• Prof. Dr.

YUVAL YAROM

  • Computer Security

Ruhr University Bochum
Faculty of Computer Science
Computer Security
Universitätsstraße 150

44801 Bochum

Room:  MC 5.148

Tel:      +49 (0)234 32-19290

E-Mail: yuval.yarom@rub.de

Office hours: By Arrangement

Table of Contents

Courses

Publications

2024

[1]
M. Lange, N. Krystiniak, R. C. Engelhardt, W. Konen, und L. Wiskott, „Improving reinforcement learning efficiency with auxiliary tasks in non-visual environments: a comparison“, in Machine Learning, Optimization, and Data Science, Grasmere, UK, 2024, Bd. 14506, S. 177–191. doi: 10.1007/978-3-031-53966-4_14.
[2]
R. C. Engelhardt, R. Raycheva, M. Lange, L. Wiskott, und W. Konen, „Ökolopoly: case study on large action spaces in reinforcement learning“, in Machine Learning, Optimization, and Data Science, Grasmere, UK, 2024, Bd. 14506, S. 109–123. doi: 10.1007/978-3-031-53966-4_9.
[3]
F. Baucks, R. Schmucker, und L. Wiskott, „Gaining insights into course difficulty cariations using item response theory“, in LAK ’24, Kyoto, Japan, März 2024, S. 450–461. doi: 10.1145/3636555.3636902.
[4]
P. Rath-Manakidis, F. Strothmann, T. Glasmachers, und L. Wiskott, „ProtoP-OD: explainable object detection with prototypical parts“, 2024.
[5]
M. Bauroth, P. Rath-Manakidis, V. Langholf, L. Wiskott, und T. Glasmachers, „tachAId—an interactive tool supporting the design of human-centered AI solutions“, Frontiers in artificial intelligence, Bd. 7, Art. Nr. 1354114, März 2024, doi: 10.3389/frai.2024.1354114.
[6]
R. C. Engelhardt, M. Lange, L. Wiskott, und W. Konen, „Exploring the reliability of SHAP values in reinforcement learning“, in Explainable artificial intelligence, Valletta, Malta, Juli 2024, Bd. 2155. doi: 10.1007/978-3-031-63800-8_9.
[7]
J. Melchior, A. Altamimi, M. Bayati, S. Cheng, und L. Wiskott, „A neural network model for online one-shot storage of pattern sequences“, PLoS ONE, Bd. 19, Nr. 6, Art. Nr. e0304076, Juni 2024, doi: 10.1371/journal.pone.0304076.
[8]
F. Baucks, R. Schmucker, C. Borchers, Z. A. Pardos, und L. Wiskott, „Gaining insights into group-level course difficulty via differential course functioning“, Juli 2024, Publiziert. doi: 10.1145/3657604.3662028.
[9]
F. Baucks und L. Wiskott, „Empowering advisors: designing a dashboard for university student guidance“, in Learning Analytics und Künstliche Intelligenz in Studium und Lehre, J. Leschke und P. Salden, Hrsg. Wiesbaden: Springer Fachmedien Wiesbaden, 2024, S. 27–44. doi: 10.1007/978-3-658-42993-5_2.
1]
M. Schilling, B. Hammer, F. W. Ohl, H. J. Ritter, und L. Wiskott, „Modularity in nervous systems: a key to efficient adaptivity for deep reinforcement learning“, Cognitive Computation, Jan. 2023, Publiziert, doi: 10.1007/s12559-022-10080-w.
[2]
R. C. Engelhardt, M. Lange, L. Wiskott, und W. Konen, „Sample-based rule extraction for explainable reinforcement learning“, in Machine learning, optimization, and data science, Grasmere, UK, 2023, Bd. 13163, S. 330–345. doi: 10.1007/978-3-031-25599-1_25.
[3]
X. Zeng, N. Diekmann, L. Wiskott, und S. Cheng, „Modeling the function of episodic memory in spatial learning“, Frontiers in psychology, Bd. 14, Art. Nr. 1160648, Apr. 2023, doi: 10.3389/fpsyg.2023.1160648.
[4]
R. C. Engelhardt, M. Oedingen, M. Lange, L. Wiskott, und W. Konen, „Iterative oblique decision trees deliver explainable RL models“, Algorithms, Bd. 16, Nr. 6, Art. Nr. 282, Mai 2023, doi: 10.3390/a16060282.
[5]
R. C. Engelhardt, M. Oedingen, M. Lange, L. Wiskott, und W. Konen, „Iterative oblique decision trees deliver explainable RL models“, 4. Mai 2023.
[6]
E. Parra Barrero, S. Vijayabaskaran, E. Seabrook, L. Wiskott, und S. Cheng, „A map of spatial navigation for neuroscience“, Neuroscience & biobehavioral reviews, Bd. 152, Art. Nr. 105200, Mai 2023, doi: 10.1016/j.neubiorev.2023.105200.
[1]
X. Zeng, L. Wiskott, und S. Cheng, „The functional role of episodic memory in spatial learning“, 12. April 2022.
[2]
Z. Fayyaz u. a., „A model of semantic completion in generative episodic memory“, Neural computation, Bd. 34, Nr. 9, S. 1841–1870, 2022, doi: 10.1162/neco_a_01520.
[3]
H. D. Hlynsson, M. Schüler, R. Schiewer, T. Glasmachers, und L. Wiskott, „Latent representation prediction networks“, International journal of pattern recognition and artificial intelligence, Bd. 36, Nr. 1, Art. Nr. 2251002, 2022, doi: 10.1142/s0218001422510028.
[1]
T. Walther u. a., „Context-dependent extinction learning emerging from raw sensory inputs: a reinforcement learning approach“, Scientific reports, Bd. 11, Art. Nr. 2713, 2021, doi: 10.1038/s41598-021-81157-z.
[2]
H. Altartouri, T. Glasmachers, und L. Wiskott, „Improving the classification of protein sequence functions by reducing the heterogeneity of datasets“, Universitätsbibliothek, Ruhr-Universität Bochum, Bochum, 2021. doi: 10.13154/294-8343.
[3]
J. Tekülve, G. Schöner, und L. Wiskott, „A neural process model of intentionality implemented on an autonomous robot“, Universitätsbibliothek, Ruhr-Universität Bochum, Bochum, 2021. doi: 10.13154/294-8451.
[4]
H. D. Hlynsson, L. Wiskott, und T. Glasmachers, „Visual processing in context of reinforcement learning“, Universitätsbibliothek, Ruhr-Universität Bochum, Bochum, 2021. doi: 10.13154/294-9032.
[5]
Z. Fayyaz, A. Altamimi, S. Cheng, und L. Wiskott, „A model of semantic completion in generative episodic memory“, 26. November 2021.
[6]
M. Nafzi, T. Glasmachers, und L. Wiskott, „Methoden zur Fahrzeugwiedererkennung unter Verwendung maschineller Lernverfahren“, Universitätsbibliothek, Ruhr-Universität Bochum, Bochum, 2021. doi: 10.13154/294-9102.
[7]
X. Zeng, L. Wiskott, und S. Cheng, „The functional role of episodic memory in spatial learning“, 29. November 2021.
[1]
J. Melchior, L. Wiskott, und T. Glasmachers, „On the importance of centering in artificial neural networks“, Universitätsbibliothek, Ruhr-Universität Bochum, Bochum, 2020. doi: 10.13154/294-7713.
[2]
R. Görler, L. Wiskott, und S. Cheng, „Cover Image, Volume 30, Issue 6“, Hippocampus, Bd. 30, Nr. 6, S. C1, Juni 2020, doi: 10.1002/hipo.22789.
[3]
T. Walther u. a., „Context-dependent extinction learning emerging from raw sensory inputs: a reinforcement learning approach“, 28. April 2020.
[4]
L. Wiskott und F. Schönfeld, „Laplacian Matrix for Dimensionality Reduction and Clustering“, in Big data management and analytics, Berlin, Germany, Nov. 2020, Bd. 390, S. 93–119. doi: 10.1007/978-3-030-61627-4_5.
[1]
