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..
Variational Label Enhancement
Authors: Ning Xu, Jun Shu, Yun-Peng Liu, Xin Geng
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The recovery experiment on fourteen label distribution datasets and the predictive experiment on ten multi-label learning datasets validate the advantage of our approach over the state-of-the-art approaches. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Southeast University, Nanjing 210096, China 2School of Mathematics and Statistics, Xi an Jiaotong University, Xi an 710049, China. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code for the described methodology, nor does it provide a direct link to a code repository. |
| Open Datasets | Yes | There are in total one arti๏ฌcial dataset and 13 real-world label distribution datasets1. These real-world datasets (Geng, 2016) collected from biological experiments on the yeast genes, facial expression images, natural scene images and movies, respectively. (1http://palm.seu.edu.cn/xgeng/LDL/index.htm) There are ten MLL datasets2 used in the experiments. (2mulan.sourceforge.net/datasets.html) |
| Dataset Splits | Yes | All the algorithms are tested via ten-fold cross validation. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments. |
| Software Dependencies | No | The paper mentions general methods and model architectures (e.g., MLPs, SGD, Ada Grad) but does not provide specific version numbers for any software dependencies like programming languages or libraries. |
| Experiment Setup | Yes | For LEVI, the MLPs are constructed with three hidden layers, each with 500 hidden units and softplus activation functions. |