Werner Zellinger, Bernhard Moser, Susanne Saminger-Platz, "On generalization in moment-based domain adaptation." Annals of Mathematics and Artificial Intelligence, November 2020, https://doi.org/10.1007/s10472-020-09719-x
Shuai Lu, Peter Mathe, and Sergei V. Pereverzyev, "Randomized matrix approximation to enhance regularized projection schemes in inverse problems." Inverse Problems, Volume 36, Issue 8, id.085013, 20 pp, August 2020, https://iopscience.iop.org/article/10.1088/1361-6420/ab9c44
Ramin Nikzad-Langerodi, Werner Zellinger, Susanne Saminger-PLatz, Bernhard Moser, "Domain adaptation for regression under Beer–Lambert’s law." Knowlege-Based Systems, Volume 210, 106447, December 2020, https://doi.org/10.1016/j.knosys.2020.106447
Vihang P. Patil, Markus Hofmarcher, Marius-Constantin Dinu, Matthias Dorfer, Patrick M. Blies, Johannes Brandstetter, Jose A. Arjona-Medina, Sepp Hochreiter, "Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution." arXiv:2009.14108, 2020-09-29, https://doi.org/10.48550/arxiv.2009.14108, Software: https://ml-jku.github.io/align-rudder/
Melis, M., Scalas, M., Demontis, A., Maiorca, D., Biggio, B., Giacinto, G., and Roli, F. “Do gradient-based explanations tell anything about adversarial robustness to android malware?”, International Journal of Machine Learning and Cybernetics, Int. J. Mach. Learn. & Cyber. 13, 217–232 (2022). https://doi.org/10.1007/s13042-021-01393-7 and https://doi.org/10.48550/arXiv.2005.01452
Lukas Fischer, Lisa Ehrlinger, Verena Geist, Rudolf Ramler, Florian Sobiezky,Werner Zellinger, David Brunner, Mohit Kumar and Bernhard A. Moser, "AI System Engineering—Key Challenges and Lessons Learned" Mach. Learn. Knowl. Extr. 3, no. 1: 56-83, 2021, https://doi.org/10.3390/make3010004
Holzleitner, M., Gruber, L., Arjona-Medina, J., Brandstetter, J., Hochreiter, S. (2021). Convergence Proof for Actor-Critic Methods Applied to PPO and RUDDER. In: Hameurlain, A., Tjoa, A.M. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XLVIII. Lecture Notes in Computer Science, vol 12670. Springer, Berlin, Heidelberg, https://doi.org/10.1007/978-3-662-63519-3_5 and https://doi.org/10.48550/arXiv.2012.01399
Thomas Adler, Johannes Brandstetter, Michael Widrich, Andreas Mayr, David Kreil, Michael Kopp, Günter Klambauer, Sepp Hochreiter, “Cross-Domain Few-Shot Learning by Representation Fusion”, arXiv:2010.06498, 2020, https://doi.org/10.48550/arXiv.2010.06498, Software: https://github.com/tomte812/chef
Mohit Kumar, David Brunner, Bernhard A. Moser, Freudenthaler, B. (2020). Variational Optimization of Informational Privacy. In: , et al. Database and Expert Systems Applications. DEXA 2020. Communications in Computer and Information Science, vol 1285. Springer, Cham. https://doi.org/10.1007/978-3-030-59028-4_4
Non Peer-reviewed Journals and Contents
Natalia Shepeleva, Werner Zellinger, Michal Lewandowski, Bernhard Moser, "ReLU Code Space: A Basis for Rating Network Quality Besides Accuracy", ICLR Workshop on Neural Architecture Search, April 2020, https://doi.org/10.48550/arXiv.2005.09903
Rafa Galvez, Veelasha Moonsamy, Claudia Diaz, "Less is More: A privacy-respecting Android malware classifier using Federated Learning", Proceedings on Privacy Enhancing Technologies, pp. 96 – 116, 2021, https://doi.org/10.48550/arXiv.2007.08319
Bernhard A. Moser, "Computer Implementiertes Verfahren zur Bewertung der Integrität von Neuronalen Netzen" (Method for the integrity evaluation of neural networks) International Patent PCT/EP2019/072830, filed 27th August 2019 (priority date 10th of Sept. 2018; DPMA S2959)
Conferences / Workshops
Werner Zellinger, Volkmar Wieser, Mohit Kumar, David Brunner, Natalia Shepeleva, Rafa Galvez, Josef Langer, Lukas Fischer, and Bernhard Moser, "Beyond federated learning: On confidentiality-critical machine learning applications in industry." International Conference on Industry 4.0 and Smart Manufacturing, November 2020, https://doi.org/10.1016/j.procs.2021.01.296
David Solans, Battista Biggio, and Carlos Castillo, "Poisoning Attacks on Algorithmic Fairness." The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, September 2020, https://doi.org/10.48550/arXiv.2004.07401
Hamid Eghbal-zadeh, Khaled Koutini, Paul Primus, Verena Haunschmid, Michal Lewandowski, Werner Zellinger, Bernhard A. Moser, Gerhard Widmer, "On Data Augmentation and Adversarial Risk: An Empirical Analysis." ICLR Workshop Towards Trustworthy Machine Learning, April 2020, https://doi.org/10.48550/arXiv.2007.02650
Michael Widrich, Bernhard Schäfl, Hubert Ramsauer, Milena Pavlović, Lukas Gruber, Markus Holzleitner, Johannes Brandstetter, Geir Kjetil Sandve, Victor Greiff, Sepp Hochreiter, Günter Klambauer, "Modern Hopfield Networks and Attention for Immune Repertoire Classification", 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada, and https://doi.org/10.48550/arXiv.2007.13505, Software: https://github.com/ml-jku/DeepRC
Mohit Kumar, Michael Rossbory, Bernhard A. Moser, and Bernhard Freudenthaler, “Differentially Private Learning of Distributed Deep Models”. In Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization (UMAP '20 Adjunct). Association for Computing Machinery, New York, NY, USA, 193–200, 2020, https://doi.org/10.1145/3386392.3399562
Master and Doctoral Thesis
Werner Zellinger, "Moment-based domain adaptation: Learning bounds and algorithms." Doctoral thesis (JKU Linz), April 2020, https://doi.org/10.48550/arXiv.2004.10618
Davide Deidda, "Towards Countering Physical Adversarial Attacks On Deep Learning For Face Recognition." Master's thesis (UNI Cagliari), August 2020.