R. Görler, L. Wiskott, und S. Cheng, „Improving sensory representations using episodic memory“, Hippocampus, Bd. 30, Nr. 6, S. 638–656, Jan. 2019, doi: 10.1002/hipo.23186.
[2]
S. Qa’adan, T. Glasmachers, und L. Wiskott, „Budgeted stochastic coordinate ascent for large-scale kernelized dual support vector machine training“, Universitätsbibliothek, Ruhr-Universität Bochum, Bochum, 2019. doi: 10.13154/294-6846.
[3]
B. Weghenkel, L. Wiskott, C. Igel, und G. Schöner, „Unsupervised extraction of predictable features from high-dimensional time series“, Universitätsbibliothek, Ruhr-Universität Bochum, Bochum, 2019. doi: 10.13154/294-6819.
[4]
J. Melchior, M. Bayati, A. H. Azizi, S. Cheng, und L. Wiskott, „A Hippocampus Model for Online One-Shot Storage of Pattern Sequences“, 30. Mai 2019.
[5]
A. N. Escalante Bañuelos und L. Wiskott, „Improved graph-based SFA: information preservation complements the slowness principle“, Machine learning, Bd. 109, Nr. 5, S. 999–1037, Dez. 2019, doi: 10.1007/s10994-019-05860-9.
[1]
M. Bayati, T. Neher, J. Melchior, K. Diba, L. Wiskott, und S. Cheng, „Storage fidelity for sequence memory in the hippocampal circuit“, PLoS ONE, Bd. 13, Nr. 10, Art. Nr. e0204685, 2018, doi: 10.1371/journal.pone.0204685.
[2]
L. Wiskott, „Wenn Maschinen Menschen überflügeln“, Rubin, Bd. 28, Nr. 1, S. 32, 2018, [Online]. Verfügbar unter: http://news.rub.de/sites/default/files/rubin_01_2018.pdf
[3]
J. Fang, N. N. Rüther, C. Bellebaum, L. Wiskott, und S. Cheng, „The interaction between semantic representation and episodic memory“, Neural computation, Bd. 30, Nr. 2, S. 293–332, 2018, doi: 10.1162/neco_a_01044.
[4]
B. Weghenkel und L. Wiskott, „Slowness as a Proxy for Temporal Predictability: an empirical comparison“, Neural computation, Bd. 30, Nr. 5, S. 1151–1179, 2018, doi: 10.1162/neco_a_01070.
[5]
J. Freiwald u. a., „Utilizing slow feature analysis for lipreading“, in Speech communication, Oldenburg, 2018, Bd. 282, S. 191–195.
[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.
[2]
J. Melchior, N. Wang, und L. Wiskott, „Gaussian-binary restricted Boltzmann machines for modeling natural image statistics“, PLoS ONE, Bd. 12, Nr. 2, Art. Nr. e0171015, Feb. 2017, doi: 10.1371/journal.pone.0171015.
[3]
V. R. Kompella und L. Wiskott, „Intrinsically motivated acquisition of modular slow features for humanoids in continuous and non-stationary environments“, 17. Januar 2017.
[4]
M. Bayati, J. Melchior, L. Wiskott, und S. Cheng, „Generating sequences in recurrent neural networks for storing and retrieving episodic memories“, BMC neuroscience, Bd. 18, Nr. Suppl. 1. BioMed Central, London, S. 30–31, 2017. doi: 10.1186/s12868-017-0371-2.
[5]
A. N. Escalante Bañuelos, L. Wiskott, und R. Würtz, „Extensions of hierarchical slow feature analysis for efficient classification and regression on high-dimensional data“, Universitätsbibliothek, Ruhr-Universität Bochum, Bochum, 2017.
[6]
S. Zibner, G. Schöner, und L. Wiskott, „A neuro-dynamic architecture for autonomous visual scene representation“, Verlag Dr. Hut, München, 2017. [Online]. Verfügbar unter: https://www.ini.rub.de/upload/file/1530687954_61562995ba319272a158/Zibner2015Thesis.pdf
[7]
F. Draht, S. Zhang, A. Rayan, F. Schönfeld, L. Wiskott, und D. Manahan-Vaughan, „Experience-dependency of reliance on local visual and idiothetic cues for spatial representations created in the absence of distal information“, Frontiers in behavioral neuroscience, Bd. 11, Art. Nr. 92, Juni 2017, doi: 10.3389/fnbeh.2017.00092.
[1]
S. Richthofer und L. Wiskott, „Predictable feature analysis“, in 2015 IEEE 14th International Conference on Machine Learning and Applications, März 2016, S. 190–196. doi: 10.1109/icmla.2015.158.
[2]
A. N. Escalante Bañuelos und L. Wiskott, „Improved graph-based SFA: information preservation complements the slowness principle“, 2016. [Online]. Verfügbar unter: http://arxiv.org/pdf/1601.03945v1.pdf
[3]
A. N. Escalante Bañuelos und L. Wiskott, „Theoretical analysis of the optimal free responses of graph-based SFA for the design of training graphs“, Journal of machine learning research, Bd. 17, Art. Nr. 157, 2016, [Online]. Verfügbar unter: http://jmlr.csail.mit.edu/papers/volume17/15-311/15-311.pdf
[4]
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. H. Azizi, S. Cheng, und L. Wiskott, „The generation of sequential activitiy and spatial responses in the hippocampus: computational studies of network mechanisms and their robustness“, Universitätsbibliothek, Ruhr-Universität Bochum, Bochum, 2015.
[2]
F. Schönfeld und L. Wiskott, „Modeling place field activity with hierarchical slow feature analysis“, Frontiers in computational neuroscience, Bd. 9, Art. Nr. 51, 2015, doi: 10.3389/fncom.2015.00051.
[3]
S. Richthofer und L. Wiskott, „Predictable feature analysis“, in Workshop New Challenges in Neural Computation 2015, Aachen, 2015, Bd. 2015, 3, S. 68–75.
[4]
T. Neher, L. Wiskott, und D. Manahan-Vaughan, „Analysis of the formation of memory and place cells in the hippocampus: a computational approach“, Universitätsbibliothek, Ruhr-Universität Bochum, Bochum, 2015. [Online]. Verfügbar unter: http://hss-opus.ub.ruhr-uni-bochum.de/opus4/files/4739/diss.pdf
[5]
T. Neher, S. Cheng, und L. Wiskott, „Memory storage fidelity in the hippocampal circuit: the role of subregions and input statistics“, PLoS computational biology, Bd. 11, Nr. 5, Art. Nr. e1004250, 2015, doi: 10.1371/journal.pcbi.1004250.
[6]
L. Wiskott, „Slow feature analysis“, in Encyclopedia of Computational Neuroscience, D. Jaeger und R. Jung, Hrsg. New York, NY: Springer Science + Business, 2015, S. 2715–2717. doi: 10.1007/978-1-4614-6675-8_682.
[1]
N. Wang, L. Wiskott, und S. Cheng, „Learning natural image statistics with variants of restricted Boltzmann machines“, Universitätsbibliothek, Ruhr-Universität Bochum, Bochum, 2014. [Online]. Verfügbar unter: http://hss-opus.ub.ruhr-uni-bochum.de/opus4/files/4619/diss.pdf
[2]
M. Leßmann und L. Wiskott, „Learning of invariant object recognition in hierarchical neural networks using temporal continuity“, Universitätsbibliothek, Ruhr-Universität Bochum, Bochum, 2014. [Online]. Verfügbar unter: http://www-brs.ub.ruhr-uni-bochum.de/netahtml/HSS/Diss/LessmannMarkus/diss.pdf
[3]
B. Weghenkel und L. Wiskott, „Learning predictive partitions for continuous feature spaces“, 2014. [Online]. Verfügbar unter: https://www.ini.rub.de/PEOPLE/wiskott/Reprints/WeghenkelWiskott-2014-ESANN-Preprint.pdf
[4]
