Search results

  • Full text

  • Document type

    • Publication year

  • Organisation

Results: 9
Number of items: 9
  • Open Access
    De Cao, N. (2024). Entity centric neural models for natural language processing. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    De Cao, N., Schmid, L., Hupkes, D., & Titov, I. (2022). Sparse Interventions in Language Models with Differentiable Masking. In J. Bastings, Y. Belinkov, Y. Elazar, D. Hupkes, N. Saphra, & S. Wiegreffe (Eds.), BlackboxNLP Analyzing and Interpreting Neural Networks for NLP: BlackboxNLP 2022 : Proceedings of the Workshop : December 8, 2022 (pp. 16-27). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.blackboxnlp-1.2
  • Open Access
    De Cao, N., Aziz, W., & Titov, I. (2021). Editing Factual Knowledge in Language Models. In M.-C. Moens, X. Huang, L. Specia, & S. W. Sih (Eds.), 2021 Conference on Empirical Methods in Natural Language Processing: EMNLP 2021 : proceedings of the conference : November 7-11, 2021 (pp. 6491-6506). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.emnlp-main.522
  • Open Access
    De Cao, N., Aziz, W., & Titov, I. (2021). Highly Parallel Autoregressive Entity Linking with Discriminative Correction. In M.-C. Moens, X. Huang, L. Specia, & S. W. Yih (Eds.), 2021 Conference on Empirical Methods in Natural Language Processing: EMNLP 2021 : proceedings of the conference : November 7-11, 2021 (pp. 7662-7669). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.emnlp-main.604
  • Open Access
    De Cao, N., Schlichtkrull, M., Aziz, W., & Titov, I. (2020). How do Decisions Emerge across Layers in Neural Models? Interpretation with Differentiable Masking. 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. 3243–3255). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.emnlp-main.262
  • Open Access
    De Cao, N., Aziz, W., & Titov, I. (2019). Question answering by reasoning across documents with graph convolutional networks. In J. Burstein, C. Doran, & T. Solorio (Eds.), The 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: NAACL HLT 2019 : proceedings of the conference : June 2-June 7, 2019 (Vol. 1, pp. 2306-2317). The Association for Computational Linguistics. https://doi.org/10.18653/v1/N19-1240
  • Open Access
    Davidson, T. R., Falorsi, L., De Cao, N., Kipf, T., & Tomczak, J. M. (2018). Hyperspherical Variational Auto-Encoders. In A. Globerson, & R. Silva (Eds.), Uncertainty in Artificial Intelligence: proceedings of the Thirty-Fourth Concerence (2018) : August 6-10, 2018, Monterey, California, USA (pp. 856-865). AUAI Press. http://auai.org/uai2018/proceedings/papers/309.pdf
  • Open Access
    Falorsi, L., de Haan, P., Davidson, T. R., De Cao, N., Weiler, M., Forré, P., & Cohen, T. S. (2018). Explorations in Homeomorphic Variational Auto-Encoding. Paper presented at ICML18 Workshop on Theoretical Foundations and Applications
    of Deep Generative Models, Stockholm, Sweden. https://arxiv.org/abs/1807.04689
  • Open Access
    De Cao, N., & Kipf, T. (2018). MolGAN: An implicit generative model for small molecular graphs. Paper presented at ICML18 Workshop on Theoretical Foundations and Applications
    of Deep Generative Models, Stockholm, Sweden. https://arxiv.org/abs/1805.11973
Page of