Dr. V. (Vlad) Niculae
Faculteit der Natuurwetenschappen, Wiskunde en Informatica
Informatics Institute
Bezoekadres
- Science Park 900
- Kamernummer: L6.08
Postadres
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Postbus 94323
1090 GH Amsterdam
Contactgegevens
Social media
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Publicaties
2024
- Mohammed, W., & Niculae, V. (2024). On Measuring Context Utilization in Document-Level MT Systems. In Y. Graham, & M. Purver (Eds.), The 18th Conference of the European Chapter of the Association for Computational Linguistics : Findings of EACL 2024: EACL 2024 : March 17-22, 2024 (pp. 1633–1643). Association for Computational Linguistics. https://aclanthology.org/2024.findings-eacl.113/
Tokarchuk, E., & Niculae, V. (2024). The Unreasonable Effectiveness of Random Target Embeddings for Continuous-Output Neural Machine Translation. In K. Duh, H. Gomez, & S. Bethard (Eds.), The 2024 Conference of the North American Chapter of the Association for Computational Linguistics : proceedings of the conference: NAACL 2024 : June 16-21, 2024 (Vol. 2, pp. 653-662). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.naacl-short.56 [details]
2023
- 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 [details]
- 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 [details]
- 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 [details]
- Zantedeschi, V., Franceschi, L., Kaddour, J., Kusner, M. J., & Niculae, V. (2023). DAG learning on the Permutahedron. In The Eleventh International Conference on Learning Representations (ICLR) https://openreview.net/pdf?id=m9LCdYgN8-6
2022
- Araabi, A., Monz, C., & Niculae, V. (2022). How Effective is Byte Pair Encoding for Out-Of-Vocabulary Words in Neural Machine Translation? In K. Duh, F. Guzmán, & S. Richardson (Eds.), Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track), AMTA 2022, Orlando, USA, September 12-16, 2022 (pp. 117-130). Association for Machine Translation in the Americas. https://aclanthology.org/2022.amta-research.9
- Farinhas, A., Ferreira Aziz, W., Niculae, V., & Martins, A. F. T. (2022). Sparse Communication via Mixed Distributions. In International Conference on Learning Representations https://openreview.net/forum?id=WAid50QschI
- 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 [details]
- 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 [details]
- 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 [details]
2021
- Correia, G., Niculae, V., Aziz, W., & Martins, A. (2021). Efficient Marginalization of Discrete and Structured Latent Variables via Sparsity. 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. 15, pp. 11789-11802). (Advances in Neural Information Processing Systems; Vol. 33). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2020/hash/887caadc3642e304ede659b734f79b00-Abstract.html [details]
- 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 [details]
- 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 [details]
- 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 [details]
2020
- Mihaylova, T., Niculae, V., & Martins, A. F. T. (2020). Understanding the Mechanics of SPIGOT: Surrogate Gradients for Latent Structure Learning. In B. Webber, T. Cohn, Y. He, & Y. Liu (Eds.), 2020 Conference on Empirical Methods in Natural Language Processing: EMNLP 2020 : proceedings of the conference : November 16-20, 2020 (pp. 2186–2202). The Association for Computational Linguistics. https://aclanthology.org/2020.emnlp-main.171/ [details]
2025
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Tokarchuk, E., & Niculae, V. (2024). The Unreasonable Effectiveness of Random Target Embeddings for Continuous-Output Neural Machine Translation. In K. Duh, H. Gomez, & S. Bethard (Eds.), The 2024 Conference of the North American Chapter of the Association for Computational Linguistics : proceedings of the conference: NAACL 2024 : June 16-21, 2024 (Vol. 2, pp. 653-662). Association for Computational Linguistics.