S. Dähne, N. Wilbert, und L. Wiskott, „Slow feature analysis on retinal waves leads to V1 complex cells“, PLoS computational biology, Bd. 10, Nr. 5, Art. Nr. e1003564, 2014, doi: 10.1371/journal.pcbi.1003564.
[5]
N. Wang, D. Jancke, und L. Wiskott, „Modeling correlations in spontaneous activity of visual cortex with centered Gaussian: binary deep Boltzmann machines“, in Workshop Proceedings International Conference of Learning Representations (ICLR’14, workshop), 2014. [Online]. Verfügbar unter: http://arxiv.org/pdf/1312.6108v3.pdf
[6]
N. Wang, D. Jancke, und L. Wiskott, „Modeling correlations in spontaneous activity of visual cortex with Gaussian-binary deep Boltzmann machines“, Program and abstracts Bernstein Conference for Computational Neuroscience. BFNT Göttingen, Göttingen, S. 263–264, 2014.
[7]
B. Weghenkel und L. Wiskott, „Learning predictive partitions for continuous feature spaces“, in Proceedings, 2014, S. 577–582. [Online]. Verfügbar unter: https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2014-98.pdf
[8]
H. Sprekeler, T. Zito, und L. Wiskott, „An extension of slow feature analysis for nonlinear blind source separation“, Journal of machine learning research, Bd. 15, S. 921–947, 2014, [Online]. Verfügbar unter: http://jmlr.org/papers/volume15/sprekeler14a/sprekeler14a.pdf
[9]
L. Wiskott, R. Würtz, und G. Westphal, „Elastic bunch graph matching“, Scholarpedia journal, Bd. 9, Nr. 3, S. 10587, 2014, doi: 10.4249/scholarpedia.10587.
[10]
N. Wang, J. Melchior, und L. Wiskott, „Gaussian-binary restricted Boltzmann machines on modeling natural image statistics“, 2014. [Online]. Verfügbar unter: http://arxiv.org/pdf/1401.5900v1.pdf
[11]
S. Zhang, F. Schönfeld, L. Wiskott, und D. Manahan-Vaughan, „Spatial representations of place cells in darkness are supported by path integration and border information“, Frontiers in behavioral neuroscience, Bd. 8, Art. Nr. 222, Juni 2014, doi: 10.3389/fnbeh.2014.00222.
[12]
B. Weghenkel und L. Wiskott, „Learning predictive partitions for continuous feature spaces“, in Proceedings, 2014, S. 577–582. [Online]. Verfügbar unter: http://www.scopus.com/inward/record.url?eid=2-s2.0-84961997861&partnerID=MN8TOARS
[13]
H. Sprekeler, T. Zito, und L. Wiskott, „An extension of slow feature analysis for nonlinear blind source separation“, Journal of machine learning research, Bd. 15, S. 921–947, 2014, [Online]. Verfügbar unter: http://www.scopus.com/inward/record.url?eid=2-s2.0-84899790321&partnerID=MN8TOARS
[1]
N. Kruger u. a., „Deep hierarchies in the primate visual cortex: what can we learn for computer vision?“, IEEE transactions on pattern analysis and machine intelligence / Institute of Electrical and Electronics Engineers, Bd. 35, Nr. 8, S. 1847–1871, 2013, doi: 10.1109/tpami.2012.272.
[2]
H. Q. Minh und L. Wiskott, „Multivariate slow feature analysis and decorrelation filtering for blind source separation“, IEEE transactions on image processing / Institute of Electrical and Electronics Engineers, Bd. 22, Nr. 7, S. 2737–2750, 2013, doi: 10.1109/tip.2013.2257808.
[3]
S. Richthofer und L. Wiskott, „Predictable feature analysis“, 2013. [Online]. Verfügbar unter: http://arxiv.org/pdf/1311.2503v1.pdf
[4]
F. Schönfeld und L. Wiskott, „RatLab: an easy to use tool for place code simulations“, Frontiers in computational neuroscience, Bd. 7, Art. Nr. 104, 2013, doi: 10.3389/fncom.2013.00104.
[5]
A. N. Escalante Bañuelos und L. Wiskott, „How to solve classification and regression problems on high-dimensional data with a supervised extension of slow feature analysis“, 2013. [Online]. Verfügbar unter: http://cogprints.org/8966/1/EscalanteWiskott-Cogprints-2013.pdf
[6]
A. N. Escalante Bañuelos und L. Wiskott, „How to solve classification and regression problems on high-dimensional data with a supervised extension of slow feature analysis“, Journal of machine learning research, Bd. 14, S. 3683–3719, 2013, [Online]. Verfügbar unter: http://jmlr.org/papers/volume14/escalante13a/escalante13a.pdf
[7]
F. Schönfeld und L. Wiskott, „Theoretical neuroscience: finding your way into the light“, in IGSN report 2013, D. Manahan-Vaughan und U. Heiler, Hrsg. Bochum: IGSN, 2013, S. 47–49.
[8]
N. Wang, D. Jancke, und L. Wiskott, „Modeling correlations in spontaneous activity of visual cortex with centered Gaussian-binary deep Boltzmann machines“, 2013. [Online]. Verfügbar unter: http://arxiv.org/pdf/1312.6108v3.pdf
[9]
A. H. Azizi, L. Wiskott, und S. Cheng, „A computational model for preplay in the hippocampus“, Frontiers in computational neuroscience, Bd. 7, Art. Nr. 161, 2013, doi: 10.3389/fncom.2013.00161.
[10]
T. Neher, S. Cheng, und L. Wiskott, „Are memories really stored in the hippocampal CA3 region?“, 10th Göttingen Meeting of the German Neuroscience Society. S. 34, 2013.
[11]
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
[12]
A. N. Escalante Bañuelos und L. Wiskott, „How to solve classification and regression problems on high-dimensional data with a supervised extension of slow feature analysis“, Journal of machine learning research, Bd. 14, S. 3686–3719, 2013, [Online]. Verfügbar unter: http://www.scopus.com/inward/record.url?eid=2-s2.0-84893452106&partnerID=MN8TOARS
[1]
A. N. Escalante Bañuelos und L. Wiskott, „How to solve classification and regression problems on real data with slow feature analysis“, gehalten auf der Machine Learning Summer School, Kyoto, 3. September 2012, Publiziert.
[2]
S. Richthofer, B. Weghenkel, und L. Wiskott, „Predictable feature analysis“, Bernstein Conference 2012. 2012. doi: 10.3389/conf.fncom.2012.55.00120.
[3]
A. N. Escalante Bañuelos und L. Wiskott, „Slow feature analysis: perspectives for technical applications of a versatile learning algorithm“, Künstliche Intelligenz, Bd. 26, Nr. 4, S. 341–348, 2012, doi: 10.1007/s13218-012-0190-7.
[4]
N. Wang, J. Melchior, und L. Wiskott, „An analysis of Gaussian-binary restricted Boltzmann machines for natural image“, in ESANN 2012, 2012, S. 287–292. [Online]. Verfügbar unter: https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2012-95.pdf
[5]
F. Schönfeld und L. Wiskott, „Sensory integration of place and head-direction cells in a virtual environment“, 8th FENS Forum of Neuroscience. FENS, s. l., 2012.
[6]
T. Neher und L. Wiskott, „A computational model of memory formation in the hippocampus“, 8th FENS Forum of Neuroscience. FENS, s. l., 2012.
[7]
F. Schönfeld und L. Wiskott, „Spatial representation in the hippocampus“, 2012.
[1]
L. Wiskott, „Die Entdeckung der Langsamkeit: ein Selbstorganisationsprinzip im Gehirn“, 21. September 2011, Publiziert.
