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Results: 22
Number of items: 22
  • Open Access
    Araabi, A. (2024). Exploring solutions for low-resource neural machine translation. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Araabi, A., Niculae, V., & Monz, C. (2023). Joint Dropout: Improving Generalizability in Low-Resource Neural Machine Translation through Phrase Pair Variables. In M. Utiyama, & R. Wang (Eds.), MTS: Machine Translation Summit 2023: September 4-8, 2023, Macau SAR, China : Proceedings of Machine Translation Summit XIX. - Vol. 1: Research Track (pp. 12-25). Asia-Pacific Association for Machine Translation. https://aclanthology.org/2023.mtsummit-research.2
  • Open Access
    Troshin, S., & Niculae, V. (2023). Wrapped ß-Gaussians with compact support for exact probabilistic modeling on manifolds. Transactions on Machine Learning Research, 2023, Article 1351. https://openreview.net/forum?id=KrequDpWzt
  • Open Access
    Stap, D., Niculae, V., & Monz, C. (2023). Viewing Knowledge Transfer in Multilingual Machine Translation Through a Representational Lens. In H. Bouamor, J. Pino, & K. Bali (Eds.), The 2023 Conference on Empirical Methods in Natural Language Processing : Findings of the Association for Computational Linguistics: EMNLP 2023: December 6-10, 2023 (pp. 14973–14987). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.findings-emnlp.998
  • Open Access
    Tokarchuk, E., & Niculae, V. (2022). On Target Representation in Continuous-output Neural Machine Translation. In S. Gella, H. He, B. P. Majumder, B. Can, E. Giunchiglia, S. Cahyawijaya, S. Min, M. Mozes, X. L. Li, I. Augenstein, A. Rogers, K. Cho, E. Grefenstette, L. Rimell, & C. Dyer (Eds.), The 7th Workshop on Representation Learning for NLP (RepL4NLP 2022): proceedings of the workshop : ACL : May 26, 2022 (pp. 227–235). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.repl4nlp-1.24
  • Open Access
    Mihaylova, T., Niculae, V., & Martins, A. F. T. (2022). Modeling Structure with Undirected Neural Networks. Proceedings of Machine Learning Research, 162, 15544-15560. https://proceedings.mlr.press/v162/mihaylova22a.html
  • Open Access
    Martins, A. F. T., Treviso, M., Farinhas, A., Aguiar, P. M. Q., Figueiredo, M. A. T., Blondel, M., & Niculae, V. (2022). Sparse continuous distributions and Fenchel-Young losses. Journal of Machine Learning Research, 23, Article 257. https://www.jmlr.org/papers/v23/21-0879.html
  • Open Access
    Martins, P. H., Niculae, V., Marinho, Z., & Martins, A. F. T. (2021). Sparse and structured visual attention. In 2021 IEEE International Conference on Image Processing: proceedings : 19-22 September 2021, Anchorage, Alaska, USA (pp. 379-383). (ICIP). IEEE. https://doi.org/10.48550/arXiv.2002.05556, https://doi.org/10.1109/ICIP42928.2021.9506028
  • Open Access
    Martins, A., Farinhas, A., Treviso, M., Niculae, V., Aguiar, P., & Figueiredo, M. (2021). Sparse and Continuous Attention Mechanisms. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), 34th Concerence on Neural Information Processing Systems (NeurIPS 2020): online, 6-12 December 2020 (Vol. 26, pp. 20989-21001). (Advances in Neural Information Processing Systems; Vol. 33). Neural Information Processing Systems Foundation. https://papers.neurips.cc/paper/2020/hash/f0b76267fbe12b936bd65e203dc675c1-Abstract.html
  • Open Access
    Zantedeschi, V., Kusner, M. J., & Niculae, V. (2021). Learning binary trees by argmin differentiation. Proceedings of Machine Learning Research, 139, 12298-12309. https://proceedings.mlr.press/v139/zantedeschi21a.html
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