• Prof. Dr.

ASJA FISCHER

  • Maschinelles Lernen

Ruhr-University Bochum
Faculty of Computer Science
Machine Learning / Algorithmics
Universitätsstr. 150

44801 Bochum

 

Room:  MC 5.124

Tel:      +49 (0)234 32-23207

E-Mail: asja.fischer@rub.de

Office hours: By Arrangement

Table of Contents

About Me

Before becoming a professor in Bochum I was an assistant professor at RUB, akademische Rätin (assistant professor) at Bonn university, and a post-doctoral researcher at the Montreal Institute for Learning Algorithms (MILA). Between 2010 and 2015, I was employed both at the Institute for Neural Computation at the Ruhr-University Bochum and the Department of Computer Science at the University of Copenhagen working on my PhD, which I defended in Copenhagen in 2014. Before, I studied Biology. Bioinformatics, Mathematics, and Cognitive Science at the Ruhr-University Bochum, the Universidade de Lisboa, and the University of Osnabrück.

Publications

2024

[1]
J. Ricker, S. Damm, T. Holz, und A. Fischer, „Towards the detection of diffusion model deepfakes“, in Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications – (Volume 4), Rom, März 2024, S. 446–457. doi: 10.5220/0012422000003660.
[2]
S. Däubener, A. Fischer, T. Glasmachers, und S. Mandt, „Leveraging stochasticity to increase the robustness of artificial neural networks“, Universitätsbibliothek, Ruhr-Universität Bochum, Bochum, 2024. doi: 10.13154/294-12582.
[3]
J. Ricker, D. Assenmacher, T. Holz, A. Fischer, und E. Quiring, „AI-generated faces in the real world: a large-scale case study of twitter profile images“, in Proceedings of the 27th international symposium on research in attacks, intrusions and defenses, Padua, Italien, Sep. 2024, S. 513–530. doi: 10.1145/3678890.3678922.
[4]
S. Mavali, J. Ricker, D. Pape, Y. Sharma, A. Fischer, und L. Schönherr, „Fake it until you break it: on the adversarial robustness of AI-generated image detectors“, 2. Oktober 2024. https://arxiv.org/abs/2410.01574
[5]
M. Laszkiewicz, J. Ricker, J. Lederer, und A. Fischer, „Single-model attribution of generative models through final-layer inversion“, in Proceedings of the 41st International Conference on Machine Learning (ICML 2024), 2024, Bd. 235, S. 26007–26042. [Online]. Verfügbar hier: 
[6]
J. Ricker, D. Lukovnikov, und A. Fischer, „AEROBLADE: training-free detection of latent diffusion images using autoencoder reconstruction error“, 31. Januar 2024.
[7]
J. Ricker, D. Lukovnikov, und A. Fischer, „AEROBLADE: training-free detection of latent diffusion images using autoencoder reconstruction error“, in Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, Sep. 2024, Bd. 2024. doi: 10.1109/cvpr52733.2024.00872.
[8]
J. Frank u. a., „A representative study on human detection of artificially generated media across countries“, in 2024 IEEE Symposium on Security and Privacy (SP), San Francisco, Sep. 2024, S. 55–73. doi: 10.1109/sp54263.2024.00159.
[1]
M.-R. Amini, S. Canu, A. Fischer, T. Guns, P. K. Novak, und G. Tsoumakas, Hrsg., Machine learning and knowledge discovery in databases, Part IV: European conference, ECML PKDD 2022, Grenoble, France, September 19–23, 2022, proceedings. Cham: Springer Cham, 2023. doi: 10.1007/978-3-031-26412-2.
[2]
K. Maag und A. Fischer, „Uncertainty-weighted loss functions for improved adversarial attacks on semantic segmentation“, 22. Mai 2023.
[3]
K. Maag und A. Fischer, „Uncertainty-weighted Loss Functions for Improved Adversarial Attacks on Semantic Segmentation“, 26. Oktober 2023.
[4]
M. Laszkiewicz, J. Ricker, J. Lederer, und A. Fischer, „Single-model attribution of generative models through final-layer inversion“, 26. Mai 2023.
[5]
J. Frank u. a., „A representative study on human detection of artificially generated media across countries“, 10. Dezember 2023.
[6]
M.-R. Amini, S. Canu, A. Fischer, T. Guns, P. Kralj Novak, und G. Tsoumakas, Hrsg., Machine Learning and Knowledge Discovery in Databases. Cham: Springer Nature Switzerland, 2023. doi: 10.1007/978-3-031-26422-1.
[1]
S. Däubener und A. Fischer, „How sampling impacts the robustness of stochastic neural networks“, Apr. 2022. doi: 10.48550/arxiv.2204.10839.
[2]
N. Zengeler, T. Glasmachers, A. Fischer, und U. Handmann, „Transfer meta learning: Herausforderungen der Mustererkennung“, Universitätsbibliothek, Ruhr-Universität Bochum, Bochum, 2022. doi: 10.13154/294-9296.
[3]
F. Linsner, L. Adilova, S. Däubener, M. Kamp, und A. Fischer, „Approaches to uncertainty quantification in federated deep learning“, in Machine learning and principles and practice of knowledge discovery in databases, online, Feb. 2022, Bd. 1524, S. 128–145. doi: 10.1007/978-3-030-93736-2_12.
[4]
J. Ricker, S. Damm, T. Holz, und A. Fischer, „Towards the detection of diffusion model deepfakes“, 26. Oktober 2022. https://arxiv.org/abs/2210.14571
