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iLampard merged 1 commit into from
Nov 7, 2025
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Add link and notebook for the S2P2 model. #74

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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -77,7 +77,7 @@ We provide reference implementations of various state-of-the-art TPP papers:
| 6 | ICLR'20 | IntensityFree | [Intensity-Free Learning of Temporal Point Processes](https://arxiv.org/abs/1909.12127) | [PyTorch](easy_tpp/model/torch_model/torch_intensity_free.py) |
| 7 | ICLR'21 | ODETPP | [Neural Spatio-Temporal Point Processes (simplified)](https://arxiv.org/abs/2011.04583) | [PyTorch](easy_tpp/model/torch_model/torch_ode_tpp.py) |
| 8 | ICLR'22 | AttNHP | [Transformer Embeddings of Irregularly Spaced Events and Their Participants](https://arxiv.org/abs/2201.00044) | [PyTorch](easy_tpp/model/torch_model/torch_attnhp.py) |
| 9 | NeurIPS'25 | S2P2 | Deep Continuous-Time State-Space Models for Marked Event Sequences | [PyTorch](easy_tpp/model/torch_model/torch_s2p2.py) |
| 9 | NeurIPS'25 | S2P2 | [Deep Continuous-Time State-Space Models for Marked Event Sequences](https://openreview.net/pdf?id=74SvE2GZwW) | [PyTorch](easy_tpp/model/torch_model/torch_s2p2.py) |



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