DKE research theme

Machine Learning (ML)

Many of the most visible advances in the field of Data Science all require some form of machine learning techniques. Machine learning is a central topic of interest to DKE, with applications spanning multiple research themes.

Highlighted publications

Alvanitopoulos, P., Diplaris, S., de Gelder, B., Shvets, A., Benayoun, M., Koulali, P., Moghnieh, A., Shekhawat, Y., Stentoumis, C., Hosmer, T., Anadol, R., Borreguero, M., Martin, A., Sciama, P., Avgerinakis, K., Petrantonakis, P., Briassouli, A., Mille, S., Tellios, A., ... Kompatsiaris, I. (2019). MindSpaces: Art-driven Adaptive Outdoors and Indoors Design. In 9th International Conference on Digital Presentation and Preservation of Cultural and Scientific Heritage (DiPP): DiPP2019 (Vol. 9, pp. 391-400). Digital presentation and preservation of cultural and scientific heritage

Alvarez, F., Popa, M., Solachidis, V., Hernandez-Penaloza, G., Belmonte-Hernandez, A., Asteriadis, S., Vretos, N., Quintana, M., Theodoridis, T., Dotti, D., & Daras, P. (2018). Behavior Analysis through Multimodal Sensing for Care of Parkinson's and Alzheimer's Patients. Ieee Multimedia, 25(1), 14-25. https://doi.org/10.1109/MMUL.2018.011921232

Dotti, D., Popa, M., & Asteriadis, S. (2020). A hierarchical autoencoder learning model for path prediction and abnormality detection. Pattern Recognition Letters, 130, 216-224. https://doi.org/10.1016/j.patrec.2019.06.030

Dotti, D., Popa, M., & Asteriadis, S. (2020). Being the center of attention: A Person-Context CNN framework for Personality Recognition. Transactions on Interactive Intelligent Systems, 10(3), [19]. https://doi.org/10.1145/3338245

Ghaleb, E., Popa, M., & Asteriadis, S. (2019). Multimodal and Temporal Perception of Audio-visual Cues for Emotion Recognition. In 8th International Conference on Affective Computing & Intelligent Interaction (ACII 2019), Cambridge, United Kingdom

Ismailoglu, F., Cavill, R., Smirnov, E., Zhou, S., Collins, P., & Peeters, R. (2020). Heterogeneous Domain Adaptation for IHC Classification of Breast Cancer Subtypes. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(1), 347-353. https://doi.org/10.1109/TCBB.2018.2877755

Ismailoglu, F., Smirnov, E., Peeters, R., Zhou, S., & Collins, P. (2018). Heterogeneous Domain Adaptation Based on Class Decomposition Schemes. In D. Phung, V. Tseng, G. Webb, B. Ho, M. Ganji, & L. Rashidi (Eds.), Advances in Knowledge Discovery and Data Mining: PAKDD 2018 (pp. 169-182). Springer. Lecture Notes in Computer Science Vol. 10937 https://doi.org/10.1007/978-3-319-93034-3_14

Jin, H., Goossens, P., Juhasz, P., Eijgelaar, W-J., Manca, M., Karel, J., Smirnov, E., Sikkink, C. J. J. M., Mees, B., Waring, O., van Kuijk, K., Fazzi, G., Gijbels, M., Kutmon, M., Evelo, C., Hedin, U., Daemen, M. J., Sluimer, J., Matic, L. P., & Biessen, E. (2021). Integrative multiomics analysis of human atherosclerosis reveals a serum response factor-driven network associated with intraplaque hemorrhage. Clinical and Translational Medicine, 11(6), [e458]. https://doi.org/10.1002/ctm2.458

Kroner, A., Senden, M., Driessens, K., & Goebel, R. (2020). Contextual encoder-decoder network for visual saliency prediction. Neural Networks, 129, 261-270. https://doi.org/10.1016/j.neunet.2020.05.004

Mehrkanoon, S. (2018). Indefinite kernel spectral learning. Pattern Recognition, 78, 144-153.

Mehrkanoon, S. (2019). Deep neural-kernel blocks. Neural Networks, 116, 46-55. https://doi.org/10.1016/j.neunet.2019.03.011

Montulet, R., & Briassouli, A. (2019). Deep Learning for Robust end-to-end Tone Mapping. In British Machine Vision Conference Proceedings https://bmvc2019.org/programme/detailed-programme/

Montulet, R., & Briassouli, A. (2020). Densely Annotated Photorealistic Virtual Dataset Generation for Abnormal Event Detection. In Proceedings of the International Conference on Pattern Recognition, ICPR 2020: ICPR FGVRID Workshop: Fine-Grained Visual Recognition and re-Identification

Morsomme, R., & Smirnov, E. (2019). Conformal Prediction for Students' Grades in a Course Recommender System. In Proceedings of Machine Learning Research: Conformal and Probabilistic Prediction and Applications, 9-11 September 2019, Golden Sands, Bulgaria (Vol. 105, pp. 196-213). Proceedings of Machine Learning Research http://proceedings.mlr.press/v105/morsomme19a/morsomme19a.pdf

Nikolaev, N. Y., Smirnov, E., Stamate, D., & Zimmer, R. (2019). A regime-switching recurrent neural network model applied to wind time series. Applied Soft Computing, 80, 723-734. https://doi.org/10.1016/j.asoc.2019.04.009

Roos, N. (2018). Learning-Based Diagnosis and Repair. Communications in Computer and Information Science, 823, 1-15.

Seiler, C., Ferreira, A-M., Kronstad, L. M., Simpson, L. J., Le Gars, M., Vendrame, E., Blish, C. A., & Holmes, S. (2021). CytoGLMM: conditional differential analysis for flow and mass cytometry experiments. BMC Bioinformatics, 22(1), [137]. https://doi.org/10.1186/s12859-021-04067-x

Warnes, Z., & Smirnov, E. (2020). Course Recommender Systems with Statistical Confidence. In Proceedings of the 13th International Conference on Educational Data Mining, EDM 2020, Fully virtual conference, July 10-13, 2020 (pp. 509-515). educationaldatamining.org. https://educationaldatamining.org/files/conferences/EDM2020/papers/paper_103.pdf

Zhou, S., Smirnov, E., Schoenmakers, G., Peeters, R., & Wu, X. (2020). Conformal Feature-Selection Wrappers and ensembles for negative-transfer avoidance. Neurocomputing, 397, 309-319. https://doi.org/10.1016/j.neucom.2019.09.105

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