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..
New Interpretations of Normalization Methods in Deep Learning
Authors: Jiacheng Sun, Xiangyong Cao, Hanwen Liang, Weiran Huang, Zewei Chen, Zhenguo Li5875-5882
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, a series of experiments are conducted to verify these claims.In this section, we conduct a series of experiments to verify the claims of normalization methods induced by our proposed analysis tools. |
| Researcher Affiliation | Collaboration | 1Huawei Noah s Ark Lab, 2Xi an Jiaotong University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | The experiments are conducted on CIFAR-10 or CIFAR-100 dataset where images are normalized to zero mean and unit variance. |
| Dataset Splits | No | The paper does not explicitly provide specific training/validation/test dataset splits. It mentions "CIFAR-10 or CIFAR-100 dataset" and "training samples" but no percentages or specific counts for splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | Specifically, in this experiment, we train Res Net-101 model on CIFAR-10 using the SGD algorithm with learning rate 10 3 and epoch number 200. |