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.
Dynamic Nonlinear Matrix Completion for Time-Varying Data Imputation
Authors: Jicong Fan6587-6596
AAAI 2022 | Venue PDF | LLM Run Details | Input Tokens: 18,777 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,293 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 | Numerical results show that D-NLMC outperforms the baselines in real applications. |
| Researcher Affiliation | Academia | Jicong Fan1,2 1The Chinese University of Hong Kong (Shenzhen) 2Shenzhen Research Institute of Big Data Shenzhen, China EMAIL |
| Pseudocode | Yes | Algorithm 1: Fast EVD for Kt |
| Open Source Code | No | The paper does not provide any statement about releasing the source code for its described methodology, nor does it include any links to a code repository. |
| Open Datasets | Yes | We test the proposed method on the SML2010 indoor temperature dataset3 from the UCI machine learning repository. The dataset consists of 2764 samples of 24 variables such as indoor temperature, relative humidity, and lightning. (https://archive.ics.uci.edu/ml/datasets/SML2010) |
| Dataset Splits | No | The paper describes randomly removing fractions of entries or blocks for testing performance and mentions a training period of 20 columns for synthetic data, but it does not specify explicit train/validation/test dataset splits (e.g., percentages or sample counts) needed for reproduction. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'L-BFGS (Liu and Nocedal 1989)' for optimization, but it does not provide specific version numbers for any software, libraries, or frameworks used in the implementation or experimentation. |
| Experiment Setup | Yes | In D-NLMC, we set w = 20, R = 15, and use Gaussian kernel with ฯ = ยตw 2 Pw i=1 Pw j=1 xi xj (similar to (Fan, Zhang, and Udell 2020)), where ยต is a constant such as 1 or 3. In this case, we use ยต = 1. |