[2]
P. A. Appleby, G. Kempermann, und L. Wiskott, „The role of additive neurogenesis and synaptic plasticity in a hippocampal memory model with grid-cell like input“, PLoS computational biology, Bd. 7, Nr. 1, S. e1001063-1-e1001063-15, 2011, doi: 10.1371/journal.pcbi.1001063.
[3]
A. N. Escalante Bañuelos und L. Wiskott, „Heuristic evaluation of expansions for non-linear hierarchical slow feature analysis“, in 10th International Conference on Machine Learning and Applications and workshops (ICMLA), 2011, 2011, S. 133–138. doi: 10.1109/icmla.2011.72.
[4]
M. Ha Quang und L. Wiskott, „Slow feature analysis and decorrelation filtering for separating correlated sources“, in IEEE International Conference on Computer Vision (ICCV), 2011, 2011, S. 866–873. doi: 10.1109/iccv.2011.6126327.
[5]
L. Wiskott, P. Berkes, M. Franzius, H. Sprekeler, und N. Wilbert, „Slow feature analysis“, Scholarpedia journal, Bd. 6, Nr. 4. S. 5282, 2011. doi: 10.4249/scholarpedia.5282.
[6]
N. Wilbert, T. Zito, R.-B. Schuppner, Z. Jędrzejewski-Szmek, L. Wiskott, und P. Berkes, „Building extensible frameworks for data processing: the case of MDP, Modular toolkit for data processing“, Journal of computational science, Bd. 4, Nr. 5, S. 345–351, 2011, doi: 10.1016/j.jocs.2011.10.005.
[7]
M. Franzius, N. Wilbert, und L. Wiskott, „Invariant object recognition and pose estimation with slow feature analysis“, Neural computation, Bd. 23, Nr. 9, S. 2289–2323, 2011, doi: 10.1162/neco_a_00171.
[8]
H. Sprekeler und L. Wiskott, „A theory of slow feature analysis for transformation-based input signals with an application to complex cells“, Neural computation, Bd. 23, Nr. 2, S. 303–335, 2011, doi: 10.1162/neco_a_00072.
[1]
Y. Sandamirskaya, G. Schöner, und L. Wiskott, „Sequence generation in dynamic field theory“, Universitätsbibliothek, Ruhr-Universität Bochum, Bochum, 2010. [Online]. Verfügbar unter: http://www-brs.ub.ruhr-uni-bochum.de/netahtml/HSS/Diss/SandamirskayaYulia/diss.pdf
[2]
P. Appleby, G. Kempermann, und L. Wiskott, „The role of neurogenesis in the hippocampus: [Vortrag gehalten auf der Konferenz ‚Adult Neurogenesis: Structure and Function‘, 27. – 29. Mai 2010, Frauenchiemsee, Deutschland]“, gehalten auf der Adult Neurogenesis: Structure and Function, Frauenchiemsee, 27. Mai 2010, Publiziert.
[3]
S. Dähne, N. Wilbert, und L. Wiskott, „Self-organization of V1 complex cells based on slow feature analysis and retinal waves: [Vortrag gehalten auf der Bernstein Conference on Computational Neuroscience, 2010, Berlin]“, Frontiers in computational neuroscience. Frontiers Research Foundation, Lausanne, 2010. doi: 10.3389/conf.fncom.2010.51.00090.
[4]
R. Legenstein, N. Wilbert, und L. Wiskott, „Reinforcement learning on slow features of high-dimensional input streams“, PLoS computational biology, Bd. 6, Nr. 8, S. e1000894-1-e1000894-13, 2010, doi: 10.1371/journal.pcbi.1000894.
[5]
A. N. Escalante Bañuelos und L. Wiskott, „Gender and age estimation from synthetic face images with hierarchical slow feature analysis“, in Computational intelligence for knowledge-based systems design, 2010, Bd. 6178, S. 240–249. doi: 10.1007/978-3-642-14049-5_25.
[6]
N. Wilbert und L. Wiskott, „Hierarchical slow feature analysis and top-down processes: [Poster am 27.09.2010 auf der Bernstein Conference on Computational Neuroscience, 2010, Berlin]“, Frontiers in computational neuroscience. Frontiers Research Foundation, Lausanne, 2010. doi: 10.3389/conf.fncom.2010.51.00119.
[7]
H. Sprekeler, T. Zito, und L. Wiskott, „An extension of slow feature analysis for nonlinear blind source separation“, 2010. [Online]. Verfügbar unter: http://cogprints.org/7056/1/SprekelerZitoWiskott-Cogprints-2010.pdf
[8]
L. Wiskott, M. Ha Quang, H. Sprekeler, und T. Zito, „Slow feature analysis: analyzing signals with the slowness principle“, Second joint statistical meeting [der] Deutschen Arbeitsgemeinschaft Statistik (DAGStat), 56. Biometrisches Kolloquium, Pfingsttagung der Deutschen Statistischen Gesellschaft. Universität, Dortmund, S. 398, 2010.
[1]
P. A. Appleby und L. Wiskott, „Additive neurogenesis as a strategy for avoiding interference in a sparsely-coding dentate gyrus“, Network, Bd. 20, Nr. 3, S. 137–161, 2009, doi: 10.1080/09548980902993156.
[2]
S. Lezius, I. Kirste, C. Bandt, G. Kempermann, und L. Wiskott, „Quantitative modeling of the dynamics of adult hippocampal neurogenesis in mice: P335“, BMC neuroscience, Bd. 10, Nr. S1. BioMed Central, London, S. 1–2, 2009. doi: 10.1186/1471-2202-10-s1-p335.
[3]
C. Hinze, N. Wilbert, und L. Wiskott, „Visualization of higher-level receptive fields in a hierarchical model of the visual system: P158“, BMC neuroscience, Bd. 10, Nr. S1. BioMed Central, London, S. 1–2, 2009. doi: 10.1186/1471-2202-10-s1-p158.
[4]
N. Wilbert, R. Legenstein, M. Franzius, und L. Wiskott, „Reinforcement learning on complex visual stimuli: P90“, BMC neuroscience, Bd. 10, Nr. S1. BioMed Central, London, S. 1–2, 2009. doi: 10.1186/1471-2202-10-s1-p90.
[5]
S. Dähne, N. Wilbert, und L. Wiskott, „Learning complex cell units from simulated prenatal retinal waves using slow feature analysis: [Poster presentation from Eighteenth Annual Computational Neuroscience Meeting: CNS*2009, Berlin, Germany. 18–23 July 2009]“, BMC neuroscience, Bd. 10, Nr. Suppl. 1. BioMed Central, London, 2009. doi: 10.1186/1471-2202-10-s1-p129.
[6]
S. Dähne, N. Wilbert, und L. Wiskott, „Learning complex cell units from simulated prenatal retinal waves using slow feature analysis“, BMC neuroscience, Bd. 10, Nr. Suppl. 1, 2009, doi: 10.1186/1471-2202-10-s1-p129.
[7]
H. Sprekeler und L. Wiskott, „Slowness learning: mathematical approaches and synaptic mechanisms“, Humboldt-Univ., Berlin, 2009. [Online]. Verfügbar unter: http://edoc.hu-berlin.de/dissertationen/sprekeler-henning-2008-11-14/PDF/sprekeler.pdf
[8]
L. Wiskott, M. Franzius, H. Sprekeler, und P. A. Appleby, „Self-organization of place cells with slowness, sparseness and neurogenesis“, 41st European Brain and Behaviour Society Meeting. 2009. doi: 10.3389/conf.neuro.08.2009.09.062.
[1]
T. Zito, N. Wilbert, L. Wiskott, und P. Berkes, „Modular toolkit for data processing (MDP): a Python data processing framework“, Frontiers in neuroinformatics, Bd. 2, Nr. 8, 2008, doi: 10.3389/neuro.11.008.2008.
[2]
J. B. Aimone und L. Wiskott, „Computational modeling of neurogenesis“, in Adult neurogenesis, Bd. 52, F. H. Gage, Hrsg. Cold Spring Harbor, NY: Cold Spring Harbor Laboratory, 2008.
[3]
