Prof. Dr. Laurenz Wiskott
Theory of Neural Systems
Theory of Neural Systems
Ruhr-Universität Bochum
Institut für Neuroinformatik
Universitätsstraße 150
Building NB, Room NB 3/29
Universitätsstraße 150
Building NB, Room NB 3/29
D-44801 Bochum, Germany
Affiliations at the Ruhr-University
Dean of Studies of the Bachelor/Master program Applied Computer Science
Department of Physics and Astronomy
Department of Electrical Engineering and Information Sciences
Research Department of Neuroscience
International Graduate School of Neuroscience
Center for Mind and Cognition
Center for Mind, Brain and Cognitive Evolution
Selected Publications
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Improved graph-based SFA: information preservation complements the slowness principleEscalante-B., A. N., & Wiskott, L.Machine Learning
@article{Escalante-B.Wiskott2019, author = {Escalante-B., Alberto N. and Wiskott, Laurenz}, title = {Improved graph-based SFA: information preservation complements the slowness principle}, journal = {Machine Learning}, month = {December}, year = {2019}, doi = {10.1007/s10994-019-05860-9}, }
Escalante-B., A. N., & Wiskott, L.. (2019). Improved graph-based SFA: information preservation complements the slowness principle. Machine Learning. http://doi.org/10.1007/s10994-019-05860-9Measuring the Data Efficiency of Deep Learning MethodsHlynsson, H., Escalante-B., A., & Wiskott, L.In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods SCITEPRESS - Science and Technology Publications@inproceedings{HlynssonEscalante-B.Wiskott2019, author = {Hlynsson, Hlynur and Escalante-B., Alberto and Wiskott, Laurenz}, title = {Measuring the Data Efficiency of Deep Learning Methods}, booktitle = {Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods}, publisher = {SCITEPRESS - Science and Technology Publications}, year = {2019}, doi = {10.5220/0007456306910698}, }
Hlynsson, H., Escalante-B., A., & Wiskott, L.. (2019). Measuring the Data Efficiency of Deep Learning Methods. In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications. http://doi.org/10.5220/00074563069106982018
The Interaction between Semantic Representation and Episodic MemoryFang, J., Rüther, N., Bellebaum, C., Wiskott, L., & Cheng, S.Neural Computation, 30(2), 293–332@article{FangRütherBellebaumEtAl2018, author = {Fang, Jing and Rüther, Naima and Bellebaum, Christian and Wiskott, Laurenz and Cheng, Sen}, title = {The Interaction between Semantic Representation and Episodic Memory}, journal = {Neural Computation}, volume = {30}, number = {2}, pages = {293–332}, month = {February}, year = {2018}, doi = {10.1162/neco_a_01044}, }
Fang, J., Rüther, N., Bellebaum, C., Wiskott, L., & Cheng, S.. (2018). The Interaction between Semantic Representation and Episodic Memory. Neural Computation, 30(2), 293–332. http://doi.org/10.1162/neco_a_01044Slowness as a Proxy for Temporal Predictability: An Empirical ComparisonWeghenkel, B., & Wiskott, L.Neural Computation, 30(5), 1151–1179@article{WeghenkelWiskott2018, author = {Weghenkel, Björn and Wiskott, Laurenz}, title = {Slowness as a Proxy for Temporal Predictability: An Empirical Comparison}, journal = {Neural Computation}, volume = {30}, number = {5}, pages = {1151–1179}, year = {2018}, doi = {10.1162/neco_a_01070}, }
Weghenkel, B., & Wiskott, L.. (2018). Slowness as a Proxy for Temporal Predictability: An Empirical Comparison. Neural Computation, 30(5), 1151–1179. http://doi.org/10.1162/neco_a_010702017
Gaussian-binary restricted Boltzmann machines for modeling natural image statisticsMelchior, J., Wang, N., & Wiskott, L.PLOS ONE, 12(2), 1–24@article{MelchiorWangWiskott2017, author = {Melchior, Jan and Wang, Nan and Wiskott, Laurenz}, title = {Gaussian-binary restricted Boltzmann machines for modeling natural image statistics}, journal = {PLOS ONE}, volume = {12}, number = {2}, pages = {1–24}, month = {February}, year = {2017}, doi = {10.1371/journal.pone.0171015}, }
