Benedikt Tscheschner, Eduardo Veas, Mars Masana
Incremental Learning with Repetition via Pseudo-Feature Projection
28th Computer Vision Winter Workshop. Graz, Austria, February 12–14, 2025
Proceedings of the 28th Computer Vision Winter Workshop
Gianluca Guglielmo, Marc Masana
Leveraging Intermediate Representations for Better Out-of-Distribution Detection
28th Computer Vision Winter Workshop. Graz, Austria, February 12–14, 2025
Proceedings of the 28th Computer Vision Winter Workshop
Edi Muskardin, Martin Tappler, Bernhard K. Aichernig, Ingo Pill
Active model learning of stochastic reactive systems (extended version)
Softw. Syst. Model. 23(2): 503-524
https://doi.org/10.1007/s10270-024-01158-0
Edi Muskardin, Martin Tappler, Ingo Pill, Bernhard K. Aichernig, Thomas Pock
On the Relationship Between RNN Hidden-State Vectors and Semantic Structures
ACL (Findings) 2024: 5641-5658
https://doi.org/10.18653/v1/2024.findings-acl.335
Edi Muskardin, Tamim Burgstaller, Martin Tappler, Bernhard K. Aichernig
Active Model Learning of Git Version Control System
ICSTW 2024: 78-82
https://doi.org/10.1109/ICSTW60967.2024.00024
Martin Tappler, Edi Muskardin, Bernhard K. Aichernig, Bettina Könighofer
Learning Environment Models with Continuous Stochastic Dynamics – with an Application to Deep RL Testing
ICST 2024: 197-208
https://doi.org/10.1109/ICST60714.2024.00026
Ozan Özdenizci, Robert Legenstein
Adversarially Robust Spiking Neural Networks Through Conversion.
Trans. Mach. Learn. Res. 2024 (2024)
https://openreview.net/forum?id=I8FMYa2BdP
Jasmin Viktoria Gritsch, Robert Legenstein, Ozan Özdenizci
Preserving Real-World Robustness of Neural Networks Under Sparsity Constraints.
ECML/PKDD (5) 2024: 337-354
https://link.springer.com/chapter/10.1007/978-3-031-70362-1_20
Maximilian Baronig, Romain Ferrand, Silvester Sabathiel, Robert Legenstein
Advancing Spatio-Temporal Processing in Spiking Neural Networks through Adaptation. CoRR abs/2408.07517 (2024)
2408.07517
Andrea Pferscher, Benjamin Wunderling, Bernhard K. Aichernig, Edi Muskardin
Mining Digital Twins of a VPN Server
FMDT@FM
https://ceur-ws.org/Vol-3507/paper6.pdf
Edi Muskardin, Martin Tappler, Bernhard K. Aichernig
Testing-based Black-box Extraction of Simple Models from RNNs and Transformers
ICGI: 291-294
https://proceedings.mlr.press/v217/muskardin23a.html
Edi Muskardin, Martin Tappler, Bernhard K. Aichernig, Ingo Pill
Reinforcement Learning Under Partial Observability Guided by Learned Environment Models
iFM: 257-276
https://doi.org/10.1007/978-3-031-47705-8_14
Edi Muskardin, Martin Tappler, Ingo Pill, Bernhard K. Aichernig, Thomas Pock
On the Relationship Between RNN Hidden State Vectors and Semantic Ground Truth
CoRR abs/2306.16854
https://doi.org/10.48550/arXiv.2306.16854
Martin Tappler, Edi Muskardin, Bernhard K. Aichernig, Bettina Könighofer
Learning Environment Models with Continuous Stochastic Dynamics
CoRR abs/2306.17204
https://doi.org/10.48550/arXiv.2306.17204
Bettina Könighofer, Julian Rudolf, Alexander Palmisano, Martin Tappler, Roderick Bloem
Online shielding for reinforcement learning.
Innov. Syst. Softw. Eng. 19(4): 379-394 (2023)
https://doi.org/10.1007/s11334-022-00480-4
Martin Tappler, Bernhard K. Aichernig
Differential Safety Testing of Deep RL Agents Enabled by Automata Learning.
AISoLA 2023: 138-159
https://doi.org/10.1007/978-3-031-46002-9_8
Ozan Özdenizci, Robert Legenstein
Restoring Vision in Adverse Weather Conditions With Patch-Based Denoising Diffusion Models.
IEEE Trans. Pattern Anal. Mach. Intell. 45(8): 10346-10357 (2023)
https://ieeexplore.ieee.org/document/10021824/
Philipp Hallgarten, David Bethge, Ozan Özdenizci, Tobias Grosse-Puppendahl, Enkelejda Kasneci
TS-MoCo: Time-Series Momentum Contrast for Self-Supervised Physiological Representation Learning.
EUSIPCO 2023: 1030-1034
https://ieeexplore.ieee.org/document/10289753
Francisco Javier Klaiber Aboitiz, Robert Legenstein, Ozan Özdenizci
Interaction of Generalization and Out-of-Distribution Detection Capabilities in Deep Neural Networks.
