From b834428e70d30358eb736f930501c0848eb3676f Mon Sep 17 00:00:00 2001 From: jarrycyx Date: Thu, 14 Dec 2023 11:52:38 +0800 Subject: [PATCH] Update CUTS+ Supplementary Material --- CUTS_Plus/README.md | 2 +- README.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/CUTS_Plus/README.md b/CUTS_Plus/README.md index 1faccd7..3b19c83 100644 --- a/CUTS_Plus/README.md +++ b/CUTS_Plus/README.md @@ -1,6 +1,6 @@ ## 🎄CUTS+: High-dimensional Causal Discovery from Irregular Time-series -[arXiv](https://arxiv.org/abs/2305.05890) | [Tutorial (Coming Soon) ![Open filled In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/) +[arXiv](https://arxiv.org/abs/2305.05890) | [Tutorial (Coming Soon) ![Open filled In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/) | [Supplementary Materials](CUTS_Plus/github_files/CUTS_Plus_Supp_ver1214.pdf) ### ✍️ Paper summary diff --git a/README.md b/README.md index b19c7a3..db5aef9 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ This repository provides our latest research on Causal Neural Network. | Algorithm | Summary | Paper | Code | |--------|---------------------------------------------------------------------------|----| ----| | CUTS | EM-Style joint causal graph learning and missing data imputation for irregular temporal data | [ICLR 2023](https://openreview.net/forum?id=UG8bQcD3Emv)
[Latest Version](CUTS/CUTS_ver0324_camera5.pdf) |[Code](CUTS/) -| CUTS+ | Increasing scalability of neural causal discovery on high-dimensional irregular data. | Accepted to AAAI-24
[arXiv](https://arxiv.org/abs/2305.05890) |[Code](CUTS_Plus/) +| CUTS+ | Increasing scalability of neural causal discovery on high-dimensional irregular data. | Accepted to AAAI-24
[Supplements](CUTS_Plus/github_files/CUTS_Plus_Supp_ver1214.pdf)
[arXiv](https://arxiv.org/abs/2305.05890)|[Code](CUTS_Plus/) | CausalTime Benchmark| A novel pipeline capable of generating realistic time-series along with a ground truth causal graph that is generalizable to different fields. [Official Website.](https://www.causaltime.cc/) | [arXiv](https://arxiv.org/abs/2310.01753) | [Code](CausalTime/) | REACT | A causal deep learning approach that combines neural networks with causal discovery to develop a reliable and generalizable model to predict a patient's risk of developing CSA-AKI within the next 48 hours. | [medRxiv](https://www.medrxiv.org/content/10.1101/2023.12.04.23299332v1) | [Code](REACT/)