MM-DAG: Multi-task DAG Learning for Multi-modal Data - with Application for Traffic Congestion Analysis-Reference-Cited by-同舟云学术

MM-DAG: Multi-task DAG Learning for Multi-modal Data - with Application for Traffic Congestion Analysis

Published:2023-08-04 Issue: Volume: Page:
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Container-title:Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Lan Tian¹^ORCID,Li Ziyue²^ORCID,Li Zhishuai³^ORCID,Bai Lei⁴^ORCID,Li Man⁵^ORCID,Tsung Fugee⁵^ORCID,Ketter Wolfgang²^ORCID,Zhao Rui³^ORCID,Zhang Chen¹^ORCID

Affiliation:

1. Tsinghua University, BeiJing, China

2. University of Cologne, Cologne, Germany

3. SenseTime Research, BeiJing, China

4. Shanghai AI Laboratory, ShangHai, China

5. The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong

Funder

BNSF

NSFC

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3580305.3599436

Reference53 articles.

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3. Cooperative Evaluation of the Cause of Urban Traffic Congestion via Connected Vehicles

4. Michael Behrisch , Laura Bieker , Jakob Erdmann , and Daniel Krajzewicz . 2011 . SUMO--simulation of urban mobility: an overview . In Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation. ThinkMind. Michael Behrisch, Laura Bieker, Jakob Erdmann, and Daniel Krajzewicz. 2011. SUMO--simulation of urban mobility: an overview. In Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation. ThinkMind.

5. Rohit Bhattacharya , Tushar Nagarajan , Daniel Malinsky , and Ilya Shpitser . 2021 . Differentiable causal discovery under unmeasured confounding . In International Conference on Artificial Intelligence and Statistics. PMLR, 2314--2322 . Rohit Bhattacharya, Tushar Nagarajan, Daniel Malinsky, and Ilya Shpitser. 2021. Differentiable causal discovery under unmeasured confounding. In International Conference on Artificial Intelligence and Statistics. PMLR, 2314--2322.

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