Publications

2023

Boget, Y., Gregorova, M., Kalousis, A. (2023). Vector-Quantized Graph Auto-Encoder. https://doi.org/10.48550/arXiv.2306.07735

Bykova, A., Filippov, N., & Yamshchikov, I. P. (2023). Rehabilitating Homeless: Dataset and Key Insights. Proceedings of the AAAI Conference on Artificial Intelligence, 37(12), 14136-14143. https://doi.org/10.1609/aaai.v37i12.26654

Holomjova, V., Starkey, A. J., & Meißner, P. (2023). GSMR-CNN: An End-to-End Trainable Architecture for Grasping Target Objects from Multi-Object Scenes [Paper presentation]. ICRA 2023: IEEE International Conference on Robotics and Automation, London, United Kingdom. https://ieeexplore.ieee.org/document/10161009

Holomjova, V., Starkey A. J., Yun B., & Meißner, P. (2023). One-Shot Learning for Task-Oriented Grasping. IEEE Robotics and Automation Letters, 8(12), 8232-8238. doi:10.1109/LRA.2023.3326001

Kastner, S., Ebner, M., Bullmann, M.; Fetzer, T.,Deinzer, F., Grzegorzek, M. (2023). SIMUL: conjunto de datos IMU sincronizados de personas caminando en seis ubicaciones corporales. IPIN 2023: Thirteenth International Conference on Indoor Positioning and Indoor Navigation, Nuremberg, Germany. http://ipin-conference.org/2023/papers/47.pdf

Koltun, V., Yamshchikov I. P. (2023). Pump It: Twitter Sentiment Analysis for Cryptocurrency Price Prediction. Risks, 11(9), 159. https://doi.org/10.3390/risks11090159

Münch, M., Röder, M., Schleif, F.-M. (2023). Unlocking the Potential of Non-PSD Kernel Matrices: A Polar Decomposition-based Transformation for Improved Prediction Models, Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, Birmingham, United Kingdom.

Münch, M., Bohnsack, K. S., Engelsberger, A., Schleif, F.-M., & Villmann, T. (2023). Sparse Nyström Approximation for Non-Vectorial Data Using Class-informed Landmark Selection. ESANN 2022 Proceedings. Presented at the ESANN 2022 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges (Belgium)

Münch, M., Röder, M., Heilig, S., Raab, C., & Schleif, F.-M. (2023). Static and adaptive subspace information fusion for indefinite heterogeneous proximity data. Neurocomputing, 555, 126635.j.neucom.2023.126635. doi:10.1016

Väth, P., Frühwald, A. M., Paaßen, B., Gregorova, M. (2023). Diffusion-based Visual Counterfactual Explanations - Towards Systematic Quantitative Evaluation. ECML: 5th International Workshop on eXplainable Knowledge Discovery in Data Mining, Turin, Italy. https://doi.org/10.48550/arXiv.2308.06100

Yamshchikov, I. P., Tikhonov, A. (2023). What is Wrong with Language Models that Can Not Tell a Story?. Proceedings of the The 5th Workshop on Narrative Understanding. Association for Computational Linguistics, Toronto, Canada, 58–64. https://aclanthology.org/2023.wnu-1.8/

2022

Boget, Y., Gregorová, M., & Kalousis, A. (2022). GrannGAN: Graph annotation generative adversarial networks. 14th Asian Conference on Machine Learning, Hyderabad, India, https://arxiv.org/abs/2212.00449
 
Heilig, S., Münch, M., & Schleif, F.-M. (2022). Memory Efficient Kernel Approximation for Non-Stationary and Indefinite Kernels. International Joint Conference on Neural Networks, IJCNN 2022, Padua, Italy, July 18-23, 2022, 1–8. doi:10.1109/IJCNN55064.2022.9892153
 
Heusinger, M., Raab, C., & Schleif, F.-M. (2022a). Dimensionality reduction in the context of dynamic social media data streams. Evol. Syst., 13(3), 387–401. doi:10.1007/s12530-021-09396-z
 
Heusinger, M., Raab, C., & Schleif, F.-M. (2022b). Passive concept drift handling via variations of learning vector quantization. Neural Comput. Appl., 34(1), 89–100. doi:10.1007/s00521-020-05242-6
 