M. Franzius, N. Wilbert, und L. Wiskott, „Invariant object recognition with slow feature analysis“, in Artificial neural networks, 2008, Bd. 5163, S. 961–970.
[4]
M. Franzius, N. Wilbert, und L. Wiskott, „Unsupervised learning of invariant 3d-object and pose representations with slow feature analysis“, in Proceedings of the federation of European neuroscience societies (FENS) Forum 2008, Geneva, July 12-16, 2008, Publiziert.
[1]
T. Blaschke und L. Wiskott, „Independent slow feature analysis and nonlinear blind source separation“, Neural computation, Bd. 19, Nr. 4, S. 994–1021, 2007, doi: 10.1162/neco.2007.19.4.994.
[2]
H. Sprekeler, C. Michaelis, und L. Wiskott, „Slowness: an objective for spike-timing-dependent plasticity?“, PLoS computational biology, Bd. 3, Nr. 6, S. 1136–1148, 2007, doi: 10.1371/journal.pcbi.0030112.
[3]
M. Franzius, H. Sprekeler, und L. Wiskott, „Slowness and sparseness lead to place-, head direction- and spatial-view cells“, PLoS computational biology, Bd. 3, Nr. 8, S. 1605–1622, 2007, doi: 10.1371/journal.pcbi.0030166.
[4]
P. Berkes und L. Wiskott, „Analysis and interpretation of quadratic models of receptive fields“, Nature protocols, Bd. 2, Nr. 2, S. 400–407, 2007, doi: 10.1038/nprot.2007.27.
[5]
M. Franzius, H. Sprekeler, und L. Wiskott, „Unsupervised learning of place cells, head-direction cells, and spatial-view cells with slow feature analysis on quasi-natural videos“, 2007. [Online]. Verfügbar unter: http://cogprints.org/5492/1/placeFieldsManuscript_submitCogPrints.pdf
[6]
P. A. Appleby, I. Kirste, S. Lezius, C. Bandt, G. Kempermann, und L. Wiskott, „Adult neurogenesis in the dentate gyrus: data analysis and modeling“, in Proceedings of the midterm evaluation of the German national network for computational neuroscience, 2007, S. 26.
[7]
L. Wiskott, H. Sprekeler, und P. Berkes, „Towards an analytical derivation of complex cell receptive field properties“, in Proceedings o f the 7th Göttingen meeting of the German neuroscience society, Göttingen, March 29 – April 1, 2007, S. S12-2-1-S12-2–1.
[8]
L. Wiskott, P. A. Appleby, und G. Kempermann, „Adult hippocampal neurogenesis – a strategy for avoiding catastrophic interference?“, in Proceedings of the 3rd Annual Computational Cognitive Neuroscience Conference, 2007, Publiziert.
[9]
M. Franzius, H. Sprekeler, und L. Wiskott, „Unsupervised learning of place cells and head direction cells with slow feature analysis“, in Proceedings o f the 7th Göttingen meeting of the German neuroscience society, Göttingen, March 29 – April 1, 2007, S. TS19-1C.
[10]
M. Franzius, N. Wilbert, und L. Wiskott, „Unsupervised learning of invariant 3D-object representations with slow feature analysis“, in Proceedings of the 3rd Bernstein Symposium for Computational Neuroscience, 2007, S. 105.
[11]
H. Sprekeler, C. Michaelis, und L. Wiskott, „Slowness: an objective for spike timing-dependent plasticity?“, in Proceedings o f the 7th Göttingen meeting of the German neuroscience society, Göttingen, March 29 – April 1, 2007, S. T27-3A.
[12]
L. Wiskott, P. A. Appleby, und G. Kempermann, „What is the functional role of adult neurogenesis in the hippocampus?: A computational approach“, in Proceedings of the adult neurogenesis symposium, Dresden, October 15, 2007, Publiziert.
[13]
P. A. Appleby, S. Lezius, C. Bandt, G. Kempermann, und L. Wiskott, „Neurogenesis avoids catastrophic interference in a sparsely coding dentate gyrus“, in Proceedings of the 3rd Bernstein Symposium for Computational Neuroscience, 2007, S. 41.
[14]
N. Wilbert, M. Franzius, R. Cichy, S. Schmidt, S. A. Brandt, und L. Wiskott, „Towards a model of visual attention“, in Proceedings of the midterm evaluation of the German national network for computational neuroscience, 2007, S. 30.
[15]
L. Wiskott, M. J. Rasch, und G. Kempermann, „What is the functional role of adult neurogenesis in the hippocampus?“, in Kognitionsforschung 2007, 2007, Bd. 8, S. 53.
[16]
H. Sprekeler und L. Wiskott, „Spike-timing-dependent plasticity and temporal input statistics: P86“, BMC neuroscience, Bd. 8, Nr. S2. BioMed Central, London, S. 1, 2007. doi: 10.1186/1471-2202-8-s2-p86.
[17]
H. Sprekeler und L. Wiskott, „Analytical derivation of complex cell properties from the slowness principle“, in Computational neuroscience, 2007, Bd. 70,10/12.
[18]
M. Franzius, H. Sprekeler, und L. Wiskott, „Unsupervised learning of visually driven place cells in the hippocampus“, in Kognitionsforschung 2007, 2007, Bd. 8, S. 60.
[19]
H. Sprekeler und L. Wiskott, „Spike-timing-dependent plasticity and temporal input statistics“, in Computational neuroscience, 2007, Bd. 70,10/12.
[20]
M. Franzius, H. Sprekeler, und L. Wiskott, „Slowness leads to place cells“, in Computational neuroscience, 2007, Bd. 70,10/12.
[21]
M. Franzius, H. Sprekeler, und L. Wiskott, „Slowness and sparseness lead to place-, head direction-, and spatial-view cells“, in Proceedings of the 3rd Annual Computational Cognitive Neuroscience Conference, 2007, Publiziert.
[22]
L. Wiskott, M. Franzius, P. Berkes, und H. Sprekeler, „Is slowness a learning principle of the visual system?“, in Proceedings of the 39th annual european brain and behaviour society, Triest, Italy, September 15-19, 2007, S. 14–15.
[1]
P. Berkes und L. Wiskott, „On the analysis and interpretation of inhomogeneous quadratic forms as receptive fields“, Neural computation, Bd. 18, Nr. 8, S. 1868–1895, 2006, doi: 10.1162/neco.2006.18.8.1868.
[2]
T. Blaschke, P. Berkes, und L. Wiskott, „What is the relationship between slow feature analysis and independent component analysis?“, Neural computation, Bd. 18, Nr. 10, S. 2495–2508, 2006, doi: 10.1162/neco.2006.18.10.2495.
[3]
M. Franzius, R. Vollgraf, und L. Wiskott, „From grids to places“, Journal of computational neuroscience, Bd. 22, Nr. 3, S. 297–299, 2006, doi: 10.1007/s10827-006-0013-7.
[4]
L. Wiskott, M. J. Rasch, und G. Kempermann, „A functional hypothesis for adult hippocampal neurogenesis: Avoidance of catastrophic interference in the dentate gyrus“, Hippocampus, Bd. 16, Nr. 3, S. 329–343, 2006.
[5]
H. Sprekeler und L. Wiskott, „Analytical derivation of complex cell properties from the slowness principle“, in Proceedings of the 2nd Bernstein Symposium for Computational Neuroscience, 2006, S. 67.
[6]
T. Zito und L. Wiskott, „Diagonalization of time-delayed covariance matrices does not guarantee statistical independence in high-dimensional feature space“, in Proceedings of the ICA research network international workshop, 2006, S. 120–122.
[7]
