Search results
Results: 20
Number of items: 20
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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 -
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
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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
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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
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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
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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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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
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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
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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
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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/
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