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Knowledge distillation is a learning paradigm for boosting resource-efficient graph neural networks (GNNs) using more expressive yet cumbersome teacher models.
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2019
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2019
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2019
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2020
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2020
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2020
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2020
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2020
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2021
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R. Addanki, P. W. Battaglia, D. Budden, A. Deac, J. Godwin, T. Keck, W. L. S. Li, A. Sanchez-Gonzalez, J. Stott, S. Thakoor
2021
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2021
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2021
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J. Gou, B. Yu, S. J. Maybank, and D. Tao, “Knowledge distillation: A survey,”
2021
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M. M. Bronstein, J. Bruna, T. Cohen, and P. Veličković, “Geometric deep learning: Grids, groups, graphs, geodesics, and gauges,” 2021
2021
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2021
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Q. Cappart, D. Chételat, E. Khalil, A. Lodi, C. Morris, and P. Veličković, “Combinatorial optimization and reasoning with graph neural networks,” in
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C. Yang, J. Liu, and C. Shi, “Extract the knowledge of graph neural networks and go beyond it: An effective knowledge distillation framework,”
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S. Zhang, Y. Liu, Y. Sun, and N. Shah, “Graph-less neural networks: Teaching old mlps new tricks via distillation,”
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X. Deng and Z. Zhang, “Graph-free knowledge distillation for graph neural networks,” in
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H. Stärk, D. Beaini, G. Corso, P. Tossou, C. Dallago, S. Günnemann, and P. Liò, “3d infomax improves gnns for molecular property prediction,”
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C. K. Joshi, “Recent advances in efficient and scalable graph neural networks,” 2022
2022
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