H. Sprekeler, C. Michaelis, und L. Wiskott, „Slowness: an objective for spike-timing dependent plasticity?“, in Proceedings of the 2nd Bernstein Symposium for Computational Neuroscience, 2006, S. 24.
[8]
H. Sprekeler und L. Wiskott, „Analytical derivation of complex cell properties from the slowness principle“, in Proceedings of the Berlin neuroscience forum 2006, Bad Liebenwalde, June 8-10, 2006, S. 65–66.
[9]
L. Wiskott, „How does our visual system achieve shift and size invariance?: 16“, in 23 Problems in systems neuroscience, J. L. van Hemmen und T. J. Sejnowski, Hrsg. Oxford [u.a.]: Oxford Univ. Pr., 2006, S. 322–340.
[10]
M. Franzius, H. Sprekeler, und L. Wiskott, „Slowness leads to place cells“, in Proceedings of the 2nd Bernstein Symposium for Computational Neuroscience, 2006, S. 45.
[11]
H. Sprekeler und L. Wiskott, „Analytical derivation of complex cell properties from the slowness principle“, in Proceedings, 2006, S. 62.
[12]
M. Franzius, H. Sprekeler, und L. Wiskott, „Slowness leads to place cells“, in Proceedings of the Berlin neuroscience forum 2006, Bad Liebenwalde, June 8-10, 2006, S. 42.
[13]
L. Wiskott, „Is slowness a learning principle of visual cortex?“, in Proceedings of the Japan-Germany Symposium on Computational Neuroscience, 2006, S. 25.
[1]
P. Berkes und L. Wiskott, „Slow feature analysis yields a rich repertoire of complex cell properties“, Journal of vision, Bd. 5, Nr. 6, S. 579–602, 2005, doi: 10.1167/5.6.9.
[2]
T. Blaschke, L. Wiskott, K. Obermayer, und L. Schimansky-Geier, „Independent component analysis and slow feature analysis: relations and combination“, Humboldt-Univ., Berlin, 2005. [Online]. Verfügbar unter: http://edoc.hu-berlin.de/docviews/abstract.php?lang=ger&id=25458
[3]
P. Berkes und L. Wiskott, „Temporal slowness as an unsupervised learning principle: self-organization of complex-cell receptive  fields and application to pattern recognition“, Humboldt-Univ., Berlin, 2005. [Online]. Verfügbar unter: http://edoc.hu-berlin.de/dissertationen/berkes-pietro-2005-06-23/PDF/berkes.pdf
[4]
T. Blaschke und L. Wiskott, „Nonlinear blind source separation by integrating independent component analysis and slow feature analysis“, in Advances in neural information processing systems, 2005, S. 177–184.
[5]
P. Berkes und L. Wiskott, „On the analysis and interpretation of inhomogeneous quadratic forms as receptive fields“, 2005. [Online]. Verfügbar unter: http://cogprints.org/4081/1/quadratic.pdf
[6]
P. Berkes und L. Wiskott, „Analysis of inhomogeneous quadratic forms for physiological and theoretical studies“, in Proceedings, 2005, Publiziert.
[7]
C. Bandt, E. Beißwanger, L. Wiskott, und G. Kempermann, „A dynamical model for neural cell development“, in Book of abstracts, 2005, Bd. 29 E, S. 233.
[8]
L. Wiskott, M. J. Rasch, und G. Kempermann, „What is the functional role of adult neurogenesis in the hippocampus?“, in Proceedings, 2005, Publiziert.
[1]
G. Kempermann, L. Wiskott, und F. H. Gage, „Functional significance of adult neurogenesis“, Current opinion in neurobiology, Bd. 14, Nr. 2, S. 186–191, 2004, doi: 10.1016/j.conb.2004.03.001.
[2]
T. Blaschke und L. Wiskott, „Independent component analysis by simultaneous third- and fourth-order cumulant diagonalization“, IEEE transactions on signal processing / Institute of Electrical and Electronics Engineers, Bd. 52, Nr. 5, S. 1250–1256, 2004.
[3]
L. Wiskott, M. J. Rasch, und G. Kempermann, „What is the functional role of adult neurogenesis in the hippocampus?“, 2004. [Online]. Verfügbar unter: http://cogprints.org/4012/1/wiskottetal2004.pdf
[4]
P. Berkes und L. Wiskott, „Slow feature analysis yields a rich repertoire of complex-cells properties“, in Proceedings of the early cognitive vision workshop, Isle Of Skye Scotland, May 28-June 1, 2004, Publiziert.
[5]
G. Kempermann und L. Wiskott, „What is the functional role of new neurons in the adult dentate gyrus?“, in Stem cells in the nervous system, 2004, S. 57–65.
[6]
T. Blaschke und L. Wiskott, „Independent slow feature analysis and nonlinear blind source separation“, in Independent component analysis and blind signal separation, 2004, Bd. 3195, S. 742–749. doi: 10.1007/978-3-540-30110-3_94.
[1]
L. Wiskott und P. Berkes, „Is slowness a learning principle of the visual cortex?“, Zoology, Bd. 106, Nr. 4, S. 373–382, 2003, doi: 10.1078/0944-2006-00132.
[2]
T. Blaschke und L. Wiskott, „CuBICA: Independent component analysis by simultaneous third- and fourth-order cumulant diagonalization“, IEEE transactions on signal processing / Institute of Electrical and Electronics Engineers, Bd. 52, Nr. 5, S. 1250–1256, 2003, doi: 10.1109/tsp.2004.826173.
[3]
L. Wiskott, „Estimating driving forces of nonstationary time series with slow feature analysis“, 2003. [Online]. Verfügbar unter: http://arxiv.org/PS_cache/cond-mat/pdf/0312/0312317v1.pdf
[4]
P. Berkes und L. Wiskott, „Slow feature analysis yields a rich repertoire of complex-cells properties“, in The neurosciences from basic research to therapy, 2003, S. 602–603.
[5]
L. Wiskott und P. Berkes, „Is slowness a learning principle of the visual cortex?“, in Jahrestagung der Deutschen Zoologischen Gesellschaft 2003, 2003, Publiziert.
[6]
L. Wiskott, „How does our visual system achieve shift and size invariance?“, 2003. [Online]. Verfügbar unter: http://cogprints.org/3321/1/Wiskott2003.pdf
[7]
L. Wiskott, „Slow feature analysis: a theoretical analysis of optimal free responses“, Neural computation, Bd. 15, Nr. 9, S. 2147–2177, 2003.
[1]
P. Berkes und L. Wiskott, „Applying slow feature analysis to image sequences yields a rich repertoire of complex cell properties“, in Artificial neural networks, 2002, Bd. 2415, S. 81–86.
[2]
L. Wiskott, C. von der Malsburg, und A. Weitzenfeld, „Face recognition by dynamic link matching: 18“, in The neural simulation language, A. Weitzenfeld, M. A. Arbib, und A. Alexander, Hrsg. Cambridge, MA: MIT Pr., 2002, S. 343–372.
[3]
L. Wiskott und P. Berkes, „Is slowness a principle for the emergence of complex cells in primary visual cortex?“, in Proceedings of the Berlin Neuroscience Forum 2002, Liebenwalde April 18-20, 2002, S. 43.
[4]
T. Blaschke und L. Wiskott, „An improved cumulant based method for independent component analysis“, in Artificial neural networks, 2002, Bd. 2415, S. 1087–1093.
[5]
L. Wiskott und T. J. Sejnowski, „Slow feature analysis: unsupervised learning of invariances“, Neural computation, Bd. 14, Nr. 4, S. 715–770, 2002, doi: 10.1162/08997660231731893.
 
