1. 2019
  2. Spenader, J., & Roest, C. (2019). Facilitating Quantifier Acquisition: Training Can Eliminate Children's Spreading Errors. In BUCLD 43: Proceedings of the 43rd annual Boston University Conference on Language Development edited by Megan M. Brown and Brady Dailey (Vol. 2, pp. 653-666). Boston, USA: Cascadilla Press.
  3. Ayoobi, H., Cao, M., Verbrugge, L., & Verheij, B. (Accepted/In press). Handling Unforeseen Failures Using Argumentation-Based Learning. In International Conference on Automation Science and Engineering (CASE) 2019 (pp. 1-8)
  4. Mohades Kasaei, H. (2019). Interactive Open-Ended Object, Affordance and Grasp Learning for Robotic Manipulation. In IEEE/RSJ International Conference on Robotics and Automation (ICRA) IEEE.
  5. van Beers, F., Lindström, A., Okafor, E., & Wiering, M. (2019). Deep Neural Networks with Intersection over Union Loss for Binary Image Segmentation. In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods (Vol. 1 ICPRAM, pp. 438-445). Prague: SciTePress. https://doi.org/10.5220/0007347504380445
  6. Ansó, N., Wiehe, A., Drugan, M., & Wiering, M. (2019). Deep Reinforcement Learning for Pellet Eating in Agar.io. In Proceedings of the 11th International Conference on Agents and Artificial Intelligence (Vol. 2, ICAART, pp. 123-133). Prague: SciTePress. https://doi.org/10.5220/0007360901230133
  7. Wolf, B., & van Netten, S. (2019). Training submerged source detection for a 2D fluid flow sensor array with Extreme Learning Machines. In Eleventh International Conference on Machine Vision (ICMV 2018) (Vol. 11041, pp. 1104126). SPIE.Digital Library. https://doi.org/10.1117/12.2522667
  8. Boulogne, L., Dijkstra, K., & Wiering, M. (2019). Extra Domain Data Generation with Generative Adversarial Nets. In Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018 (pp. 1403-1410). (Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018; Vol. 13, No. 2). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI.2018.8628701
  9. van Vugt, M. K., Moye, A., Pollock, J., Johnson, B., Bonn-Miller, M. O., Gyatso, K., ... Fresco, D. M. (2019). Tibetan Buddhist monastic debate: Psychological and neuroscientific analysis of a reasoning-based analytical meditation practice. In Imagining the Brain: Episodes in the History of Brain Research (Progress in brain research). Elsevier. https://doi.org/10.1016/bs.pbr.2018.10.018
  10. Sillitti, A., Schomaker, L., Anakabe, J. F., Basurko, J., Dam, P., Ferreira, H., ... Zurutuza, U. (2019). Providing Proactiveness: Data Analysis Techniques Portfolios. In M. Albano, E. Jantunen, G. Papa, & U. Zurutuza (Eds.), The MANTIS Book : Cyber Physical System Based Proactive Collaborative Maintenance (pp. 145-238). Gistrup (DK): River Publishers.
  11. Bosnic, A., & Spenader, J. (2019). Acquisition Path of Distributive Markers in Serbian and Dutch: Evidence from an Act-Out Task. In M. M. Brown, & B. Dailey (Eds.), Proceedings of the 43rd Boston University Conference on Language Development (pp. 94-108). Somerville, MA: Cascadilla Press.
