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Results: 106
Number of items: 106
  • 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
  • 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
    Hupkes, D., Veldhoen, S., & Zuidema, W. (2017). Visualisation and 'diagnostic classifiers' reveal how recurrent and recursive neural networks process hierarchical structure. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.1711.10203
  • Open Access
    Garrido Alhama, R. (2017). Computational modelling of Artificial Language Learning: Retention, Recognition & Recurrence. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Alhama, R. G., & Zuidema, W. (2017). Segmentation as Retention and Recognition: the R&R model. In G. Gunzelmann, A. Howes, T. Tenbrink, & E. J. Davelaar (Eds.), CogSci 2017: proceedings of the 39th Annual Meeting of the Cognitive Science Society : London, UK : 26-29 July 2017 : Computational Foundations of Cognition (Vol. 2, pp. 1531-1536). Cognitive Science Society. https://cognitivesciencesociety.org/wp-content/uploads/2019/01/cogsci17_proceedings.pdf
  • Open Access
    Le, P., & Zuidema, W. (2016). Quantifying the vanishing gradient and long distance dependency problem in recursive neural networks and recursive LSTMs. In P. Blunsom, K. Cho, S. Cohen, E. Grefenstette, K. M. Hermann, L. Rimell, J. Weston, & S. W. Yih (Eds.), The 54th Annual Meeting of the Association for Computational Linguistics. Proceedings of the 1st Workshop on Representation Learning for NLP: ACL 2016 : August 11th, 2016, Berlin, Germany (pp. 87-93). The Association for Computational Linguistics. https://doi.org/10.18653/v1/W16-1610
  • Open Access
    Alhama, R. G., & Zuidema, W. (2016). Generalization in Artificial Language Learning: Modelling the Propensity to Generalize. In A. Korhonen, A. Lenci, B. Murphy, T. Poibeau, & A. Villavicencio (Eds.), The 54th Annual Meeting of the Association for Computational Linguistics: proceedings of the 7th Workshop on Cognitive Aspects of Computational Language Learning: August 11, 2016, Berlin, Germany (pp. 64-72). Association for Computational Linguistics. https://doi.org/10.18653/v1/W16-19
  • Open Access
    Veldhoen, S., Hupkes, D., & Zuidema, W. (2016). Diagnostic Classifiers: Revealing how Neural Networks Process Hierarchical Structure. In T. R. Besold, A. Bordes, A. d'Avila Garcez, & G. Wayne (Eds.), Proceedings of the Workshop on Cognitive Computation: Integrating neural and symbolic approaches 2016: co-located with the 30th Annual Conference on Neural Information Processing Systems (NIPS 2016) : Barcelona, Spain, December 9, 2016 Article 6 (CEUR Workshop Proceedings; Vol. 1773). CEUR-WS. http://ceur-ws.org/Vol-1773/CoCoNIPS_2016_paper6.pdf
  • Open Access
    Lê, P. (2016). Learning vector representations for sentences: The recursive deep learning approach. [Thesis, fully internal, Universiteit van Amsterdam].
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