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Machine learning especially deep neural networks have achieved great success but many of them often rely on a number of labeled samples for supervision.
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S. Liu, B. Grau, I. Horrocks, and E. Kostylev, “Indigo: Gnn-based inductive knowledge graph completion using pair-wise encoding,” in Advances in Neural Information Processing Systems , 2021
2021
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J. Chen, H. He, F. Wu, and J. Wang, “Topology-aware correlations between relations for inductive link prediction in knowledge graphs,” in AAAI . AAAI Press, 2021, pp. 6271–6278
2021
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2021
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S. Narayan, A. Gupta, S. Khan, F. S. Khan, L. Shao, and M. Shah, “Discriminative region-based multi-label zero-shot learning,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 8731–8740
2021
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P. Banerjee, T. Gokhale, Y. Yang, and C. Baral, “Weaqa: Weak supervision via captions for visual question answering,” in Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 , 2021, pp. 3420–3435
2021
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K. Ma, F. Ilievski, J. Francis, Y. Bisk, E. Nyberg, and A. Oltramari, “Knowledge-driven data construction for zero-shot evaluation in commonsense question answering,” in 35th AAAI Conference on Artificial Intelligence , 2021
2021
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2021
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2021
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D. Daza, M. Cochez, and P. Groth, “Inductive entity representations from text via link prediction,” in Proceedings of the Web Conference , 2021, pp. 798–808
2021
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Y. Geng, J. Chen, W. Zhang, Y. Xu, Z. Chen, J. Z. Pan, Y. Huang, F. Xiong, and H. Chen, “Disentangled ontology embedding for zero-shot learning,” in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , 2022, pp. 443–453
2022
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Y. Geng, J. Chen, X. Zhuang, Z. Chen, J. Z. Pan, J. Li, Z. Yuan, and H. Chen, “Benchmarking knowledge-driven zero-shot learning,” Journal of Web Semantics , vol. 75, p. 100757, 2023
2023
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