[1]
L. Wiskott und C. von der Malsburg, „Labeled bunch graphs for image analysis“, 2969101 [Online]. Verfügbar unter: https://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20030513&DB=&locale=en_EP&CC=US&NR=6563950B1&KC=B1&ND=4
[2]
L. Wiskott und T. J. Sejnowski, „Constrained optimization for neural map formation: a unifying framework for weight growth and normalization“, in Self-organizing map formation, K. Obermayer und T. J. Sejnowski, Hrsg. Cambridge: MIT Pr., 2001, S. 83–128.
[3]
L. Wiskott, „Unsupervised learning of invariances in a simple model of the visual system“, in Proceedings of the mathematical, computational and biological study of vision, 2001, S. 21–22.
[4]
L. Wiskott, „Some ideas about organic computing“, in Some ideas about organic computing, 2001, S. 39–42.
[1]
L. Wiskott, „Unsupervised learning of invariances in a simple model of the visual system“, in Computational neuroscience, 2000, Bd. 38/40, S. 157.
[1]
L. Wiskott, „Segmentation from motion: combining gabor- and mallat-wavelets to overcome the aperture and correspondence problems“, Pattern recognition, Bd. 32, Nr. 10, S. 1751–1766, 1999, doi: 10.1016/s0031-3203(98)00179-4.
[2]
L. Wiskott, „The role of topographical constraints in face recognition“, Pattern recognition letters, Bd. 20, Nr. 1, S. 89–96, 1999, doi: 10.1016/s0167-8655(98)00122-6.
[3]
L. Wiskott, J.-M. Fellous, N. Krüger, und C. von der Malsburg, „Face recognition by elastic bunch graph matching: 11“, in Intelligent biometric techniques in fingerprint and face recognition, L. C. Jain, U. Halici, I. Hayashi, und S. B. Lee, Hrsg. Boca Raton: CRC, 1999, S. 355–396.
[4]
L. Wiskott, „Learning invariance manifolds“, in Computational neuroscience, Pittsburgh, PA, 1999, S. 925–932.
[5]
L. Wiskott, „Unsupervised learning and generalization of translation invariance in a simple model of the visual system“, in Learning and adaptivity for connectionist models and neural networks, 1999, Bd. 59, S. 56–67.
[6]
L. Wiskott und C. von der Malsburg, „Objekterkennung in einem selbstorganisierenden neuronalen System“, in Komplexe Systeme und nichtlineare Dynamik in Natur und Gesellschaft, K. Mainzer, Hrsg. Berlin [u.a.]: Springer, 1999, S. 169–188.
[1]
L. Wiskott und T. J. Sejnowski, „Constrained optimization for neural map formation: a unifying framework for weight growth and normalization“, Neural computation, Bd. 10, Nr. 3, S. 671–716, 1998, doi: 10.1162/089976698300017700.
[2]
M. Wicklein, N. J. Strausfeld, T. J. Sejnowski, P. Sabes, und L. Wiskott, „Looming sensitivity in hummingbird hawkmoths: Neurons and models“, in Proceedings of the 28th annual meeting, 1998, S. 188.
[3]
L. Wiskott, „Learning invariance manifolds“, in Proceedings of the 5th Joint Symposium on Neural Computation May 16, San Diego, CA, 1998, S. 196–203.
[4]
L. Wiskott, „Learning invariance manifolds“, in ICANN ’98, 1998, S. 555–560.
[1]
L. Wiskott, J.-M. Fellous, N. Krüger, und C. von der Malsburg, „Face recognition by elastic bunch graph matching“, IEEE transactions on pattern analysis and machine intelligence / Institute of Electrical and Electronics Engineers, Bd. 19, Nr. 7, S. 775–779, 1997, doi: 10.1109/34.598235.
[2]
L. Wiskott, „Phantom faces for face analysis“, Pattern recognition, Bd. 30, Nr. 6, S. 837–846, 1997, doi: 10.1016/s0031-3203(96)00132-x.
[3]
L. Wiskott, J.-M. Fellous, N. Krüger, und C. von der Malsburg, „Face recognition by elastic bunch graph matching“, in Proceedings, 1997, S. 129–132.
[4]
L. Wiskott und T. J. Sejnowski, „Objective functions for neural map formation“, in Artificial neural networks – ICANN ’97, Lausanne, Schweiz, 1997, Bd. 1327, S. 243–248. doi: 10.1007/bfb0020163.
[5]
L. Wiskott, „Segmentation from motion: combining gabor- and mallat-wavelets to overcome aperture and correspondence problem“, in Computer analysis of images and patterns, 1997, Bd. 1296, S. 329–336.
[6]
L. Wiskott, „Phantom faces for face analysis“, in Proceedings, 1997, S. 308–311. doi: 10.1109/icip.1997.632101.
[7]
L. Wiskott, J.-M. Fellous, N. Krüger, und C. von der Malsburg, „Face recognition by elastic bunch graph matching“, in Computer analysis of images and patterns, 1997, Bd. 1296, S. 456–463.
[8]
L. Wiskott und T. J. Sejnowski, „Objective functions for neural map formation“, in Proceedings of the 4th Joint Symposium on neural computation May 17, Los Angeles, 1997, S. 242–248.
[9]
L. Wiskott, „Phantom faces for face analysis“, in Computer analysis of images and patterns, 1997, Bd. 1296, S. 480–487.
[10]
L. Wiskott und T. J. Sejnowski, „Objective functions for neural map formation“, 1997.
[1]
L. Wiskott, „Segmentation from motion: combining gabor- and mallat-wavelets to overcome aperture and correspondence problem“, 1996.
[2]
L. Wiskott, „Phantom faces for face analysis“, in Proceedings of the of the 3rd joint symposium on neural computation, 1996, S. 46–52.
[3]
L. Wiskott und C. von der Malsburg, „Recognizing faces by dynamic link matching“, in Symposium über biologische Informationsverarbeitung und Neuronale Netze – SINN ‘95, 1996, S. 63–68.
[4]
L. Wiskott und C. von der Malsburg, „Recognizing faces by dynamic link matching“, US-EC neuroinformatics, 1996, Publiziert.
[5]
M. Pötzsch, T. Maurer, L. Wiskott, und C. von der Malsburg, „Reconstruction from graphs labeled with responses of gabor filters“, in Artificial neural networks – ICANN 96, 1996, Bd. 1112, S. 845–850.
[6]
L. Wiskott und C. von der Malsburg, „Face recognition by dynamic link matching: 11“, in Lateral interactions in the cortex, J. Sirosh, R. Miikkulainen, und Y. Choe, Hrsg. The UTCS Neural Networks Research Group, Austin, TX, 1996. [Online]. Verfügbar unter: http://www.cs.utexas.edu/users/nn/web-pubs/htmlbook96/wiskott/
[7]
L. Wiskott und C. von der Malsburg, „Face recognition by dynamic link matching“, 1996. [Online]. Verfügbar unter: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.17.4773&rep=rep1&type=pdf
[8]
L. Wiskott, „Phantom faces for face analysis“, 1996.
[9]
L. Wiskott, J.-M. Fellous, N. Krüger, und C. von der Malsburg, „Face recognition by elastic bunch graph matching“, 1996.
[1]
L. Wiskott und C. von der Malsburg, „Recognizing faces by dynamic link matching“, in Actes, 1995, S. 347–352. [Online]. Verfügbar unter: http://fias.uni-frankfurt.de/~malsburg/papers/face-recognition-DLM-ICANN95.pdf
[2]
L. Wiskott, Labeled graphs and dynamic link matching for face recognition and scene analysis. Thun [u.a.]: Deutsch, 1995.
[3]
L. Wiskott, J.-M. Fellous, N. Krüger, und C. von der Malsburg, „Face recognition and gender determination“, in Proceedings, 1995, S. 92–97.
[4]
L. Wiskott und C. von der Malsburg, „Face recognition by dynamic link matching“, in Proceedings of the international conference on artificial neural networks ICANN‘95, Paris, 1995, S. 347–352.
[1]
L. Wiskott und C. von der Malsburg, „A neural system for the recognition of partially occluded objects in cluttered scenes“, in Advances in pattern recognition systems using neural network technologies, Bd. 7, I. Guyon und P. S.-P. Wang, Hrsg. Singapore [u.a.]: World Scientific Publishing, 1994.
[2]
L. Wiskott und C. von der Malsburg, „Object recognition with dynamic link matching“, in Neural computing, 1994, Bd. 103, S. 20–21.
[1]
L. Wiskott und C. von der Malsburg, „A neural system for the recognition of partially occluded objects in cluttered scenes“, International journal of pattern recognition and artificial intelligence, Bd. 7, Nr. 4, S. 935–948, 1993.
[2]
R. Doursat u. a., „Neural mechanisms of elastic pattern matching“, 1993.
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R. Doursat u. a., „Neural mechanisms of elastic pattern matching“, in Statusseminar des BMFT Neuroinformatik, 1992, S. 71–82. [Online]. Verfügbar unter: http://www.neuroinformatik.ruhr-uni-bochum.de/PEOPLE/rolf/articles/irini-93-01.pdf
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O. Althoff, A. Erdmann, L. Wiskott, und P. Hertel, „The photorefractive effect in LiNbO₃ at high light intensity“, Physica status solidi A, Bd. 128, Nr. 1, S. K41‐K46, 1991, doi: 10.1002/pssa.2211280138.
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Publications

