1. 2017
  2. Schutten, M., Wiering, M., & MacDougall, P. (2017). Balancing Imbalance: On using reinforcement learning to increase stability in smart electricity grids. In B. Verheij, & M. WIering (Eds.), Preproceedings of the 29th Benelux Conference on Artificial Intelligence (BNAIC'2017) (pp. 423-424). University of Groningen.
  3. Hogervorst, J., Okafor, E., & Wiering, M. (2017). Deep Colorization for Facial Gender Recognition. In B. Verheij, & M. Wiering (Eds.), Preproceedings of the 29th Benelux Conference on Artificial Intelligence (BNAIC'2017) (pp. 317-325). University of Groningen.
  4. van Vugt, M., Brandt, A., & Schulze-Bonhage, A. (2017). Tracking Perceptual and Memory Decisions by Decoding Brain Activity. In BIAS 2017 preproceedings (pp. 76-85). University of Groningen.
  5. de Koster, A., Spenader, J., & Hendriks, P. (2017). Are children's overly distributive interpretations and spreading errors related?. Abstract from The 42nd Annual Boston University Conference on Language Development, Boston, United States.
  6. Maathuis, H., Boulogne, L., Wiering, M., & Sterk, A. (2017). Predicting chaotic time series using machine learning techniques. In B. Verheij, & M. Wiering (Eds.), Preproceedings of the 29th Benelux Conference on Artificial Intelligence (BNAIC 2017) (pp. 326-340). University of Groningen: SPO.
  7. Huijser, S., Taatgen, N., & van Vugt, M. (2017). Distracted in a Demanding Task: A Classification Study with Artificial Neural Networks. In B. Verheij, & M. Wiering (Eds.), Proceedings of BNAIC 2017 (pp. 199-212). University of Groningen.
  8. Pawara, P., Okafor, E., Schomaker, L., & Wiering, M. (2017). Data Augmentation for Plant Classification. In Advanced Concepts for Intelligent Vision Systems (Acivs 2017) [112]
  9. Ghosh, S., Heifetz, A., Verbrugge, R., & Weerd, H. D. (2017). What Drives People's Choices in Turn-Taking Games, if not Game-Theoretic Rationality? In J. Lang (Ed.), Proceedings 6th Conference on Theoretical Aspects of Rationality and Knowledge (TARK 2017), Liverpool, U.K.: Proceedings (pp. 265-284). (Electronic Proceedings in Theoretical Computer Science (EPTCS); Vol. 251). Open Publishing Association. DOI: 10.4204/EPTCS.251.19
  10. Moye, A. S., & van Vugt, M. (2017). A computational model of focused attention meditation and its transfer to a sustained attention task. In M. K. van Vugt, A. P. Banks, & W. G. Kennedy (Eds.), Proceedings of the 15th International Conference on Cognitive Modeling (pp. 43-48). Coventry, UK: University of Warwick, Inclusive Technology Ltd.
  11. van der Velde, M., & ESM-MERGE investigators (2017). How does rumination impact cognition? A first mechanistic model. In M. van Vugt, A. P. Banks, & W. G. Kennedy (Eds.), Proceedings of the 15th International Conference on Cognitive Modeling (pp. 25-30). Coventry, UK: University of Warwick, Inclusive Technology Ltd.
  12. Okafor, E., Smit, R., Schomaker, L., & Wiering, M. (2017). Operational Data Augmentation in Classifying Single Aerial Images of Animals. In IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA), 2017 (pp. 354-360). IEEE. DOI: 10.1109/INISTA.2017.8001185
  13. de Koster, A., Spenader, J., & Dotlacil, J. (2017). Children's Understanding of Distributivity and Adjectives of Comparison. In M. LaMendola, & J. Scott (Eds.), Proceedings of the 41st annual Boston University Conference on Language Development (Vol. 1, pp. 373-386). [30] Cascadilla Press.
  14. Valentijn, E. A., Begeman, K., Belikov, A., Boxhoorn, D. R., Brinchmann, J., McFarland, J., ... van Dijk, G. J. W. (2017). Target and (Astro-)WISE technologies - Data federations and its applications. In Astroinformatics 2017 (pp. 333-340). (Proceedings IAU Symposium; Vol. 12, issue S325, Astroinformatics). International Astronomical Union. DOI: 10.1017/S1743921317000254
  15. Dijkstra, K., van de Loosdrecht, J., Schomaker, L., & Wiering, M. (2017). Hyper-spectral frequency selection for the classification of vegetation diseases. In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (2017 ed., pp. 483-488). Bruges (Belgium): ESANN.