ICANN (10) 2023: 248-259
https://link.springer.com/chapter/10.1007/978-3-031-44204-9_21
Edi Muskardin, Bernhard K. Aichernig, Ingo Pill, Andrea Pferscher, Martin Tappler
AALpy: an active automata learning library
Innov. Syst. Softw. Eng. 18(3): 417-426
https://doi.org/10.1007/s11334-022-00449-3
Edi Muskardin, Bernhard K. Aichernig, Ingo Pill, Martin Tappler
Learning Finite State Models from Recurrent Neural Networks
IFM: 229-248
https://doi.org/10.1007/978-3-031-07727-2_13
Martin Tappler, Stefan Pranger, Bettina Könighofer, Edi Muskardin, Roderick Bloem, Kim G. Larsen
Automata Learning Meets Shielding
ISoLA (1): 335-359
https://doi.org/10.1007/978-3-031-19849-6_20
Bernhard K. Aichernig, Edi Muskardin, Andrea Pferscher
Active vs. Passive: A Comparison of Automata Learning Paradigms for Network Protocols
FMAS/ASYDE@SEFM: 1-19
https://doi.org/10.4204/EPTCS.371.1
Edi Muskardin, Martin Tappler, Bernhard K. Aichernig, Ingo Pill
Reinforcement Learning under Partial Observability Guided by Learned Environment Models
CoRR abs/2206.11708
https://arxiv.org/abs/2206.11708
Martin Tappler, Stefan Pranger, Bettina Könighofer, Edi Muskardin, Roderick Bloem, Kim G. Larsen
Automata Learning meets Shielding
CoRR abs/2212.01838
https://arxiv.org/abs/2212.01838
Luca Gazzola, Leonardo Mariani, Matteo Orrù, Mauro Pezzè, Martin Tappler:
Testing Software in Production Environments with Data from the Field.
ICST 2022: 58-69
https://doi.org/10.1109/ICST53961.2022.00017
Martin Tappler, Filip Cano Córdoba, Bernhard K. Aichernig, Bettina Könighofer:
Search-Based Testing of Reinforcement Learning.
IJCAI 2022: 503-510
https://doi.org/10.24963/ijcai.2022/72
Martin Tappler, Bernhard K. Aichernig, Florian Lorber:
Timed Automata Learning via SMT Solving.
NFM 2022: 489-507
https://doi.org/10.1007/978-3-031-06773-0_26
Bernhard K. Aichernig, Sandra König, Cristinel Mateis, Andrea Pferscher, Dominik Schmidt, Martin Tappler:
Constrained Training of Recurrent Neural Networks for Automata Learning.
SEFM 2022: 155-172
https://doi.org/10.1007/978-3-031-17108-6_10
Martin Tappler, Filip Cano Córdoba, Bernhard K. Aichernig, Bettina Könighofer:
Search-Based Testing of Reinforcement Learning.
CoRR abs/2205.04887 (2022)
https://doi.org/10.48550/arXiv.2205.04887
Bettina Könighofer, Julian Rudolf, Alexander Palmisano, Martin Tappler, Roderick Bloem:
Online Shielding for Reinforcement Learning.
CoRR abs/2212.01861 (2022)
https://doi.org/10.48550/arXiv.2212.01861
Ozan Özdenizci, Robert Legenstein
Improving Robustness Against Stealthy Weight Bit-Flip Attacks by Output Code Matching.
CVPR 2022: 13378-13387
https://ieeexplore.ieee.org/document/9880223
David Bethge, Philipp Hallgarten, Ozan Özdenizci, Ralf Mikut, Albrecht Schmidt, Tobias Grosse-Puppendahl
Exploiting Multiple EEG Data Domains with Adversarial Learning.
EMBC 2022: 3154-3158
https://ieeexplore.ieee.org/document/9871743/
David Bethge, Philipp Hallgarten, Tobias Grosse-Puppendahl, Mohamed Kari, Ralf Mikut, Albrecht Schmidt, Ozan Özdenizci
Domain-Invariant Representation Learning from EEG with Private Encoders.
ICASSP 2022: 1236-1240
https://ieeexplore.ieee.org/document/9747398/
David Bethge, Philipp Hallgarten, Tobias Grosse-Puppendahl, Mohamed Kari, Lewis L. Chuang, Ozan Özdenizci, Albrecht Schmidt
EEG2Vec: Learning Affective EEG Representations via Variational Autoencoders.
SMC 2022: 3150-3157
https://ieeexplore.ieee.org/document/9945517/
Thomas Limbacher, Ozan Özdenizci, Robert Legenstein
Memory-enriched computation and learning in spiking neural networks through Hebbian plasticity.
CoRR abs/2205.11276 (2022)
https://doi.org/10.48550/arXiv.2205.11276
Ozan Özdenizci, Robert Legenstein
Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Models.
