Advancing Temporal Multimodal Learning with Physics Informed Regularization-Reference-Cited by-同舟云学术

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Advancing Temporal Multimodal Learning with Physics Informed Regularization

Published:2023-03-22 Issue: Volume: Page:
ISSN:
Container-title:2023 57th Annual Conference on Information Sciences and Systems (CISS)
language:
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Author:

Deshpande Niharika¹,Park Hyoshin¹,Pandey Venktesh²,Yoon Gyugeun³

Affiliation:

1. Computational Data Science & Engineering, North Carolina A&T State University,Greensboro,USA

2. Civil, Architectural and Environmental Engineering North Carolina A&T State University,Greensboro,USA

3. Computational Data Science & Engineering North Carolina A&T State University,Greensboro,USA

Funder

NSF

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10089615/10089616/10089632.pdf?arnumber=10089632

Reference15 articles.

1. On Information and Sufficiency

2. The Mathematical Theory of Communication;shannon;Urbana The University of Illinois Press,1949

3. KL divergence based agglomerative clustering for automated Vitiligo grading

4. Graph learning-based spatial-temporal graph convolutional neural networks for traffic forecasting

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Physics-informed deep learning with Kalman filter mixture for traffic state prediction;International Journal of Transportation Science and Technology;2024-04

2. Physics-Informed Deep Learning with Kalman Filter Mixture: A New State Prediction Model;2024 58th Annual Conference on Information Sciences and Systems (CISS);2024-03-13

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