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Results: 106
Number of items: 106
  • 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
  • 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
    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
    Garrido Alhama, R. (2017). Computational modelling of Artificial Language Learning: Retention, Recognition & Recurrence. [Thesis, fully internal, Universiteit van Amsterdam].
  • 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
    Lê, P. (2016). Learning vector representations for sentences: The recursive deep learning approach. [Thesis, fully internal, Universiteit van Amsterdam].
  • 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
    Alhama, R. G., & Zuidema, W. (2016). Pre-Wiring and Pre-Training: What does a neural network need to learn truly general identity rules? 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 4 (CEUR Workshop Proceedings; Vol. 1773). CEUR-WS. http://ceur-ws.org/Vol-1773/CoCoNIPS_2016_paper4.pdf
  • 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
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