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..
Tensor Compressive Sensing Fused Low-Rankness and Local-Smoothness
Authors: Xinling Liu, Jingyao Hou, Jiangjun Peng, Hailin Wang, Deyu Meng, Jianjun Wang
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | At last, we carry out experiments to apply our model to hyperspectral image and video restoration problems. The experimental results show that our method is prominently better than many other competing ones. |
| Researcher Affiliation | Academia | 1 School of Mathematics and Statistics, Southwest University, Chongqing 400715, China 2 School of Mathematics and Information, China West Normal University, Nanchong 637002, China 3 School of Mathematics and Statistics and Ministry of Education Key Lab of Intelligent Networks and Network Security, Xi an Jiaotong University, Xi an 710099, China 4 Macao Institute of Systems Engineering, Macau University of Science and Technology, Taipa, Macao |
| Pseudocode | Yes | Algorithm 1: ADMM for problem (19) |
| Open Source Code | Yes | Our code and Supplementary Material are available at https://github.com/fsliuxl/cs-tctv. |
| Open Datasets | Yes | We choose two typical datasets, HYDICE Washington DC Mall and HYDICE Urbanpart in this experiment. |
| Dataset Splits | Yes | Regularization parameters λ for our models are chosen empirically from the set {10 3, 10 2, 10 1, 1, 101, 102, 103} by cross-validation. |
| Hardware Specification | Yes | All experiments are run in MATLAB R2016a on a 64-bit PC with an E7-4820 2.00GHz CPU and 64GB memory. |
| Software Dependencies | Yes | All experiments are run in MATLAB R2016a |
| Experiment Setup | Yes | Regularization parameters λ for our models are chosen empirically from the set {10 3, 10 2, 10 1, 1, 101, 102, 103} by cross-validation. |