EasyTPP: Towards Open Benchmarking Temporal Point Processes
Authors: Siqiao Xue, Xiaoming Shi, Zhixuan Chu, Yan Wang, Hongyan Hao, Fan Zhou, Caigao JIANG, Chen Pan, James Y. Zhang, Qingsong Wen, JUN ZHOU, Hongyuan Mei
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We comprehensively evaluate 9 models in our benchmark, which include the classical Multivariate Hawkes Process (MHP) with an exponential kernel, (see Appendix B for more details), and 8 widely-cited state-of-the-art neural models: |
| Researcher Affiliation | Collaboration | Siqiao Xue , Xiaoming Shi , Zhixuan Chu , Yan Wang , Hongyan Hao , Fan Zhou , Caigao Jiang , Chen Pan , James Y. Zhang , Qingsong Wen , Jun Zhou Ant Group, Alibaba Group siqiao.xsq@alibaba-inc.com Hongyuan Mei TTIC hongyuan@ttic.edu |
| Pseudocode | Yes | Listing 1: Pseudo implementation of customizing a TPP model in Py Torch using Easy TPP. |
| Open Source Code | Yes | The code and data are available at https: //github.com/ant-research/Easy Temporal Point Process. |
| Open Datasets | Yes | All preprocessed datasets are available at Google Drive. |
| Dataset Splits | Yes | Following common practices, we split the set of sequences into disjoint train, validation, and test set. |
| Hardware Specification | Yes | All the experiments were conducted on a server with 256G RAM, a 64 logical cores CPU (Intel(R) Xeon(R) Platinum 8163 CPU @ 2.50GHz) and one NVIDIA Tesla P100 GPU for acceleration. |
| Software Dependencies | No | Our library is compatible with both Py Torch (Paszke et al., 2019) and Tensor Flow (Abadi et al., 2016), the top-2 popular deep learning frameworks, and thus offers a great flexibility for future research in method development. (No specific version numbers for these frameworks are provided, only citations to their original papers). |
| Experiment Setup | Yes | We keep the model architectures as the original implementations in their papers. For a fair comparison, we use the same training procedure for all the models: we used Adam (Kingma & Ba, 2015) with the default parameters, biases initialized with zeros, no learning rate decay, the same maximum number of training epochs, and early stopping criterion (based on log-likelihood on the held-out dev set) for all models. |