Melchior, J., Wang, N., & Wiskott, L.. (2017). Gaussian-binary restricted Boltzmann machines for modeling natural image statistics. PLOS ONE, 12(2), 1–24. http://doi.org/10.1371/journal.pone.01710152016
Theoretical analysis of the optimal free responses of graph-based SFA for the design of training graphs.Escalante-B., A. N., & Wiskott, L.Journal of Machine Learning Research, 17(157), 1–36@article{Escalante-B.Wiskott2016b, author = {Escalante-B., Alberto N. and Wiskott, Laurenz}, title = {Theoretical analysis of the optimal free responses of graph-based SFA for the design of training graphs.}, journal = {Journal of Machine Learning Research}, volume = {17}, number = {157}, pages = {1–36}, year = {2016}, }
Escalante-B., A. N., & Wiskott, L.. (2016). Theoretical analysis of the optimal free responses of graph-based SFA for the design of training graphs. Journal of Machine Learning Research, 17(157), 1–36. Retrieved from http://jmlr.org/papers/v17/15-311.htmlHow to Center Deep Boltzmann MachinesMelchior, J., Fischer, A., & Wiskott, L.Journal of Machine Learning Research, 17(99), 1–61@article{MelchiorFischerWiskott2016, author = {Melchior, Jan and Fischer, Asja and Wiskott, Laurenz}, title = {How to Center Deep Boltzmann Machines}, journal = {Journal of Machine Learning Research}, volume = {17}, number = {99}, pages = {1–61}, year = {2016}, }
Melchior, J., Fischer, A., & Wiskott, L.. (2016). How to Center Deep Boltzmann Machines. Journal of Machine Learning Research, 17(99), 1–61. Retrieved from http://jmlr.org/papers/v17/14-237.html2015
Memory Storage Fidelity in the Hippocampal Circuit: The Role of Subregions and Input StatisticsNeher, T., Cheng, S., & Wiskott, L.PLoS Computational Biology, 11(5), e1004250@article{NeherChengWiskott2015, author = {Neher, Torsten and Cheng, Sen and Wiskott, Laurenz}, title = {Memory Storage Fidelity in the Hippocampal Circuit: The Role of Subregions and Input Statistics}, journal = {PLoS Computational Biology}, volume = {11}, number = {5}, pages = {e1004250}, year = {2015}, doi = {10.1371/journal.pcbi.1004250}, }
Neher, T., Cheng, S., & Wiskott, L.. (2015). Memory Storage Fidelity in the Hippocampal Circuit: The Role of Subregions and Input Statistics. PLoS Computational Biology, 11(5), e1004250. http://doi.org/10.1371/journal.pcbi.1004250Modeling place field activity with hierarchical slow feature analysisSchoenfeld, F., & Wiskott, L.Frontiers in Computational Neuroscience, 9(51)@article{SchoenfeldWiskott2015, author = {Schoenfeld, Fabian and Wiskott, Laurenz}, title = {Modeling place field activity with hierarchical slow feature analysis}, journal = {Frontiers in Computational Neuroscience}, volume = {9}, number = {51}, year = {2015}, doi = {10.3389/fncom.2015.00051}, }
Schoenfeld, F., & Wiskott, L.. (2015). Modeling place field activity with hierarchical slow feature analysis. Frontiers in Computational Neuroscience, 9(51). http://doi.org/10.3389/fncom.2015.000512011
A theory of Slow Feature Analysis for transformation-based input signals with an application to complex cellsSprekeler, H., & Wiskott, L.Neural Computation, 23(2), 303–335@article{SprekelerWiskott2011, author = {Sprekeler, Henning and Wiskott, Laurenz}, title = {A theory of Slow Feature Analysis for transformation-based input signals with an application to complex cells}, journal = {Neural Computation}, volume = {23}, number = {2}, pages = {303–335}, year = {2011}, doi = {10.1162/NECO_a_00072}, }
Sprekeler, H., & Wiskott, L.. (2011). A theory of Slow Feature Analysis for transformation-based input signals with an application to complex cells. Neural Computation, 23(2), 303–335. http://doi.org/10.1162/NECO_a_00072-
2018-01 - 2018-05
Simons, Berkeley, California - Visiting scholar
Visiting scholar at the Simons Institute for the Theory of Computing at the University of California, Berkeley, USA
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since Nov 2008
