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..
Learning with Feature Network and Label Network Simultaneously
Authors: Yingming Li, Ming Yang, Zenglin Xu, Zhongfei (Mark) Zhang
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive evaluations on three benchmark data sets demonstrate that DRML outstands with a superior performance in comparison with some existing multi-label learning methods. |
| Researcher Affiliation | Academia | College of Information Science & Electronic Engineering, Zhejiang University, China School of Computer Science and Engineering, Big Data Research Center University of Electronic Science and Technology of China |
| Pseudocode | No | The paper describes algorithms and derivations in text and equations, but it does not include a distinct pseudocode block or algorithm box. |
| Open Source Code | No | The paper does not provide any link or explicit statement about the availability of its source code. |
| Open Datasets | Yes | All data sets are obtained from http://mulan.sourceforge.net/datasets-mlc.html. |
| Dataset Splits | Yes | On the Medical and Yeast data sets, we follow the experimental setup used in Mulan. Since there is no fixed split in the Bookmarks data set in Mulan, we use a fixed training set of 60% of the data, and evaluate the performance of our predictions on the fixed test set of 40% of the data. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments (e.g., GPU models, CPU types). |
| Software Dependencies | No | The paper does not specify the version numbers of any software libraries, frameworks, or programming languages used in the experiments. |
| Experiment Setup | No | The paper mentions regularization parameters λ, γ, η and explores 'dropout level p' but does not provide specific values for these or other hyperparameters (e.g., learning rate, optimizer settings) used for the main results presented in tables. |