Self-paced Mixture of Regressions
Authors: Longfei Han, Dingwen Zhang, Dong Huang, Xiaojun Chang, Jun Ren, Senlin Luo, Junwei Han
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To demonstrate the effectiveness of SPMo R, we conducted experiments on both the sythetic examples and real-world applications to age estimation and glucose estimation. The results show that SPMo R outperforms the stateof-the-arts methods. |
| Researcher Affiliation | Academia | 1School of Information and Electronics, Beijing Institute of Technology 2School of Automation, Northwestern Polytechnical University 3School of Computer Science, Carnegie Mellon University 4Beijing Electro-Mechanical Engineering Institute |
| Pseudocode | Yes | Algorithm 1 SPMo R Training Algorithm |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | Dataset: We conducted experiments on the most frequently used Longitudinal Morphological Face Database (MORPH) [Ricanek and Tesafaye, 2006] database... and Dataset: We conducted experiments on the popular 2009-2014 National Health and Nutrition Examination Survey (NHANES) dataset [Zipf et al., 2013], which is the cross-sectional data and the ground-truth Hb A1c data were publicly available. |
| Dataset Splits | Yes | randomly dividing the whole dataset into two parts: 80% for training and the other 20% for test, and repeating 30 random trails. Next, in the first run, the optimal hyper-parameters, including k and λ, were obtained by using grid-search with tenfold CV on the training set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions features and analysis methods, but does not provide specific software names with version numbers (e.g., Python 3.x, PyTorch 1.x) for replication. |
| Experiment Setup | Yes | In SPMo R, we set k to 9, and λ to 1e-05. In SPMo R+, we set k to 8, and λ to 1e-05. In SPMo R, we set k to 5, and λ to 1e-05. In SPMo R+, we set k to 16, and λ to 1e-04. |