INI, Bochum, Germany - Professor (W3)
Professor (W3) at the Institute for Neural Computation at the Ruhr-University Bochum
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2006-2008
ITB, Berlin, Germany - Professor (W2)
Professor (W2) for Computational Neuroscience and Neuroinformatics at the Institute for Theoretical Biology at the Humboldt-University Berlin
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2000-2006
ITB, Berlin, Germany - Junior research group leader
Junior research group leader at the Institute for Theoretical Biology at the Humboldt-University Berlin (supported by the Volkswagen Foundation)
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1999-2000
ITB, Berlin, Germany - Research associate
Research associate with Prof. Andreas V. M. Herz at the Innovationskolleg Theoretische Biologie at the Humboldt-University Berlin
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1998-1999
Wiko, Berlin, Germany - Fellow
Fellow at the Wissenschaftskolleg zu Berlin / Institute for Advanced Study in Berlin, Germany
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1995-1998
CNL, San Diego, California - Research associate
Research associate with Prof. Terrence Sejnowski at the Computational Neurobiology Laboratory at the Salk Institute for Biological Studies, San Diego, California (supported by a Lynen Research Fellowship from the Alexander von Humboldt Foundation, Germany)
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Oct. 1995
INI, Bochum, Germany - PhD (Dr. rer. nat.) in physics
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1993, 1994
USC, Los Angeles, California - Visiting research assistant
Visiting research assistant (6 months) at the Laboratory for Computational and Biological Vision at the University of Southern California, Los Angeles, California
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1990-1995
INI, Bochum, Germany - Research assistant
Research assistant with Prof. Christoph von der Malsburg at the Institute for Neural Computation at the Ruhr-University Bochum, Germany with several visits at the Laboratory for Computational and Biological Vision at the University of Southern California, Los Angeles, California
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Sep. 1990
Uni, Osnabrück, Germany - Diploma in physics
Diploma in physics at the University Osnabrück, Germany
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1989-1990
Uni, Osnabrück, Germany - Studies of physics
Studies of physics at the University Osnabrück, Germany
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1986-1988
MPI, Göttingen, Germany - Undergraduate research assistant
Undergraduate research assistant at the Max-Planck-Institute for Biophysical Chemistry in Göttingen, developing an operating system for a parallel computer
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1985-1989
Uni, Göttingen, Germany - Studies of physics
Studies of physics at the University Göttingen, Germany
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Mitigating Biases using an Additive Grade Point Model: Towards Trustworthy Curriculum Analytics MeasuresBaucks, F., & Wiskott, L.Proceedings of the 21th DELFI
@article{BaucksWiskott2023b, author = {Baucks, Frederik and Wiskott, Laurenz}, title = {Mitigating Biases using an Additive Grade Point Model: Towards Trustworthy Curriculum Analytics Measures}, pages = {}, month = {July }, year = {2023}, }
Baucks, F., & Wiskott, L.. (2023). Mitigating Biases using an Additive Grade Point Model: Towards Trustworthy Curriculum Analytics Measures. Proceedings of the 21th DELFI.Tracing Changes in University Course Difficulty Using Item-Response TheoryBaucks, F., Schmucker, R., & Wiskott, L.AAAI Workshop on AI for Education@article{BaucksSchmuckerWiskott2023, author = {Baucks, Frederik and Schmucker, Robin and Wiskott, Laurenz}, title = {Tracing Changes in University Course Difficulty Using Item-Response Theory}, year = {2023}, }
