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 [1].
Multi-tensor Completion with Common Structures
Authors: Chao Li, Qibin Zhao, Junhua Li, Andrzej Cichocki, Lili Guo
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical results demonstrate that the proposed method not only outperforms state-of-the-art methods for video in-painting, but also can recover missing data well even in cases that conventional methods are not applicable.Experimental results Multi-view video in-painting with one frame lost |
| Researcher Affiliation | Academia | 1Harbin Engineering University, Harbin, Heilongjiang, China 2RIKEN Brain Science Institute, Wako, Saitama, Japan 3Systems Research Institute, PAS, Warsaw, Poland |
| Pseudocode | Yes | Pseudo-code of the algorithm is shown in Alg.1. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | 1http://media.au.tsinghua.edu.cn/index.jsp |
| Dataset Splits | No | The paper describes experiments where missing data is simulated at different percentages (e.g., 'randomly removed some pixels from the three videos with specific missing percentage'), but it does not specify explicit train/validation/test dataset splits for model training and evaluation. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper mentions 'tuning parameters λk,qk > 0' in Algorithm 1, but does not provide specific values for these or other typical hyperparameters such as learning rates, batch sizes, or optimizer settings used in the experiments. |