  12. Dijkstra, K., van de Loosdrecht, J., Schomaker, L. R. B., & Wiering, M. A. (2019). CentroidNet: A Deep Neural Network for Joint Object Localization and Counting. In U. Brefeld, E. Curry, E. Daly, B. MacNamee, A. Marascu, F. Pinelli, M. Berlingerio, ... N. Hurly (Eds.), ECML PKDD 2018: Machine Learning and Knowledge Discovery in Databases (pp. 585-601). ( Lecture Notes in Computer Science; Vol. 11053). Cham: Springer. https://doi.org/10.1007/978-3-030-10997-4_36
  13. Okafor, E. (2019). Deep learning for animal recognition. [Groningen]: University of Groningen.
  14. Keshavarzi Zafarghandi, A., Verheij, B., & Verbrugge, L. (Accepted/In press). Embedding Probabilities, Utilities and Decisions in a Generalization of Abstract Dialectical Frameworks. In International Symposium on Imprecise Probability: Theories and Applications (ISIPTA)
  15. Mohades Kasaei, H. (Accepted/In press). Look Further to Recognize Better: Learning Shared Topics and Category-Specific Dictionaries for Open-Ended 3D Object Recognition. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) IEEE.
  16. Vugt, M. V. (2019). Mindfulness as a Potential Tool for Productivity. In C. Sadowski, & T. Zimmermann (Eds.), Rethinking Productivity in Software Engineering (pp. 293-302). (Rethinking Productivity in Software Engineering). Apress. https://doi.org/10.1007/978-1-4842-4221-6_25
  17. Schomaker, L., Albano, M., Jantunen, E., & Ferreira, L. L. (2019). The future of Maintenance. In M. Albano, E. Jantunen, G. Papa, & U. Zurutuza (Eds.), The MANTIS Book : Cyber Physical System Based Proactive Collaborative Maintenance (pp. 555). Gistrup (DK): River Publishers.
  18. Vugt, M. V. (2019). Using Biometric Sensors to Measure Productivity. In C. Sadowski , & T. Zimmermann (Eds.), Rethinking Productivity in Software Engineering (pp. 159-167). (Rethinking Productivity in Software Engineering). Berkeley, CA: Apress. https://doi.org/10.1007/978-1-4842-4221-6_14
  19. 2018
  20. Lieto, A., Kennedy, W. G., Lebiere, C., Romero, O. J., Taatgen, N., & West, R. L. (2018). Higher-level Knowledge, Rational and Social Levels Constraints of the Common Model of the Mind. In A. V. Samsonovich, & C. J. Lebiere (Eds.), Postproceedings of the 9th Annual International Conference on Biologically Inspired Cognitive Architectures, BICA 2018 (Ninth Annual Meeting of the BICA Society), held August 22-24, 2018 in Prague, Czech Republic (Vol. 145, pp. 757-764). (Procedia Computer Science). Elsevier. https://doi.org/10.1016/j.procs.2018.11.033
  21. Chanda, S., Baas, J., Haitink, D., Hamel, S., Stutzmann, D., & Schomaker, L. (2018). Zero-shot learning based approach for medieval word recognition using deep-learned features. In Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR (pp. 345-350). (Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR; Vol. 2018-August). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICFHR-2018.2018.00067
  22. Gattinger, M., & Wagemaker, J. (2018). Towards an Analysis of Dynamic Gossip in Netkat. In J. Desharnais, W. Guttmann, & S. Joosten (Eds.), Relational and Algebraic Methods in Computer Science: 17th International Conference, RAMiCS 2018, Groningen, The Netherlands, October 29 – November 1, 2018, Proceedings (pp. 280-297). (Relational and Algebraic Methods in Computer Science; Vol. 11194). Springer International Publishing, Cham, Switzerland. https://doi.org/10.1007/978-3-030-02149-8_17
  23. Okafor, E., Berendsen, G., Schomaker, L., & Wiering, M. (2018). Detection and Recognition of Badgers Using Deep Learning. In V. Kurkova, Y. Manolopoulos, B. Hammer, L. Iliadis, & I. Maglogiannis (Eds.), International Conference on Artificial Neural Networks (pp. 554-563). (Lecture Notes in Computer Science book series; Vol. 11141). Springer International Publishing, Cham, Switzerland. https://doi.org/10.1007/978-3-030-01424-7_54
Previous 1 2 3 4 5 6 7 8 ...21 Next

ID: 32572