Baucks, F., Schmucker, R., & Wiskott, L.. (2023). Tracing Changes in University Course Difficulty Using Item-Response Theory. AAAI Workshop on AI for Education.Von der Forschung in die Praxis: Entwicklung eines Dashboards für die StudienberatungBaucks, F., & Wiskott, L.2nd Learning AID@article{BaucksWiskott2023, author = {Baucks, Frederik and Wiskott, Laurenz}, title = {Von der Forschung in die Praxis: Entwicklung eines Dashboards für die Studienberatung}, year = {2023}, }
Baucks, F., & Wiskott, L.. (2023). Von der Forschung in die Praxis: Entwicklung eines Dashboards für die Studienberatung. 2nd Learning AID.Sample-Based Rule Extraction for Explainable Reinforcement LearningEngelhardt, R. C., Lange, M., Wiskott, L., & Konen, W.In Machine Learning, Optimization, and Data Science (pp. 330–345) Springer Nature Switzerland@incollection{EngelhardtLangeWiskottEtAl2023, author = {Engelhardt, Raphael C. and Lange, Moritz and Wiskott, Laurenz and Konen, Wolfgang}, title = {Sample-Based Rule Extraction for Explainable Reinforcement Learning}, booktitle = {Machine Learning, Optimization, and Data Science}, pages = {330–345}, publisher = {Springer Nature Switzerland}, year = {2023}, doi = {10.1007/978-3-031-25599-1_25}, }
Engelhardt, R. C., Lange, M., Wiskott, L., & Konen, W. (2023). Sample-Based Rule Extraction for Explainable Reinforcement Learning. In Machine Learning, Optimization, and Data Science (pp. 330–345). Springer Nature Switzerland. http://doi.org/10.1007/978-3-031-25599-1_25Modeling the function of episodic memory in spatial learningZeng, X., Diekmann, N., Wiskott, L., & Cheng, S.Frontiers in Psychology, 14@article{ZengDiekmannWiskottEtAl2023, author = {Zeng, Xiangshuai and Diekmann, Nicolas and Wiskott, Laurenz and Cheng, Sen}, title = {Modeling the function of episodic memory in spatial learning}, journal = {Frontiers in Psychology}, volume = {14}, year = {2023}, doi = {10.3389/fpsyg.2023.1160648}, }
Zeng, X., Diekmann, N., Wiskott, L., & Cheng, S.. (2023). Modeling the function of episodic memory in spatial learning. Frontiers in Psychology, 14. http://doi.org/10.3389/fpsyg.2023.11606482022
A Model of Semantic Completion in Generative Episodic MemoryFayyaz, Z., Altamimi, A., Zoellner, C., Klein, N., Wolf, O. T., Cheng, S., & Wiskott, L.Neural Computation, 34(9), 1841–1870@article{FayyazAltamimiZoellnerEtAl2022, author = {Fayyaz, Zahra and Altamimi, Aya and Zoellner, Carina and Klein, Nicole and Wolf, Oliver T. and Cheng, Sen and Wiskott, Laurenz}, title = {A Model of Semantic Completion in Generative Episodic Memory}, journal = {Neural Computation}, volume = {34}, number = {9}, pages = {1841–1870}, month = {August}, year = {2022}, doi = {10.1162/neco_a_01520}, }
Fayyaz, Z., Altamimi, A., Zoellner, C., Klein, N., Wolf, O. T., Cheng, S., & Wiskott, L.. (2022). A Model of Semantic Completion in Generative Episodic Memory. Neural Computation, 34(9), 1841–1870. http://doi.org/10.1162/neco_a_01520Simulating Policy Changes in Prerequisite-Free Curricula: A Supervised Data-Driven ApproachBaucks, F., & Wiskott, L.Proceedings of the 15th International Conference on Educational Data Mining, 470–476@article{BaucksWiskott2022, author = {Baucks, Frederik and Wiskott, Laurenz}, title = {Simulating Policy Changes in Prerequisite-Free Curricula: A Supervised Data-Driven Approach}, pages = {470–476}, month = {July }, year = {2022}, doi = {10.5281/zenodo.6853177}, }
Baucks, F., & Wiskott, L.. (2022). Simulating Policy Changes in Prerequisite-Free Curricula: A Supervised Data-Driven Approach. Proceedings of the 15th International Conference on Educational Data Mining, 470–476. http://doi.org/10.5281/zenodo.6853177Latent Representation Prediction NetworksHlynsson, H. D., Schüler, M., Schiewer, R., Glasmachers, T., & Wiskott, L.International Journal of Pattern Recognition and Artificial Intelligence, 36(01), 2251002@article{HlynssonSchülerSchiewerEtAl2022, author = {Hlynsson, Hlynur David and Schüler, Merlin and Schiewer, Robin and Glasmachers, Tobias and Wiskott, Laurenz}, title = {Latent Representation Prediction Networks}, journal = {International Journal of Pattern Recognition and Artificial Intelligence}, volume = {36}, number = {01}, pages = {2251002}, year = {2022}, doi = {10.1142/S0218001422510028}, }