  16. van der Ree, M., Roerdink, J., Phillips, C., Garraux, G., Salmon, E., & Wiering, M. (2017). Support Vector Components Analysis. In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: ESANN ESANN.
  17. Shantia, A., Bidoia, F., Schomaker, L., & Wiering, M. (2017). Dynamic Parameter Update for Robot Navigation Systems through Unsupervised Environmental Situational Analysis. In IEEE Symposium Series on Computational Intelligence (pp. 1-7). Institute of Electrical and Electronics Engineers Inc.. DOI: 10.1109/SSCI.2016.7850238
  18. Okafor, E., Pawara, P., Karaaba, M., Surinta, O., Codreanu, V., Schomaker, L., & Wiering, M. (2017). Comparative study between deep learning and bag of visual words for wild-animal recognition. In 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 (2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016). Institute of Electrical and Electronics Engineers Inc.. DOI: 10.1109/SSCI.2016.7850111
  19. He, S., & Schomaker, L. (2017). Co-occurrence features for writer identification. In Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR (pp. 78-83). (Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR). Institute of Electrical and Electronics Engineers Inc.. DOI: 10.1109/ICFHR.2016.0027
  20. Paul, K., & Cnossen, F. (2017). A Cognitive Neuroscience Perspective on Skill Acquisition in Catheter-based Interventions. In P. Lanzer (Ed.), Textbook of Catheter-Based Cardiovascular Interventions Springer Nature. DOI: 10.1007/978-3-319-55994-0
  21. Dhali, M., He, S., Popovic, M., Tigchelaar, E., & Schomaker, L. (2017). A Digital Palaeographic Approach towards Writer Identification in the Dead Sea Scrolls. In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, 693-702, 2017, Porto, Portugal (pp. 693-702). DOI: 10.5220/0006249706930702
  22. Jager, W., Verbrugge, R., Flache, A., de Roo, G., Hoogduin, L., & Hemelrijk, C. (Eds.) (2017). Advances in Social Simulation 2015. (Advances in Intelligent Systems and Computing; Vol. 528). Springer International Publishing. DOI: 10.1007/978-3-319-47253-9
  23. Elderman, R., Pater, L., Thie, A., Drugan, M., & Wiering, M. (2017). Adversarial Reinforcement Learning in a Cyber Security Simulation}. In International Conference on Agents and Artificial Intelligence (ICAART)
  24. He, S. (2017). Beyond OCR: Handwritten manuscript attribute understanding [Groningen]: University of Groningen
  25. Pawara, P., Okafor, E., Surinta, O., Schomaker, L., & Wiering, M. (2017). Comparing Local Descriptors and Bags of Visual Words to Deep Convolutional Neural Networks for Plant Recognition. In 6th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2017) ICPRAM .
  26. Köder, F., van der Meer, J. W., & Spenader, J. (2017). The interpretation of Dutch direct speech reports by Frisian-Dutch bilinguals. In M. Wieling, M. Kroon, G. van Noord, & G. Bauma (Eds.), From Semantics to Dialectometry: Festschrift for John Nerbonne. Tributes (Vol. 32, pp. 171-179). College Publications.
  27. Wagenaar, M., Okafor, E., Frencken, W., & Wiering, M. (2017). Using Deep Convolutional Neural Networks to Predict Goal-Scoring Opportunities in Soccer. In International Conference on Pattern Recognition Applications and Methods (ICPRAM)
  28. 2016
  29. Tijsma, A., Drugan, M., & Wiering, M. (2016). Comparing Exploration Strategies for Q-learning in Random Stochastic Mazes. In Adaptive Dynamic Programming and Reinforcement Learning (Symposium Series on Computational Intelligence).
  30. Pieters, M., & Wiering, M. (2016). Q-learning with Experience Replay in a Dynamic Environment. In Adaptive Dynamic Programming and Reinforcement Learning (ADPRL) (Symposium Series on Computational Intelligence (SSCI)).
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