The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Hubness and Pollution: Delving into Cross-Space Mapping for Zero-Shot Learning
Open accessAbstract
Angeliki Lazaridou, Georgiana Dinu, Marco Baroni. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Torralba, A., & Efros, A. A. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995347 · Cited by 2,440
Predicting Human Brain Activity Associated with the Meanings of Nouns
Mitchell, T. M., Shinkareva, S. V., Carlson, A., Chang, K.-M., Malave, V. L., Mason, R. A., & Just, M. A. (2008) · Science · doi:10.1126/science.1152876 · Cited by 1,180
Grauman, K., & Leibe, B. (2011) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.2200/s00332ed1v01y201103aim011 · Cited by 126
Cited by
Unsupervised Neural Machine Translation
Artetxe, M., Labaka, G., Agirre, E., & Cho, K. (2017) · arXiv · doi:10.48550/arxiv.1710.11041 · Cited by 632
A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings
Artetxe, M., Labaka, G., & Agirre, E. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1073 · Cited by 555
A Survey of Zero-Shot Learning: Settings, Methods, and Applications
Wang, W., Zheng, V. W., Yu, H., & Miao, C. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3293318 · Cited by 528
Learning bilingual word embeddings with (almost) no bilingual data
Artetxe, M., Labaka, G., & Agirre, E. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1042 · Cited by 455
Learning principled bilingual mappings of word embeddings while preserving monolingual invariance
Artetxe, M., Labaka, G., & Agirre, E. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1250 · Cited by 369
Metadata
- Authors
- Lazaridou, A., Dinu, G., & Baroni, M.
- Venue
- Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Published
- 2015
- Pages
- 270-280
- OpenAlex
- W2250646737
- Cited by
- 210
- References
- 5
- FWCI
- 12.69
- Type
- conference paper
- Language
- en
- Open access
- gold
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- Lazaridou, A., Dinu, G., & Baroni, M. (2015). Hubness and Pollution: Delving into Cross-Space Mapping for Zero-Shot Learning. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). https://doi.org/10.3115/v1/p15-1027
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
