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..
Deep Multi-View Concept Learning
Authors: Cai Xu, Ziyu Guan, Wei Zhao, Yunfei Niu, Quan Wang, Zhiheng Wang
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments conducted on image and document datasets show that DMCL performs well and outperforms baseline methods. |
| Researcher Affiliation | Academia | State Key Lab of ISN, School of Computer Science and Technology, Xidian University School of Computer Science and Technology, Xidian University College of Computer Science and Technology, Henan Polytechnic University {cxu 3@stu., zyguan@, ywzhao@mail., yfniu@stu., qwang@}xidian.edu.cn, EMAIL |
| Pseudocode | Yes | Algorithm 1: Optimization of DMCL; Algorithm 2: Composite Gradient Mapping |
| Open Source Code | No | The paper does not provide concrete access to source code, such as a repository link or an explicit statement of code release in supplementary materials. |
| Open Datasets | Yes | Reuters [Amini et al., 2009]. It consists of 111740 documents...; Image Net [Deng et al., 2009]. It is a well known realworld image database... |
| Dataset Splits | Yes | We use the holdout method [Han et al., 2011] for evaluation and tune model parameters by cross-validation on the training set. For each dataset, we randomly split the data items for each category and use 50% for training while the remaining 50% are reserved for test. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library names with versions). |
| Experiment Setup | Yes | Based on the results, we set α = 100, β = 0.015 and γ = 0.005 in other experiments. ... The layer sizes are set to [300 200 125]. |