CoRR abs/2207.14626 (2022)
https://doi.org/10.48550/arXiv.2207.14626
Edi Muskardin, Bernhard K. Aichernig, Ingo Pill, Andrea Pferscher, Martin Tappler
AALpy: An Active Automata Learning Library
ATVA: 67-73
https://doi.org/10.1007/978-3-030-88885-5_5
Bernhard K. Aichernig, Edi Muskardin, Andrea Pferscher
Learning-Based Fuzzing of IoT Message Brokers
ICST: 47-58
https://doi.org/10.48550/arXiv.2206.11708
Martin Tappler, Edi Muskardin, Bernhard K. Aichernig, Ingo Pill
Active Model Learning of Stochastic Reactive Systems
SEFM: 481-500
https://doi.org/10.1007/978-3-030-92124-8_27
Bettina Könighofer, Julian Rudolf, Alexander Palmisano, Martin Tappler, Roderick Bloem
Online Shielding for Stochastic Systems.
NFM 2021: 231-248
https://doi.org/10.1007/978-3-030-76384-8_15
Stefan Pranger, Bettina Könighofer, Martin Tappler, Martin Deixelberger, Nils Jansen, Roderick Bloem
Adaptive Shielding under Uncertainty.
ACC 2021: 3467-3474
https://doi.org/10.23919/ACC50511.2021.9482889
Albin Soutif–Cormerais, Marc Masana, Joost Van de Weijer, Bartłomiej Twardowski
“On the importance of cross-task features for class-incremental learning”
International Conference on Machine Learning Workshop (ICML-W), 2021
https://arxiv.org/abs/2106.11930
https://github.com/AlbinSou/cross_task_cil
Ozan Özdenizci and Robert Legenstein
“Training adversarially robust sparse networks via Bayesian connectivity sampling”
International Conference on Machine Learning (ICML), 2021
http://proceedings.mlr.press/v139/ozdenizci21a/ozdenizci21a.pdf
Thomas Pinetz, Erich Kobler, Christian Doberstein, Benjamin Berkels, Alexander Effland
“Total Deep Variation for Noisy Exit Wave Reconstruction in Transmission Electron Microscopy”
SSVM 2021: Scale Space and Variational Methods in Computer Vision pp 491-502
https://link.springer.com/chapter/10.1007/978-3-030-75549-2_39
Ozan Özdenizci and Deniz Erdoğmuş
“Stochastic mutual information gradient estimation for dimensionality reduction networks.”
Information Sciences, vol 570, pages 298-305, 2021.
Anubhab Baksi, Jakub Breier, Yi Chen and Xiaoyang Dong
“Machine Learning Assisted Differential Distinguishers For Lightweight Ciphers.”
In Design, Automation and Test in Europe (DATE). IEEE, February 2021.
Alexander Effland, Behrend Heeren, Martin Rumpf, Benedikt Wirth
“Consistent curvature approximation on Riemannian shape spaces”
IMA Journal of Numerical Analysis, draa092, https://doi.org/10.1093/imanum/draa092
04 January 2021
Bettina Könighofer, Julian Rudolf, Alexander Palmisano, Martin Tappler, Roderick Bloem
“Online Shielding for Stochastic Systems.”
CoRR, vol. abs/2012.09539, 2020. [Online]. Available: https://arxiv.org/abs/2012.09539
17 December 2020
Jakub Breier, Adrian Baldwin, Helen Balinsky, Yang Liu
„Risk Management Framework for Machine Learning Security“
CoRR, vol. abs/2012.04884, 2020. [Online]. Available: https://arxiv.org/abs/2012.04884
9 Dec 2020
Thomas Pinetz and Erich Kobler and Thomas Pock and Alexander Effland
“Shared prior learning of energy-based models for image reconstruction,”
CoRR, vol. abs/2011.06539, 2020. [Online]. Available: https://arxiv.org/abs/2011.06539
B. Könighofer, F. Lorber, N. Jansen, and R. Bloem
“Shield synthesis for reinforcement learning,”
Leveraging Applications of Formal Methods, Verification and Validation: Verification Principles – 9th International Symposium on Leveraging Applications of Formal Methods
ISoLA 2020, Rhodes, Greece, October, 2020, Proceedings, Part {I}
[Online]. Available: https://doi.org/10.1007/978-3-030-61362-4 16
B. Aichernig, E. Muskardin, and A. Pferscher
”Learning-based fuzzing of iot message brokers.”
In print
R. Bloem, P. G. Jensen, B. Könighofer, K. G. Larsen, F. Lorber, and A. Palmisano
“It’s time to play safe: Shield synthesis for timed systems”
CoRR, vol. abs/2006.16688, 2020. [Online]. Available: https://arxiv.org/abs/2006.1668
S. Pranger, B. Könighofer, M. Tappler, M. Deixelberger, N. Jansen, and R. Bloem
”Adaptive shielding under uncertainty”
CoRR, vol. abs/2010.03842, 2020. [Online]. Available: https://arxiv.org/abs/2010.03842