Yes | We evaluate BCBP using NVIDIA RTX 2080 TI GPUs. |
| Software Dependencies | No | The paper mentions using 'Py Torch framework' but does not specify a version number or other software dependencies with their versions. |
| Experiment Setup | Yes | For settings of the fine-tuning, we use SGD optimizer with the weight decay, 1 10 4 and the momentum as 0.9. We set a batch size of 256 and a learning rate of 0.0001. |