[1]
K. Brügge, A. Fischer, und C. Igel, „On the convergence of the Metropolis algorithm with fixed-order updates for multivariate binary probability distributions“, in International Conference on Artificial Intelligence and Statistics, Online, 2021, Bd. 130, S. 469–477. [Online]. Verfügbar unter: https://proceedings.mlr.press/v130/brugge21a.html
[2]
D. Lukovnikov, S. Däubener, und A. Fischer, „Detecting compositionally out-of-distribution examples in semantic parsing“, in Findings of the association for computational linguistics – findings of ACL: EMNLP 2021, Dez. 2021, S. 591–598. doi: 10.18653/v1/2021.findings-emnlp.54.
[3]
A. P. Raulf, S. Däubener, B. L. Hack, A. Mosig, und A. Fischer, „SmoothLRP: smoothing LRP by averaging over stochastic input variations“, in 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, online, Okt. 2021, S. 599–604. doi: 10.14428/esann/2021.es2021-139.
[1]
J. Frank, T. Eisenhofer, L. Schönherr, A. Fischer, D. Kolossa, und T. Holz, „Leveraging frequency analysis for deep fake image recognition“, 26. Juni 2020. https://arxiv.org/pdf/2003.08685.pdf
[2]
S. Däubener, L. Schönherr, A. Fischer, und D. Kolossa, „Detecting adversarial examples for speech recognition via uncertainty quantification“, in Cognitive intelligence for speech processing, Okt. 2020, S. 4661–4665. doi: 10.21437/interspeech.2020-2734.
[3]
J. Frank, T. Eisenhofer, L. Schönherr, A. Fischer, D. Kolossa, und T. Holz, „Leveraging frequency analysis for deep fake image recognition“, in 37th International Conference on Machine Learning (ICML 2020), Online, 2020, Bd. 119, S. 3205–3216.
[4]
S. Däubener, J. Frank, T. Holz, und A. Fischer, „Efficient calculation of adversarial examples for bayesian neural networks “, 2020. [Online]. Verfügbar unter: https://openreview.net/pdf?id=0KlHSyXxHDW
[1]
A. Kristiadi, S. Däubener, und A. Fischer, „Uncertainty quantification with compound density networks“, gehalten auf der Conference on Neural Information Processing Systems (NeurIPS), Vancouver, 13. Dezember 2019, Publiziert. [Online]. Verfügbar unter: https://casa.rub.de/en/research/publications/detail/uncertainty-quantification-with-compound-density-network-workshop-beitrag
[2]
S. Däubener, A. Kristiadi, und A. Fischer, „Infinite ensembles for uncertainty prediction“, gehalten auf der Women in Machine Learning, Vancouver, 9. Dezember 2019, Publiziert. doi: 10.48550/arxiv.1902.01080.
[3]
A. Kristiadi, S. Däubener, und A. Fischer, „uncertainty quantification with compound density networks“, Konferenz-Abstract, 2019. [Online]. Verfügbar unter: http://bayesiandeeplearning.org/2019/papers/57.pdf
[1]
B. Weghenkel, A. Fischer, und L. Wiskott, „Graph-based predictable feature analysis“, Machine learning, Bd. 106, Nr. 9–10, S. 1359–1380, Mai 2017, doi: 10.1007/s10994-017-5632-x.
[1]B. Weghenkel, A. Fischer, und L. Wiskott, „Graph-based predictable feature analysis“, Machine learning, Bd. 106, Nr. 9–10, S. 1359–1380, Mai 2017, doi: 10.1007/s10994-017-5632-x.
[1]
J. Melchior, A. Fischer, und L. Wiskott, „How to center deep Boltzmann machines“, Journal of machine learning research, Bd. 17, Art. Nr. 99, 2016.
[1]
A. Fischer und C. Igel, „A bound for the convergence rate of parallel tempering for sampling restricted Boltzmann machines“, Theoretical computer science, Bd. 598, S. 102–117, 2015, doi: 10.1016/j.tcs.2015.05.019.
[1]
A. Fischer und C. Igel, „Training restricted Boltzmann machines: An introduction“, Pattern recognition, Bd. 47, Nr. 1, S. 25–39, 2014, doi: 10.1016/j.patcog.2013.05.025.
[1]
K. Brügge, A. Fischer, und C. Igel, „The flip-the-state transition operator for restricted Boltzmann machines“, Machine learning, Bd. 93, Nr. 1, S. 53–69, 2013, doi: 10.1007/s10994-013-5390-3.
[2]
J. Melchior, A. Fischer, N. Wang, und L. Wiskott, „How to center binary Restricted Boltzmann Machines“, 2013. [Online]. Verfügbar unter: http://arxiv.org/pdf/1311.1354v3.pdf
[3]
O. Krause, A. Fischer, T. Glasmachers, und C. Igel, „Approximation properties of DBNs with binary hidden units and real-valued visible units“, in 30th International Conference on Machine Learning, Atlanta, 2013, Bd. 28, S. 419–426.
[1]
A. Fischer und C. Igel, „An introduction to restricted Boltzmann machines“, in Progress in pattern recognition, image analysis, computer vision, and applications, Buenos Aires, 2012, Bd. 7441, S. 14–36. doi: 10.1007/978-3-642-33275-3_2.
[1]
A. Fischer und C. Igel, „Bounding the bias of contrastive divergence learning“, Neural computation, Bd. 23, Nr. 3, S. 664–673, 2011, doi: 10.1162/neco_a_00085.
[1]
A. Fischer und C. Igel, „Empirical analysis of the divergence of Gibbs sampling based learning algorithms for Restricted Boltzmann Machines“, in Artificial neural networks – ICANN 2010, Thessaloniki, 2010, Bd. 6352–6354, S. 208–217. doi: 10.1007/978-3-642-15825-4_26.