The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding
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“Generating discriminative negative samples is essential since failing to do so may hardly improve the model or even cause gradient vanishing [Zhang et al., 2019].”
From Temporal Knowledge Graph Completion: A Survey (2023) · cites this paper as Zhang et al., 2019
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Citations
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
WordNet: a lexical database for English
Miller, G. A. (1995) · Communications of the ACM · doi:10.1145/219717.219748 · Cited by 14,015
node2vec: Scalable Feature Learning for Networks
Grover, A., & Leskovec, J. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939754 · Cited by 10,926
Improved training of Wasserstein GANs
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., & Courville, A. (2017) · arXiv · doi:10.48550/arxiv.1704.00028 · Cited by 6,800
Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553380 · Cited by 5,366 · cited 2×
“The main idea of self-paced (or curriculum) learning [3], [19] is to pick up easy samples first, and then gradually switch to hard ones.”
BackgroundKnowledge Graph Embedding by Translating on Hyperplanes
Wang, Z., Zhang, J., Feng, J., & Chen, Z. (2014) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v28i1.8870 · Cited by 3,915
Self-Attention Generative Adversarial Networks
Zhang, H., Goodfellow, I., Metaxas, D., & Odena, A. (2018) · arXiv · doi:10.48550/arxiv.1805.08318 · Cited by 3,353
Introduction to Statistical Relational Learning
(2007) · doi:10.7551/mitpress/7432.001.0001 · Cited by 1,643
Observed versus latent features for knowledge base and text inference
Toutanova, K., & Chen, D. (2015) · Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality · doi:10.18653/v1/w15-4007 · Cited by 1,070
Research works printed but not linked: 32
- yao, 2018, Taking human out of learning applications A survey on automated machine learning
- yang, 2017, Embedding entities and relations for learning and inference in knowledge bases
- wang, 2018, Incorporating GAN for negative sampling in knowledge representation learning, AAAI, 10.1609/aaai.v32i1.11536 · DOI
- trouillon, 2017, Knowledge graph completion via complex tensor factorization, JMLR, 18, 4735
- 10.1145/1242572.1242667 · DOI · Paper page
- 10.1007/BF00992696 · DOI
- wang, 2018, On evaluating embedding models for knowledge base completion
- 10.1109/TKDE.2017.2754499 · DOI · Paper page
- 10.1145/3219819.3220041 · DOI
- zhang, 2018, NSCaching Simple and efficient negative sampling for knowledge graph embedding
- glorot, 2010, Understanding the difficulty of training deep feedforward neural networks, AISTATS, 249
- goodfellow, 2014, Generative adversarial nets, NeurIPS, 2672
- 10.3115/v1/P15-1067 · DOI
- 10.1145/1273496.1273551 · DOI
- kumar, 2010, Self-paced learning for latent variable models, NeurIPS, 1189
- paszke, 2017, On Automatic Differentiation
- 10.1145/1376616.1376746 · DOI · Paper page
- nickel, 2011, A three-way model for collective learning on multi-relational data, ICML, 11, 809
- 10.1007/s10994-013-5363-6 · DOI · Paper page
- singhal, 2012, Introducing the knowledge graph: things, not strings, The Official Google Blog, 5
- 10.3115/v1/D14-1067 · DOI
- 10.18653/v1/N18-1133 · DOI · Paper page
- bordes, 2013, Translating embeddings for modeling multi-relational data, NeurIPS, 2787
- 10.1007/978-3-540-76298-0_52 · DOI · Paper page
- fan, 2014, Transition-based knowledge graph embedding with relational mapping properties, PACLIC
- arjovsky, 2017, Wasserstein GAN
- lao, 2011, Random walk inference and learning in a large scale knowledge base, EMNLP, 529
- liu, 2017, Analogical inference for multi-relational embeddings, ICML, 2168
- lin, 2015, Learning entity and relation embeddings for knowledge graph completion, AAAI, 15, 2181
- mikolov, 2013, Linguistic regularities in continuous space word representations, ACL, 746
- nickel, 2016, Holographic embeddings of knowledge graphs, AAAI, 2, 3
- 10.1109/JPROC.2015.2483592 · DOI
Cited by
Sorted by citations
Temporal Knowledge Graph Completion: A Survey
Cai, B., Xiang, Y., Gao, L., Zhang, H., Li, Y., & Li, J. (2023) · Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/734 · Cited by 133
Personalized Negative Reservoir for Incremental Learning in Recommender Systems
Valkanas, A., Wang, Y., Zhang, Y., & Coates, M. (2024) · arXiv · doi:10.48550/arxiv.2403.03993 · Cited by 1
Self-citation
Metadata
- Authors
- Zhang, Y., Yao, Q., Shao, Y., & Chen, L.
- Venue
- 2019 IEEE 35th International Conference on Data Engineering (ICDE)
- Publisher
- IEEE
- Published
- 2019-04
- Pages
- 614-625
- Cited by
- 108
- References
- 9
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