Heusinger, M., & Schleif, F.-M. (2022). A Streaming Approach to the Core Vector Machine. In L. Rutkowski, R. Scherer, M. Korytkowski, W. Pedrycz, R. Tadeusiewicz, & J. M. Zurada (Eds.), Artificial Intelligence and Soft Computing - 21st International Conference, ICAISC 2022, Zakopane, Poland, June 19-23, 2022, Proceedings, Part II (pp. 91–101). doi:10.1007/978-3-031-23480-4_8
 
Heusinger, M., Raab, C., Rossi, F., & Schleif, F.-M. (2022). Federated Learning - Methods, Applications and beyond. CoRR, abs/2212.11729. doi:10.48550/arXiv.2212.11729
 
Münch, M., Raab, C., Heilig, S., Röder, M., & Schleif, F.-M. (2022). Adaptive multi-modal positive semi-definite and indefinite kernel fusion for binary classification. ESANN 2022 Proceedings. Presented at the ESANN 2022 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges (Belgium) and online event. doi:10.14428/esann/2022.es2022-70
 
Oneto, L., Navarin, N., & Schleif, F.-M. (2022). Advances in artificial neural networks, machine learning and computational intelligence. Neurocomputing, 507, 311–314. doi:10.1016/j.neucom.2022.08.001
 
Raab, C., Röder, M., & Schleif, F. (2022). Domain adversarial tangent subspace alignment for explainable domain adaptation. Neurocomputing, 506, 418–429. https://doi.org/10.1016/j.neucom.2022.07.074
 
Väth, P., Münch, M., Raab, C., & Schleif, F.-M. (2022). PROVAL: A framework for comparison of protein sequence embeddings. Journal of Computational Mathematics and Data Science, 3, 100044. doi:10.1016/j.jcmds.2022.100044

2021

Boget, Y., Gregorová, M., & Kalousis, A. (2021). Permutation Equivariant Generative Adversarial Networks for Graphs. ELLIS Machine Learning for Molecule Discovery Workshop @ NeurIPS 2021 (virtual), https://cloud.ml.jku.at/s/AmSGrCQfDzXkixT.
 
Heilig, S., Münch, M., & Schleif, F.-M. (2021). Revisiting Memory Efficient Kernel Approximation: An Indefinite Learning Perspective. CoRR, abs/2112.09893. Retrieved from https://arxiv.org/abs/2112.09893
 
Heusinger, M., Raab, C., Rossi, F., & Schleif, F.-M. (2021). Federated Learning - Methods, Applications and beyond. 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2021, Online Event (Bruges, Belgium), October 6-8, 2021. doi:10.14428/esann/2021.ES2021-4
 
Heusinger, M., & Schleif, F.-M. (2021). Classification in Non-stationary Environments Using Coresets over Sliding Windows. In I. Rojas, G. Joya, & A. Català (Eds.), Advances in Computational Intelligence - 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Virtual Event, June 16-18, 2021, Proceedings, Part I (pp. 126–137). doi:10.1007/978-3-030-85030-2_11
 
Münch, M., Heilig, S., Vath, P., & Schleif, F.-M. (2021, December 5). Scalable embedding of multiple perspectives for indefinite life-science data analysis. 2021 IEEE Symposium Series on Computational Intelligence (SSCI). Presented at the 2021 IEEE Symposium Series on Computational Intelligence (SSCI), Orlando, FL, USA. doi:10.1109/ssci50451.2021.9659914
 
Münch, M., Heilig, S., & Schleif, F.-M. (2021). Multi-perspective embedding for non-metric time series classification. ESANN 2021 Proceedings. Presented at the ESANN 2021 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Online event (Bruges, Belgium). doi:10.14428/esann/2021.es2021-114
 
Raab, C., Saralajew, S., & Schleif, F.-M. (2021). Domain Adversarial Tangent Learning Towards Interpretable Domain Adaptation. 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2021, Online Event (Bruges, Belgium), October 6-8, 2021. doi:10.14428/esann/2021.ES2021-103