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..
LERE: Learning-Based Low-Rank Matrix Recovery with Rank Estimation
Authors: Zhengqin Xu, Yulun Zhang, Chao Ma, Yichao Yan, Zelin Peng, Shoulie Xie, Shiqian Wu, Xiaokang Yang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that LERE surpasses state-of-the-art (SOTA) methods. The code for this work is accessible at https://github.com/zhengqinxu/LERE. |
| Researcher Affiliation | Collaboration | 1AMo E Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, China 2ETH Zรผrich, Switzerland 3Signal Processing, RF & Optical Dept. Institute for Infocomm Research A*STAR, Singapore 4School of Information Science and Engineering, Wuhan University of Science and Technology, China EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: LERE for RPCA |
| Open Source Code | Yes | The code for this work is accessible at https://github.com/zhengqinxu/LERE. |
| Open Datasets | Yes | Suite Sparse Database 1 which provides general data matrices with low numerical ranks, is tested, and these outcomes are depicted in Fig. 4. 1https://sparse.tamu.edu/ Furthermore, we also conduct evaluation on the video dataset from the Scene Background Initialization (SBI) datasets 2 and the Low Dynamic Range (LDR) datasets 3. 2https://sbmi2015.na.icar.cnr.it/SBIdataset.html 3http://alumni.soe.ucsc.edu/ orazio/deghost.html |
| Dataset Splits | No | The paper describes testing on synthetic and real-world datasets but does not explicitly mention any training/validation/test splits of a single dataset. It mentions 'training one set of parameters by the small sparsity dataset' but no specific split percentages or sample counts. |
| Hardware Specification | Yes | All of our tests run on a Windows 10 laptop with Intel i7-9750H CPU, 64G RAM. The parameters learning processes run on an Ubuntu workstation with Intel i9-9900K CPU and two Nvidia RTX-2080Ti GPUs. |
| Software Dependencies | No | The paper mentions employing 'Feedforward Recurrent-Mixed Neural Network (FRMNN) (Cai, Liu, and Yin 2021)' and 'RPCA' but does not specify version numbers for programming languages (e.g., Python), libraries (e.g., PyTorch, TensorFlow), or other software dependencies. |
| Experiment Setup | Yes | In our method, the sampling numbers are I = 4r log(n1) and J = 4r log(n2); the iteration number of FRMNN is 17. All parameters in compared methods follow their default settings. Moreover, all of our tests run on a Windows 10 laptop with Intel i7-9750H CPU, 64G RAM. The parameters learning processes run on an Ubuntu workstation with Intel i9-9900K CPU and two Nvidia RTX-2080Ti GPUs. We corrupt the input matrix Y Rn1 n2 by sparse noise with varying corruption rates ฮฑ = {0.1, 0.3, 0.5} and parameters: n1 = 3000, n2 = {3000, 1000, 500}, rank r = 5; the iteration stop criterion is Y L S F / Y F < 10 7, where L and S are the reconstruction matrices; the maximum number of iterations is 200. |