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 Curriculum Learning
Authors: Lu Jiang, Deyu Meng, Qian Zhao, Shiguang Shan, Alexander Hauptmann
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments We present experimental results for the proposed SPCL on two tasks: matrix factorization and multimedia event detection. We demonstrate that our approach outperforms baseline methods on both tasks. |
| Researcher Affiliation | Academia | 1 School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA, 15217 2 School of Mathematics and Statistics, Xi an Jiaotong University, Xi an, Shaanxi, P. R. China, 710049 3 Institute of Computing Technology, Chinese Academy of Sciences, Beijing, P. R. China, 100190 |
| Pseudocode | Yes | Algorithm 1: Self-paced Curriculum Learning. input : Input dataset D, predetermined curriculum γ, self-paced function f and a stepsize µ output: Model parameter w 1 Derive the curriculum region Ψ from γ; 2 Initialize v , λ in the curriculum region; 3 while not converged do 4 Update w = arg minw E(w, v ; λ, Ψ); 5 Update v = arg minv E(w , v; λ, Ψ); 6 if λ is small then increase λ by the stepsize µ; |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the proposed methodology. |
| Open Datasets | Yes | TRECVID Multimedia Event Detection (MED) 2013 Development, MED13Test and MED14Test sets were used (Over et al. 2013), which include around 34,000 Internet videos. |
| Dataset Splits | Yes | all parameters were carefully tuned on a validation set on a different set of events. |
| Hardware Specification | No | The paper does not provide specific hardware specifications used for running the experiments. |
| Software Dependencies | No | LM-BFGS (Zhu et al. 1997) in stats package in the R language, and Step 4 was solved by a standard quadratic programming toolkit. |
| Experiment Setup | No | Mixture scheme was used, and all parameters were carefully tuned on a validation set on a different set of events. ... Step 5 was solved by LM-BFGS (Zhu et al. 1997) in stats package in the R language, and Step 4 was solved by a standard quadratic programming toolkit. |