Publications

2024

Benkert, M., Heroth, M., Herrler, R., Gregorova, M. & Schmid, H. C. (2024). Variational autoencoder-based techniques for a streamlined cross-topology modeling and optimization workflow in electrical drives. Auton. Intell. Syst. 4, 8. https://doi.org/10.1007/s43684-024-00065-x

Boget, Y., Gregorova, M., & Kalousis, A. (2024). Discrete Graph Auto-Encoder. Transactions on Machine Learning Research. https://openreview.net/pdf?id=bZ80b0wb9d

Chizhov, P.;, Arnett, C.; Korotkova, E.;, Yamshchikov, I. P. (2024). BPE Gets Picky: Efficient Vocabulary Refinement During Tokenizer Training. The 2024 Conference on Empirical Methods in Natural Language Processing. Miami, Florida. https://doi.org/10.48550/arXiv.2409.04599

Fetzer, T.; Bullmann, M.; Kastner, S.; Deinzer, F.; Grzegorzek, M. (2024). Advancing Smartphone-based Indoor Positioning through Particle Distribution Optimization. Will be published at “FUSION 2024 - International Conference on Information Fusion. July 2024. Venice, Italy”. Proceedings will be published by IEEE.

Röder, M.; Münch, M.; Raab, C. and Schleif, F-M. (2024). Crossing Domain Borders with Federated Few-Shot Adaptation.  Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods 2024, Rome, Italy, 511-521. https://doi.org/10.5220/0012351900003654

Röder, M., & Schleif, F.-M. (forthcoming). Sparse Uncertainty-Informed Sampling from Federated Streaming Data. 32nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2024, Bruges, Belgium, October 9-11, 2024.
(Public link will be provided soon)

Röder, M., & Schleif, F.-M.. Deep Transfer Hashing for Adaptive Learning on Federated Streaming Data. 8th International Workshop and Tutorial on Interactive Adaptive Learning, ECML PKKD 2024, Vilnius, Lithuania, September 9, 2024.
(Public link will be provided soon)

Sorokovikova, A.; Rezagholi, S.; Fedorova, N.; Yamshchikov, I.P. (2024). LLMs Simulate Big5 Personality Traits: Further Evidence. In Proceedings of the 1st Workshop on Personalization of Generative AI Systems (PERSONALIZE 2024), St. Julians, Malta. Association for Computational Linguistics, 83–87. https://aclanthology.org/2024.personalize-1.7/

Sorokovikova, A.; Becker, M. and Yamshchikov, I.P. (2024). Echo-chambers and Idea Labs: Communication Styles on Twitter. In Proceedings of the Second Workshop on Natural Language Processing for Political Sciences at LREC-COLING 2024, pages 91–95, Torino, Italia. ELRA and ICCL. https://aclanthology.org/2024.politicalnlp-1.10/

Surkov, M. K.; Yamshchikov, I. P. (2024). Vygotsky Distance: Measure for Benchmark Task Similarity. LREC-COLING 2024 - The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, Turin, Italy. https://aclanthology.org/2024.lrec-main.1513/

Tikhonov, A.; Bylinina, L.; Yamshchikov, I. P. (2024). Individuation in Neural Models with and without Visual Grounding. NLP4Science Workshop at The 2024 Conference on Empirical Methods in Natural Language Processing. Miami, Florida. https://doi.org/10.48550/arXiv.2409.18868

Osmonova, T.; Tikhonov, A. and Yamshchikov, I. P. (2024). Knowledge Graph Representation for Political Information Sources. In Proceedings of the Second Workshop on Natural Language Processing for Political Sciences at LREC-COLING 2024, pages 45–54, Torino, Italia. ELRA and ICCL. https://aclanthology.org/2024.politicalnlp-1.6/

Varghese C.; Koshelev, S.; and Yamshchikov, I. P. (2024). Neural Machine Translation for Malayalam Paraphrase Generation. In Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, St. Julian's, Malta, Association for Computational Linguistics, 10–15. https://aclanthology.org/2024.dravidianlangtech-1.2/

Väth, P.; Fruehwald, A. M.; Paassen, B.; Gregorova, M. (2024). GradCheck: Analyzing classifier guidance gradients for conditional diffusion sampling. https://doi.org/10.48550/arXiv.2406.17399

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

Fetzer, T., Bullmann, M., Ebner, M., Kastner, S., Deinzer, F., Grzegorzek, M. (2023) Interacting Multiple Model Particle Filter for Indoor Positioning Applications. Proceedings of the 2023 International Technical Meeting of The Institute of Navigation, Long Beach, California, January 2023, pp. 1089-1100. https://doi.org/10.33012/2023.18639

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: cSynchronized IMU Dataset of Walking People at Six Body Locations. 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

Meißner, P., Dillmann, R. (2023). Implicit Shape Model Trees: Recognition of 3-D Indoor Scenes and Prediction of Object Poses for Mobile Robots. Robotics 2023, 12, 158. https://doi.org/10.3390/robotics12060158

Mosin, V., Samenko I., Kozlovskii, B., Tikhonov, A., Yamshchikov, I.P. (2023). Fine-tuning transformers: Vocabulary transfer. Artificial Intelligence, Volume 317, 103860. https://doi.org/10.1016/j.artint.2023.103860

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

Röder, M., Heller, L., Münch, M., Schleif, F.-M. (2023). Efficient Cross-Domain Federated Learning by MixStyle Approximation. https://doi.org/10.48550/arXiv.2312.07064

Shibaev, V.; Olbrich, E.; Jost, J.; Yamshchikov, I.P. (2023). Quick Estimate of Information Decomposition for Text Style Transfer. Entropy 2023, 25, 322. https://doi.org/10.3390/e25020322

Tikhonov, A., Yamshchikov, I. P. (2023). Post Turing: Mapping the landscape of LLM Evaluation. https://aclanthology.org/2023.gem-1.31/

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/

Zhemchuzhina, E., Filippov, N., Yamshchikov I. P. (2023). Pragmatic Constraint on Distributional Semantics. https://doi.org/10.48550/arXiv.2211.11041

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