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..
Theory-Driven Label-Specific Representation for Incomplete Multi-View Multi-Label Learning
Authors: Quanjiang Li, Tianxiang Xu, Tingjin Luo, Yan Zhong, Yang Li, Yiyun Zhou, Chenping Hou
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, extensive experiments on public datasets and real-world applications validate the effectiveness of TDLSR. ... 3 Experiments ... 3.1 Datasets and metrics ... 3.3 Implementation details ... 3.4 Experimental results and analysis ... 3.5 Ablation Study |
| Researcher Affiliation | Academia | 1National University of Defense Technology, 2Peking University, 3Zhejiang University |
| Pseudocode | No | The paper describes the methodology in detail across Section 2.1 to 2.4, but it does not contain a clearly labeled 'Pseudocode' or 'Algorithm' block, nor a structured, code-like procedure. |
| Open Source Code | No | We commit to sharing our code upon the acceptance of the paper. |
| Open Datasets | Yes | In our experiments, we utilize six popular multi-view multi-label datasets to validate the performance of our TDLSR, i.e., Corel 5k [5], ESPGame [1], IAPRTC12 [8], Mirflickr [16], Pascal07 [6], OBJECT [13]. ... The NBA dataset was collected from Basketball-Reference [2] |
| Dataset Splits | Yes | Each dataset is divided into training, validation and test sets in the ratio of 7:1:2. |
| Hardware Specification | Yes | Our model is implemented by Py Torch on one NVIDIA Ge Force RTX 4090 GPU of 24GB memory. |
| Software Dependencies | No | Our model is implemented by Py Torch on one NVIDIA Ge Force RTX 4090 GPU of 24GB memory. The specific version number for PyTorch or any other software dependencies is not mentioned. |
| Experiment Setup | Yes | The neighbor number k is fixed to 10 for all datasets. Adam optimizer with the initial learning rate of 0.0001 is used for optimization of all datasets. ... The parameters λ1 and λ2 are used to balance the effects of LIB and Lle. Two parameters are selected from the range of {0.01, 0.1, 1, 10, 100, 1000} |