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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Position-based Scaled Gradient for Model Quantization and Pruning
Authors: Jangho Kim, KiYoon Yoo, Nojun Kwak
NeurIPS 2020 | Venue PDF | LLM Run Details | Input Tokens: 17,253 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,481 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results on CIFAR10/100 and Image Net datasets show the effectiveness of the proposed PSG in both domains of pruning and quantization even for extremely low bits. |
| Researcher Affiliation | Academia | Jangho Kim Seoul National University Seoul, Korea EMAIL Ki Yoon Yoo Seoul National University Seoul, Korea EMAIL Nojun Kwak Seoul National University Seoul, Korea EMAIL |
| Pseudocode | No | The paper describes its proposed method mathematically and in prose but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is released in Github2. 2https://github.com/Jangho-Kim/PSG-pytorch |
| Open Datasets | Yes | The experimental results on CIFAR10/100 and Image Net datasets show the effectiveness of the proposed PSG in both domains of pruning and quantization even for extremely low bits. |
| Dataset Splits | No | The paper does not explicitly provide the training/validation/test dataset splits, only stating that it followed settings from other papers like [1] for CIFAR-10 and ImageNet. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | We used the Pytorch framework for all experiments. |
| Experiment Setup | Yes | PSGD is applied from the last 15 epochs for Image Net experiments and from the ο¬rst learning rate decay epoch for CIFAR experiments. We use additional 30 epochs for PSGD at extremely low bits experiments (Table 4). Also, we tuned the hyper-parameter Ξ»s for each bit-widths and sparsity. |