Search results
Results: 102
Number of items: 102
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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 -
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
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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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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
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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
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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
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Le, P., & Zuidema, W. (2015). The Forest Convolutional Network: Compositional Distributional Semantics with a Neural Chart and without Binarization. In L. Márquez, C. Callison-Burch, & J. Su (Eds.), EMNLP 2015 Lisbon : conference proceedings: September 17-21 : Conference on Empirical Methods in Natural Language Processing (pp. 1155-1164). The Association for Computational Linguistics. https://aclweb.org/anthology/D/D15/D15-1137.pdf
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Le, P., & Zuidema, W. (2015). Unsupervised Dependency Parsing: Let's Use Supervised Parsers. In R. Mihalcea, J. Chai, & A. Sarkar (Eds.), NAACL HLT 2015: The 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Proceedings of the Conference : May 31-June 5, 2015, Denver, Colorado, USA (pp. 651-661). The Association for Computational Linguistics. http://aclweb.org/anthology/N/N15/N15-1067.pdf
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Merker, B., Morley, I., & Zuidema, W. (2015). Five fundamental constraints on theories of the origins of music. Philosophical Transactions of the Royal Society B: Biological Sciences, 370(1664), Article 20140095. https://doi.org/10.1098/rstb.2014.0095
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