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Artikel Geplante Veröffentlichung

Multiple Group Action Dlogs with(out) Precomputation

Alexander May, Massimo Ostuzzi

In: Preprint, Geplante Veröffentlichung.

Links | Schlagwörter: Preprint

Workshop

How to Lose Some Weight - A Practical Template Syndrome Decoding Attack

Sebastian Bitzer, Jeroen Delvaux, Elena Kirshanova, Sebastian Maaßen, Alexander May, Antonia Wachter-Zeh

Coding and Cryptography (WCC 24), 2024.

Links | Schlagwörter: Crypto Others

Proceedings Article

Too Many Hints - When LLL Breaks LWE

Alexander May, Julian Nowakowski

In: Advances in Cryptology (ASIACRYPT 23), 2023.

Links | Schlagwörter: Crypto Flagship, Rank A*/A

Proceedings Article

How to Enumerate LWE Keys as Narrow as in Kyber/Dilithium

Timo Glaser, Alexander May

In: Cryptology and Network Security (CANS 23), S. 75–100, Springer, 2023.

Links | Schlagwörter: Crypto Others

Proceedings Article

Breaking Goppa-based McEliece with hints

Elena Kirshanova, Alexander May

In: Security and Cryptography for Networks (SCN 22), and Journal of Information and Computation, Volume 293, 2023.

Links | Schlagwörter: Crypto Others

Proceedings Article

Low Memory Attacks on Small Key CSIDH

Jesús-Javier Chi-Dominguez, Andre Esser, Sabrina Kunzweiler, Alexander May

In: Applied Cryptography and Network Security (ACNS 23), S. 276–304, Springer, 2023.

Links | Schlagwörter: Crypto Others

Proceedings Article

New NTRU Records with Improved Lattice Bases

Elena Kirshanova, Alexander May, Julian Nowakowski

In: Post-Quantum Cryptography (PQCrypto 23), S. 167–195, Springer, 2023.

Links | Schlagwörter: Crypto Others

Proceedings Article

Dlog is Practically as Hard (or Easy) as DH - Solving Dlogs via DH Oracles on EC Standards

Alexander May, Carl Richard Theodor Schneider

In: Transactions on Cryptographic Hardware and Embedded Systems (TCHES), S. 146–166, 2023.

Links | Schlagwörter: Crypto Area, Rank A*/A

Proceedings Article

Partial Key Exposure Attacks on BIKE, Rainbow and NTRU

Andre Esser, Alexander May, Javier A. Verbel, Weiqiang Wen

In: Advances in Cryptology (CRYPTO 2022) , S. 346–375, Springer, 2022.

Links | Schlagwörter: Crypto Flagship, Rank A*/A

Proceedings Article

Approximate Divisor Multiples - Factoring with Only a Third of the Secret CRT-Exponents

Alexander May, Julian Nowakowski, Santanu Sarkar

In: Advances in Cryptology (EUROCRYPT 22) , S. 147–167, Springer, 2022.

Links | Schlagwörter: Crypto Flagship, Rank A*/A

20 Einträge « ‹ 1 von 2 › »

Memberships

  • BITSI – Bochumer Verein zur Förderung der IT-Sicherheit und Informatik
  • CASA – DFG Excellence Cluster
  • QSI – EU Marie Curie Network
  • HGI – Horst Görtz Institute
  • IACR – Cryptology Research

Lectures (Moodle/Notes)

Crypto Challenges

Former PhDs

  1. Önder Askin, 2024
  2. Floyd Zweydinger, 2023
  3. Lars Schlieper, 2022
  4. Alexander Helm, 2020
  5. Andre Esser, 2020
  6. Matthias Minihold, 2019 
  7. Leif Both, 2018
  8. Robert Kübler, 2018
  9. Elena Kirshanova, 2016
  10. Ilya Ozerov, 2016
  11. Gottfried Herold, 2014
  12. Alexander Meurer, 2014
  13. Mathias Herrmann, 2011
  14. Maike Ritzenhofen, 2010

Calvin & Hobbes

Prof. Dr. Yuval Yarom

Professor / Head of Chair

Address:
Ruhr-University Bochum
Faculty of Computer Science
Computer Security
Universitätsstr. 150
D-44801 Bochum

Room: MC 5.148
Telephone: (+49) (0) 234 32 – 19290
Office Hours: By arrangement
E-Mail: yuval.yarom(at)rub.de

Veröffentlichungen

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