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Update README for WSMTHP model #83

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6 changes: 4 additions & 2 deletions README.md
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Expand Up @@ -37,6 +37,7 @@
## News
<span id='news'/>

- ![new](https://img.alicdn.com/imgextra/i4/O1CN01kUiDtl1HVxN6G56vN_!!6000000000764-2-tps-43-19.png) [04-10-2026] Added the implementation of the [WSMTHP](https://arxiv.org/abs/2410.04037) model, based on the WSM method presented at NeurIPS'2024.
- ![new](https://img.alicdn.com/imgextra/i4/O1CN01kUiDtl1HVxN6G56vN_!!6000000000764-2-tps-43-19.png) [11-06-2025] We have released a new version of ``EasyTPP`` that exclusively supports PyTorch. TensorFlow support has been removed to streamline the codebase and focus on PyTorch-based implementations.
- ![new](https://img.alicdn.com/imgextra/i4/O1CN01kUiDtl1HVxN6G56vN_!!6000000000764-2-tps-43-19.png) [11-05-2025] Added the implementation of the [S2P2](https://openreview.net/pdf?id=74SvE2GZwW) model, presented at NeurIPS'2025.
- ![new](https://img.alicdn.com/imgextra/i4/O1CN01kUiDtl1HVxN6G56vN_!!6000000000764-2-tps-43-19.png) [02-17-2024] ``EasyTPP`` supports HuggingFace dataset API: all datasets have been published in [HuggingFace Repo](https://huggingface.co/easytpp) and see [tutorial notebook](https://github.com/ant-research/EasyTemporalPointProcess/blob/main/notebooks/easytpp_1_dataset.ipynb) for an example of usage.
Expand Down Expand Up @@ -78,8 +79,9 @@ We provide reference implementations of various state-of-the-art TPP papers:
| 5 | ICML'20 | THP | [Transformer Hawkes process](https://arxiv.org/abs/2002.09291) | [PyTorch](easy_tpp/model/torch_model/torch_thp.py) |
| 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](https://openreview.net/pdf?id=74SvE2GZwW) | [PyTorch](easy_tpp/model/torch_model/torch_s2p2.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'24 | WSMTHP | [Is Score Matching Suitable for Estimating Point Processes?](https://arxiv.org/abs/2410.04037) | [PyTorch](easy_tpp/model/torch_model/torch_wsm_thp.py) |
| 10 | 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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