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Alhama, R.G., Zuidema, W. (2016) Generalization in Artificial Language Learning: Modelling the Propensity to Generalize.In Korhonen, A. Lenci, A. Murphy, B. Poibeau, T. Villavicencio, A. (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.Conference contribution | https://doi.org/10.18653/v1/W16-19 | UvA-DAREAlhama, R.G., Zuidema, W. (2016) Pre-Wiring and Pre-Training: What does a neural network need to learn truly general identity rules?.In Besold, T.R. Bordes, A. d'Avila Garcez, A. Wayne, G. (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 (CEUR Workshop Proceedings, Vol. 1773). CEUR-WS.Conference contribution | http://ceur-ws.org/Vol-1773/CoCoNIPS_2016_paper4.pdf | UvA-DAREAlhama, R.G., Zuidema, W. (2017) Segmentation as Retention and Recognition: the R&R model.In Gunzelmann, G. Howes, A. Tenbrink, T. Davelaar, E.J. (Eds.), CogSci 2017: proceedings of the 39th Annual Meeting of the Cognitive Science Society : London, UK : 26-29 July 2017 : Computational Foundations of Cognition (pp 1531-1536). Cognitive Science Society.Conference contribution | https://cogsci.mindmodeling.org/2017/papers/0300/index.html | UvA-DAREAlhama, R.G., Zuidema, W. (2017) Segmentation as Retention and Recognition: the R&R model.In CogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition (pp 1531-1536) (CogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition). The Cognitive Science Society.Conference contribution | UvA-DAREAlhama, 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, Vol. 61 (pp 927-946)Article | https://doi.org/10.1613/jair.1.11197 | UvA-DAREAlhama, 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, Vol. 61 (pp 927-946)Article | https://doi.org/10.1613/jair.1.11197 | UvA-DAREAlhama, R.G., Zuidema, W. (2019) A review of computational models of basic rule learning: The neural-symbolic debate and beyond.Psychonomic Bulletin and Review, Vol. 26 (pp 1174-1194)Review article | https://doi.org/10.3758/s13423-019-01602-z | UvA-DAREAlhama, R.G. (2017) Computational modelling of Artificial Language Learning: Retention, Recognition & Recurrence.Thesis, fully internal | UvA-DAREAliseda-Llera, A. (1997) Seeking Explanations: Abduction in Logic, Philosophy of Science and Artificial Intelligence.Universiteit van Amsterdam.Thesis, fully external | UvA-DAREAliseda-Llera, A. (1997) Abduction as a model for belief revision.In Proceedings of the Taller de Logica y Computacion, Primer Encuentro de Computacion (pp 1-7). Universidad de Queretaro.Chapter | UvA-DAREAljanaki, A., Bountouridis, D., Burgoyne, J.A., Van Balen, J., Wiering, F., Honing, H., Veltkamp, R.C. (2014) Designing Games with a Purpose for Data Collection in Music Research. Emotify and Hooked: Two Case Studies.In De Gloria, A. (Eds.), Games and Learning Alliance: Second International Conference, GALA 2013, Paris, France, October 23-25, 2013 : revised selected papers (pp 29-40) (Lecture Notes in Computer Science, Vol. 8605). Springer.Conference contribution | https://doi.org/10.1007/978-3-319-12157-4_3 | UvA-DAREAllcock, J., Buhrman, H., Linden, N. (2009) Arbitrarily little knowledge can give a quantum advantage for nonlocal tasks.Physical Review A, Vol. 80 (pp 032105)Article | https://doi.org/10.1103/PhysRevA.80.032105 | UvA-DAREAllender, E., Buhrman, H., Friedman, L., Loff, B. (2012) Reductions to the set of random strings: The resource-bounded case.In Rovan, B. Sassone, V. Widmayer, P. (Eds.), Mathematical Foundations of Computer Science 2012: 37th International Symposium, MFCS 2012, Bratislava, Slovakia, August 27-31, 2012 : proceedings (pp 88-99) (Lecture Notes in Computer Science, Vol. 7464). Springer.Conference contribution | https://doi.org/10.1007/978-3-642-32589-2_11 | UvA-DAREAllender, E., Buhrman, H., Friedman, L., Loff, B. (2014) Reductions to the set of random strings: The resource-bounded case.Logical Methods in Computer Science, Vol. 10Article | https://doi.org/10.2168/LMCS-10(3:5)2014 | UvA-DAREAllender, E., Buhrman, H., Kouck, M., van Melkebeek, D., Ronneburger, D. (2002) Power from random strings.In Proceedings of the 43d annual IEEE conference on Foundations of Computer Science (pp 669-678). IEEE Computer Society Press.Conference contribution | UvA-DAREAllender, E., Buhrman, H., Koucky, M., van Melkebeek, D., Ronneburger, D. (2006) Power from random strings.SIAM Journal on Computing, Vol. 35 (pp 1467-1493)Article | https://doi.org/10.1137/050628994 | UvA-DAREAllender, E., Buhrman, H., Koucky, M. (2006) What can be efficiently reduced to the kolmogorov-random strings?.Annals of Pure and Applied Logic, Vol. 138 (pp 2-19)Article | https://doi.org/10.1016/j.apal.2005.06.003 | UvA-DAREAllerstorfer, R., Christandl, M., Grinko, D., Nechita, I., Ozols, M., Rochette, D., Verduyn Lunel, P. (2023) Monogamy of highly symmetric states.ArXiv.Preprint | https://doi.org/10.48550/arXiv.2309.16655 | UvA-DAREAllerstorfer, R. (2024) Position-based quantum cryptography: From theory towards practice.ILLC Dissertation seriesThesis, fully internal | UvA-DAREAllo, P., Poggiolesi, F., Smets, S. (2015) Dynamics in Logic.Logique et Analyse, Vol. 58 (pp 143-346)Special issue (Editorship) | http://poj.peeters-leuven.be/content.php?url=issue&journal_code=LEA&issue=0&vol=230 | UvA-DARE
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