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Results: 102
Number of items: 102
  • 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
    Alhama, R. G., & Zuidema, W. (2018). Pre-wiring and pre-training: What does a neural network need to learn truly general identity rules? Journal of Artificial Intelligence Research, 61, 927-946. https://doi.org/10.1613/jair.1.11197
  • Open Access
    Abnar, S., Ahmed, R., Mijnheer, M., & Zuidema, W. (2018). Experiential, Distributional and Dependency-based Word Embeddings have Complementary Roles in Decoding Brain Activity. In Proceedings of the 8th Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2018): January 7, 2018 (pp. 57-66). Association for Computational Linguistics. https://doi.org/10.18653/v1/W18-0107
  • 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
  • Open Access
    Zuidema, W., & de Boer, B. (2018). The evolution of combinatorial structure in language. Current Opinion in Behavioral Sciences, 21, 138-144. https://doi.org/10.1016/j.cobeha.2018.04.011
  • 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
  • van Woerkom, W., & Zuidema, W. (2017). Selecting the model that best fits the data. Behavioral and Brain Sciences, 40, Article e192. https://doi.org/10.1017/S0140525X16002338
  • Open Access
    Hupkes, D., & Zuidema, W. (2017). Diagnostic classification and symbolic guidance to understand and improve recurrent neural networks. Paper presented at Interpreting, Explaining and Visualizing Deep Learning workshop, Long Beach, California, United States. http://www.interpretable-ml.org/nips2017workshop/papers/12.pdf
  • Open Access
    Garrido Alhama, R. (2017). Computational modelling of Artificial Language Learning: Retention, Recognition & Recurrence. [Thesis, fully internal, Universiteit van Amsterdam].
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