Sparse Maximum Margin Learning from Multimodal Human Behavioral Patterns
Authors: Ervine Zheng, Qi Yu, Zhi Zheng
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real-world behavioral data from medical and psychological domains demonstrate that our framework discovers meaningful multimodal behavioral patterns with improved interpretability and prediction performance. We conduct experiments on two real-world datasets from medical and psychological domains. |
| Researcher Affiliation | Academia | Ervine Zheng, Qi Yu, Zhi Zheng Rochester Institute of Technology {mxz5733, qi.yu, zhzbme}@rit.edu |
| Pseudocode | No | The paper describes the inference process but does not include any explicit pseudocode or algorithm blocks in the main text. Details are referred to the Appendix. |
| Open Source Code | Yes | The appendix and source code are presented in (Zheng, Yu, and Zheng 2023). https://github.com/ritmininglab/SM2-MRS/. Accessed: 2023-02-01. |
| Open Datasets | Yes | Dataset II: Behavioral Study in Psychology The second dataset was from a behavioral experiment that studied sensory processing in children with and without Autism Spectrum Disorder (ASD) (Koirala et al. 2021). |
| Dataset Splits | No | The paper does not explicitly provide specific training/validation/test dataset splits (e.g., percentages or sample counts) in the main text needed to reproduce the experiment. It only mentions using datasets and reporting performance. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings in the main text. |