Search results

  • Full text

  • Document type

    • Publication year

  • Organisation

Results: 108
Number of items: 108
  • Open Access
    Abnar, S., & Zuidema, W. (2020). Quantifying Attention Flow in Transformers. In D. Jurafsky, J. Chai, N. Schluter, & J. Tetreault (Eds.), The 58th Annual Meeting of the Association for Computational Linguistics: ACL 2020 : Proceedings of the Conference : July 5-10, 2020 (pp. 4190-4197). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.385
  • Open Access
    Cornelissen, B., Zuidema, W., & Burgoyne, J. A. (2020). Studying large plainchant corpora using chant21. In Proceedings of DLfM 2020: the 7th International Conference on Digital Libraries for Musicology : 16th October 2020, McGill University, Montréal, QC, Canada (pp. 40-44). (ACM international conference proceedings series). The Association for Computing Machinery. https://doi.org/10.1145/3424911.3425514
  • Open Access
    Cornelissen, B., Zuidema, W., & Burgoyne, J. A. (2020). Mode Classification and Natural Units in Plainchant. In J. Cuming, J. H. Lee, B. McFee, M. Schedl, J. Devaney, C. McKay, E. Zangerle, & T. de Reuse (Eds.), Proceedings of the 21st International Society for Music Information Retrieval Conference: ISMIR MTL2020, Montréal, Québec, Canada, Virtual Conference, 11 to 16 October 2020 (pp. 869-875). ISMIR. https://doi.org/10.5281/zenodo.4245572
  • Open Access
    Abnar, S., Beinborn, L., Choenni, R., & Zuidema, W. (2019). Blackbox Meets Blackbox: Representational Similarity & Stability Analysis of Neural Language Models and Brains. In T. Linzen, G. Chrupała, Y. Belinkov, & D. Hupkes (Eds.), The BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP at ACL 2019: ACL 2019 : proceedings of the Second Workshop : August 1, 2019, Florence, Italy (pp. 191-203). The Association for Computational Linguistics. https://doi.org/10.18653/v1/W19-4820
  • Open Access
    Alhama, R. G., & Zuidema, W. (2019). A review of computational models of basic rule learning: The neural-symbolic debate and beyond. Psychonomic Bulletin and Review, 26(4), 1174-1194. https://doi.org/10.3758/s13423-019-01602-z
  • Open Access
    Jumelet, J., Zuidema, W., & Hupkes, D. (2019). Analysing Neural Language Models: Contextual Decomposition Reveals Default Reasoning in Number and Gender Assignment. In M. Bansal, & A. Villavicencio (Eds.), The 23rd Conference on Computational Natural Language Learning: CoNLL 2019 : proceedings of the conference : November 3-4, 2019, Hong Kong, China (pp. 1-11). The Association for Computational Linguistics. https://doi.org/10.18653/v1/K19-1001
  • Merker, B., Morley, I., & Zuidema, W. (2018). Five fundamental constraints on theories of the origins of music. In H. Honing (Ed.), The Origins of Musicality (pp. 49-80). MIT Press. http://cognet.mit.edu/pdfviewer/book/9780262344548/chap3
  • Zuidema, W., Hupkes, D., Wiggins, G. A., Scharff, C., & Rohrmeirer, M. (2018). Formal Models of Structure Building in Music, Language, and Animal Song. In H. Honing (Ed.), The Origins of Musicality (pp. 253-286). MIT Press. http://cognet.mit.edu/pdfviewer/book/9780262344548/chap11
  • Open Access
    Giulianelli, M., Harding, J., Mohnert, F., Hupkes, D., & Zuidema, W. (2018). Under the Hood: Using Diagnostic Classifiers to Investigate and Improve how Language Models Track Agreement Information. In T. Linzen, G. Chrupała, & A. Alishahi (Eds.), The 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP: EMNLP 2018 : proceedings of the First Workshop : November 1, 2018, Brussels, Belgium (pp. 240–248). The Association for Computational Linguistics. https://doi.org/10.18653/v1/W18-5426
  • Open Access
    Hupkes, D., Veldhoen, S., & Zuidema, W. (2018). Visualisation and 'diagnostic classifiers' reveal how recurrent and recursive neural networks process hierarchical structure. Journal of Artificial Intelligence Research, 61, 907-926. https://doi.org/10.1613/jair.1.11196
Page 5 of 11