{"id":19199823,"url":"https://github.com/rose-stl-lab/autonpp","last_synced_at":"2025-07-02T03:37:08.448Z","repository":{"id":164613207,"uuid":"640068741","full_name":"Rose-STL-Lab/AutoNPP","owner":"Rose-STL-Lab","description":"Efficient computation of temporal point process intensity using automatic 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align=\"center\" \u003e\n  \u003ca href=\"https://github.com/Rose-STL-Lab/AI-STPP\"\u003e\u003cimg src=\"https://fremont.zzhou.info/images/2022/10/06/image-20221006102054441.png\" width=\"256\" height=\"256\" alt=\"AI-STPP\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003ch1 align=\"center\"\u003eAuto-NPP\u003c/h1\u003e\n\u003ch4 align=\"center\"\u003e✨Automatic Integration for Neural Point Process✨\u003c/h4\u003e\n\n\u003cp align=\"center\"\u003e\n    \u003ca href=\"https://zzhou.info/LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/87d0b0ec1c0a97dbf68ce4d3098de6912bca75aa006304dd0a55976e6673cbe1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f64656c67616e2f6c6f677572752e737667\" alt=\"license\"\u003e\u003c/a\u003e\n    \u003cimg src=\"https://img.shields.io/badge/Python-3.10+-yellow\" alt=\"python\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/Version-1.0.0-green\" alt=\"version\"\u003e\n\u003c/p\u003e\n\n## | Paper\n\n[Automatic Integration for Fast and Interpretable Neural Point Processes](https://proceedings.mlr.press/v211/zhou23a/zhou23a.pdf)\n\n## | Installation\n\nDependencies: `make`, `conda-lock`\n\n```bash\nmake create_environment\nconda activate autonpp\n```\n\n## | Dataset Download\n\n```bash\nmake download prefix=data\n```\n\n## | Get Trained Models\n\n```\nmake download prefix=models\n```\n\n## | Training and Testing\n\nSpecify the parameters in `configs/test_autoint_1d_dataset.yaml` and then run\n\n```bash\nmake run\n```\n\nThe loss curves and example intensity predictions are saved to `figs/`. \nWith real-world datasets, the ground truth intensity is a placeholder and can be safely ignored.\nThe logs are saved to `logs/`.\nThe models are saved to `models/`.\n\nTo use the trained models, set `retrain: false`.\n\n## | Cite\n\n```\n@article{zhou2023automatic,\n  title={Automatic Integration for Fast and Interpretable Neural Point Processes},\n  author={Zhou, Zihao and Yu, Rose},\n  journal={Learning for Dynamics and Control (L4DC)},\n  year={2023}\n}\n\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frose-stl-lab%2Fautonpp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frose-stl-lab%2Fautonpp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frose-stl-lab%2Fautonpp/lists"}