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Machine Un-learning: An Overview of Techniques, Applications, and Future Directions.

, , , , , , , and . Cogn. Comput., 16 (2): 482-506 (March 2024)

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A Meta-Learning Approach for Training Explainable Graph Neural Networks., , and . IEEE Trans. Neural Networks Learn. Syst., 35 (4): 4647-4655 (April 2024)Missing Data Imputation with Adversarially-trained Graph Convolutional Networks., , and . CoRR, (2019)Missing data imputation with adversarially-trained graph convolutional networks., , and . Neural Networks, (2020)Distributed Graph Convolutional Networks., , and . CoRR, (2020)A Meta-Learning Approach for Training Explainable Graph Neural Networks., , and . CoRR, (2021)Efficient Data Augmentation Using Graph Imputation Neural Networks., , , and . IIH-MSP (1), volume 81 of Smart Innovation, Systems and Technologies, page 57-66. Springer, (2017)Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence., , , , , , , , , and . Cogn. Comput., 16 (1): 45-74 (January 2024)Machine Un-learning: An Overview of Techniques, Applications, and Future Directions., , , , , , , and . Cogn. Comput., 16 (2): 482-506 (March 2024)Distributed Training of Graph Convolutional Networks., , and . IEEE Trans. Signal Inf. Process. over Networks, (2021)Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning., , , and . CoRR, (2021)