1. 2019
  2. Biehl, M., Caticha, N., Opper, M., & Villmann, T. (2019). Statistical Physics of Learning and Inference. In M. Verleysen (Ed.), Proc. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning : ESANN 2019 Ciaco - i6doc.com.
  3. Biehl, M. (2019). Supervised Learning - An Introduction: Lectures given at the 30th Canary Islands Winter School of Astrophysics. (Machine Learning Reports; Vol. 01/2019). Mittweida, Germany: Machine Learning Reports.
  4. Blaauw, F., Overbeek, R., Albers, T., Vlek, J., Maessen, M., Gooijer, J., ... Lazovik, A. (2019). ECiDA: Evolutionary Changes in Data Analysis. Poster session presented at ICT.Open, Hilversum, Netherlands. https://doi.org/10.13140/RG.2.2.33143.47524
  5. van Enter, A. C. D., Kimura, B., Ruszel, W., & Spitoni, C. (2019). Nucleation for One-Dimensional Long-Range Ising Models. Journal of Statistical Physics, 174(6), 1327-1345. https://doi.org/10.1007/s10955-019-02238-y
  6. Biehl, M., Abadi, F., Göpfert, C., & Hammer, B. (2019). Prototype-based classifiers in the presence of concept drift: A modelling framework. ArXiv, 1903.07273 (1903.07273 ).
  7. 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
  8. 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
  9. 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
  10. Costa, A. C., Barufaldi, B., Borges, L. R., Biehl, M., Maidment, A. D. A., & Vieira, M. A. C. (2019). Analysis of feature relevance using an image quality index applied to digital mammography. In SPIE Medical Imaging 2019 (Vol. 10948). [109485R] San Diego, CA, USA: Society of Photo-Optical Instrumentation Engineers (SPIE). https://doi.org/10.1117/12.2512975
  11. Wenniger, G. M. D. B., Schomaker, L., & Way, A. (2019). No Padding Please: Efficient Neural Handwriting Recognition. In 2019 International Conference on Document Analysis and Recognition (ICDAR) (pp. 355-362). IEEE. https://doi.org/10.1109/ICDAR.2019.00064
  12. Pfannschmidt, L., Jakob, J., Biehl, M., Tino, P., & Hammer, B. (2019). Feature Relevance Bounds for Ordinal Regression. In 2019 International Conference on Document Analysis and Recognition (ICDAR) IEEE.
  13. Marlevi, D., Ruijsink, B., Balmus, M., Dillon-Murphy, D., Fovargue, D., Pushparajah, K., ... Nordsletten, D. A. (2019). Estimation of Cardiovascular Relative Pressure Using Virtual Work-Energy. Scientific Reports, 9, [1375]. https://doi.org/10.1038/s41598-018-37714-0
  14. 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
  15. Loizou, C., Karastoyanova, D., & Schizas, C. N. (2019). Measuring the impact of blockchain on healthcare applications. In N. Petkov, N. Strisciuglio, & C. M. Travieso (Eds.), Proceedings of APPIS 2019 - 2nd International Conference on Applications of Intelligent Systems [34] (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3309772.3309806
  16. 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 (Vol. 244, pp. 233-253). (Progress in brain research). Elsevier. https://doi.org/10.1016/bs.pbr.2018.10.018
  17. Ionica, S., Kilicer, P., Lauter, K., Garcia, E. L., Massierer, M., Manzateanu, A., & Vincent, C. (2019). Modular invariants for genus 3 hyperelliptic curves. Research in Number Theory, 5(9). https://doi.org/10.1007/s40993-018-0146-6
  18. Proietti, C., Grossi, D., Smets, S., & Velázquez-Quesada, F. R. (2019). Bipolar Argumentation Frameworks, Modal Logic and Semantic Paradoxes. In P. Blackburn, E. Lorini, & M. Guo (Eds.), Logic, Rationality, and Interaction - 7th International Workshop, LORI 2019, Proceedings (pp. 214-229). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11813 LNCS). SPRINGER. https://doi.org/10.1007/978-3-662-60292-8_16
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