Deep Recurrent Survival Analysis
Authors: Kan Ren, Jiarui Qin, Lei Zheng, Zhengyu Yang, Weinan Zhang, Lin Qiu, Yong Yu4798-4805
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the experiments on the three realworld tasks from different fields, our model significantly outperforms the state-of-the-art solutions under various metrics. |
| Researcher Affiliation | Academia | Kan Ren, Jiarui Qin, Lei Zheng, Zhengyu Yang, Weinan Zhang, Lin Qiu, Yong Yu APEX Data & Knowledge Management Lab Shanghai Jiao Tong University kren, qinjr, zhenglei, zyyang, wnzhang, lqiu, yyu@apex.sjtu.edu.cn |
| Pseudocode | No | The paper includes "Figure 1: Detailed illustration of Deep Recurrent Survival Analysis (DRSA) model." which is a diagram, not structured pseudocode or an algorithm block. |
| Open Source Code | Yes | We also published the implementation code for reproductive experiments1. 1Reproductive code link: https://github.com/rk2900/drsa. |
| Open Datasets | Yes | We evaluate our model with strong baselines in three real-world tasks. We also published the processed full datasets2. 2We have put sampled data in the published code. The three processed full datasets link: https://goo.gl/n UFND4. |
| Dataset Splits | No | We split the CLINIC and MUSIC datasets to training and test sets with ratio of 4:1 and 6:1, respectively. The paper does not explicitly mention a validation split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. It only discusses the model and its performance. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., 'PyTorch 1.9', 'Python 3.8'). |
| Experiment Setup | No | The paper mentions "hyperparameter α controls the loss value balance between them" and "The discussion about various interval sizes has been included in the supplemental materials." However, it does not explicitly state concrete hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed training configurations in the main text. |