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 [1].
Position-based Scaled Gradient for Model Quantization and Pruning
Authors: Jangho Kim, KiYoon Yoo, Nojun Kwak
NeurIPS 2020 | Venue PDF | LLM Run Details
| 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. |