Domain Adaptation in Physical Systems via Graph Kernel-Reference-Cited by-同舟云学术

Domain Adaptation in Physical Systems via Graph Kernel

Published:2022-08-14 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Li Haoran¹,Tong Hanghang²,Weng Yang¹

Affiliation:

1. Arizona State University, Tempe, AZ, USA

2. University of Illinois Urbana-Champaign, Champaign, IL, USA

Funder

Department of Energy

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3534678.3539380

Reference44 articles.

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2. Protein function prediction via graph kernels

3. Gecia Bravo Hermsdorff and Lee Gunderson . 2019 . A unifying framework for spectrum-preserving graph sparsification and coarsening . Advances in Neural Information Processing Systems , Vol. 32 (2019). Gecia Bravo Hermsdorff and Lee Gunderson. 2019. A unifying framework for spectrum-preserving graph sparsification and coarsening. Advances in Neural Information Processing Systems, Vol. 32 (2019).

4. Chen Cai , Dingkang Wang , and Yusu Wang . 2021. Graph Coarsening with Neural Networks. arXiv preprint arXiv:2102.01350 ( 2021 ). Chen Cai, Dingkang Wang, and Yusu Wang. 2021. Graph Coarsening with Neural Networks. arXiv preprint arXiv:2102.01350 (2021).

5. A Graph Embedding Framework for Maximum Mean Discrepancy-Based Domain Adaptation Algorithms

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