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..
Self-Paced Learning for Matrix Factorization
Authors: Qian Zhao, Deyu Meng, Lu Jiang, Qi Xie, Zongben Xu, Alexander Hauptmann
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the performance of the proposed SPMF approach...on synthetic, structure from motion and background subtraction data. |
| Researcher Affiliation | Academia | 1School of Mathematics and Statistics, Xi an Jiaotong University 2School of Computer Science, Carnegie Mellon University |
| Pseudocode | Yes | Algorithm 1 Self-paced matrix factorization algorithm |
| Open Source Code | No | The paper mentions using 'publicly available codes from the authors websites' for competing methods, but does not state that its own code is publicly available or provide a link. |
| Open Datasets | Yes | For rigid SFM, we employ the Dinosaur sequence1 which contains 319 feature points tracked over 36 views... 1http://www.robots.ox.ac.uk/ abm/. For nonrigid SFM, we use the Giraffe sequence2, which includes 166 feature points tracked over 120 frames. 2http://www.robots.ox.ac.uk/ abm/. Four video sequences provided by Li et al. (2004)4 were adopted in our evaluation... 4http://perception.i2r.a-star.edu.sg/bkmodel/bkindex |
| Dataset Splits | No | The paper describes data generation and corruption (e.g., missing data, noise addition) and reports performance averaged over multiple runs or realizations, but it does not specify explicit training/validation/test dataset splits with percentages or counts for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper refers to modifying existing solvers, but does not provide specific software names with version numbers for any libraries or dependencies used in their implementation. |
| Experiment Setup | Yes | Input: Incomplete data matrix Y Rm n with observation indexed by โฆ, k0, kend, ยต > 1. where parameter ฮณ > 0 is introduced to control the strength of the weights assigned to the selected samples. the rank was set to 4 and 6 for rigid and nonrigid SFM, respectively. |