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..
Towards Higher Ranks via Adversarial Weight Pruning
Authors: Yuchuan Tian, Hanting Chen, Tianyu Guo, Chao Xu, Yunhe Wang
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on various datasets and different tasks demonstrate the effectiveness of our algorithm in high sparsity. |
| Researcher Affiliation | Collaboration | Yuchuan Tian1, Hanting Chen2, Tianyu Guo2, Chao Xu1, Yunhe Wang2 1 National Key Lab of General AI, School of Intelligence Science and Technology, Peking University. 2 Huawei Noah s Ark Lab. |
| Pseudocode | Yes | The whole pruning framework is detailed in Algorithm 1. |
| Open Source Code | Yes | The codes are available at https://github.com/huawei-noah/Efficient-Computing/tree/ master/Pruning/RPG and https://gitee.com/mindspore/models/tree/ master/research/cv/RPG. |
| Open Datasets | Yes | CIFAR-10 is one of the most widely used benchmark for image classification. It consists of 60000 32 32 images: 50000 for training, and 10000 for validation. Image Net ISLVRC2012 [9] is a large scale image classification dataset. Mask R-CNN pruning on COCO val2017. |
| Dataset Splits | Yes | CIFAR-10... 50000 for training, and 10000 for validation. Image Net ISLVRC2012 [9]... It contains 1281K images in the training set and 50K images in the validation set. |
| Hardware Specification | Yes | Image Net experiments are run on 8 NVIDIA Tesla V100s. |
| Software Dependencies | No | We gratefully acknowledge the support of Mind Spore [38], CANN (Compute Architecture for Neural Networks) and Ascend AI Processor used for this research. (No version numbers provided for MindSpore or CANN). |
| Experiment Setup | Yes | We use the SGD optimizer with momentum 0.9, batchsize 128, learning rate 0.1, and weight decay 0.005. |