Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Hybrid Singular Value Thresholding for Tensor Completion
Authors: Xiaoqin Zhang, Zhengyuan Zhou, Di Wang, Yi Ma
AAAI 2014 | Venue PDF | LLM Run Details | Input Tokens: 15,267 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,461 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental Evaluation To validate the effectiveness of the proposed tensor completion algorithm, we conduct two comparison experiments as follows: (1) the proposed metric versus tensor nuclear norm; (2) the proposed metric versus matrix nuclear norm. All the experiments are conducted with MATLAB on a platform with Pentium IV 3.2GHz CPU and 1G memory. |
| Researcher Affiliation | Academia | 1Institute of Intelligent System and Decision, Wenzhou University, Zhejiang, China 2Department of Electrical Engineering, Stanford University, CA, USA 3Department of Electrical and Computer Engineering, Shanghai Tech University, Shanghai, China |
| Pseudocode | Yes | Algorithm 1 Hybrid Threshold Computation |
| Open Source Code | No | The paper does not provide any statement about releasing open-source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | We ๏ฌrst randomly generate a pure low-rank tensor Lo R50 50 50 whose Tucker rank (de๏ฌned in the introduction) is (2,2,2) (the same set is adopted in (Liu et al. 2013)). ... we generate a low-rank tensor Lo R30 30 30 30 with Tucker rank (2,2,2,2) (the same set is adopted in (Mu et al. 2013)). |
| Dataset Splits | No | The paper mentions 'we sample a fraction c of elements in Lo as the observations' and varies 'c' but does not specify explicit train/validation/test splits with percentages or counts, nor does it reference standard predefined splits for the generated data. |
| Hardware Specification | Yes | All the experiments are conducted with MATLAB on a platform with Pentium IV 3.2GHz CPU and 1G memory. |
| Software Dependencies | No | The paper mentions 'MATLAB' but does not provide a specific version number for it or any other software dependencies. |
| Experiment Setup | No | The paper does not provide specific hyperparameter values or detailed training configurations (e.g., learning rates, batch sizes, optimizer settings) for the proposed method's experimental setup. It only states that comparison methods' parameters were chosen for best performance. |