Hlynsson, H. D., Schüler, M., Schiewer, R., Glasmachers, T., & Wiskott, L.. (2022). Latent Representation Prediction Networks. International Journal of Pattern Recognition and Artificial Intelligence, 36(01), 2251002. http://doi.org/10.1142/S0218001422510028Reduction of Variance-related Error through Ensembling: Deep Double Descent and Out-of-Distribution GeneralizationRath-Manakidis, P., Hlynsson, H. D., & Wiskott, L.In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM, (pp. 31–40) SciTePress@inproceedings{Rath-ManakidisHlynssonWiskott2022, author = {Rath-Manakidis, Pavlos and Hlynsson, Hlynur Davíð and Wiskott, Laurenz}, title = {Reduction of Variance-related Error through Ensembling: Deep Double Descent and Out-of-Distribution Generalization}, booktitle = {Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM,}, pages = {31–40}, organization = {INSTICC}, publisher = {SciTePress}, year = {2022}, doi = {10.5220/0010821300003122}, }
Rath-Manakidis, P., Hlynsson, H. D., & Wiskott, L.. (2022). Reduction of Variance-related Error through Ensembling: Deep Double Descent and Out-of-Distribution Generalization. In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM, (pp. 31–40). SciTePress. http://doi.org/10.5220/0010821300003122Modular Networks Prevent Catastrophic Interference in Model-Based Multi-task Reinforcement LearningSchiewer, R., & Wiskott, L.In Machine Learning, Optimization, and Data Science (pp. 299–313) Springer International Publishing@incollection{SchiewerWiskott2022, author = {Schiewer, Robin and Wiskott, Laurenz}, title = {Modular Networks Prevent Catastrophic Interference in Model-Based Multi-task Reinforcement Learning}, booktitle = {Machine Learning, Optimization, and Data Science}, pages = {299–313}, publisher = {Springer International Publishing}, year = {2022}, doi = {10.1007/978-3-030-95470-3_23}, }
Schiewer, R., & Wiskott, L.. (2022). Modular Networks Prevent Catastrophic Interference in Model-Based Multi-task Reinforcement Learning. In Machine Learning, Optimization, and Data Science (pp. 299–313). Springer International Publishing. http://doi.org/10.1007/978-3-030-95470-3_232021
Context-dependent extinction learning emerging from raw sensory inputs: a reinforcement learning approachWalther, T., Diekmann, N., Vijayabaskaran, S., Donoso, J. R., Manahan-Vaughan, D., Wiskott, L., & Cheng, S.Scientific Reports, 11(1)@article{WaltherDiekmannVijayabaskaranEtAl2021, author = {Walther, Thomas and Diekmann, Nicolas and Vijayabaskaran, Sandhiya and Donoso, José R. and Manahan-Vaughan, Denise and Wiskott, Laurenz and Cheng, Sen}, title = {Context-dependent extinction learning emerging from raw sensory inputs: a reinforcement learning approach}, journal = {Scientific Reports}, volume = {11}, number = {1}, month = {February}, year = {2021}, doi = {10.1038/s41598-021-81157-z}, }
Walther, T., Diekmann, N., Vijayabaskaran, S., Donoso, J. R., Manahan-Vaughan, D., Wiskott, L., & Cheng, S.. (2021). Context-dependent extinction learning emerging from raw sensory inputs: a reinforcement learning approach. Scientific Reports, 11(1). http://doi.org/10.1038/s41598-021-81157-zReward prediction for representation learning and reward shapingHlynsson, H. D., & Wiskott, L.arXiv@misc{HlynssonWiskott2021, author = {Hlynsson, Hlynur Davíð and Wiskott, Laurenz}, title = {Reward prediction for representation learning and reward shaping}, year = {2021}, doi = {10.48550/ARXIV.2105.03172}, }
Hlynsson, H. D., & Wiskott, L.. (2021). Reward prediction for representation learning and reward shaping. arXiv. http://doi.org/10.48550/ARXIV.2105.03172Exploring Slow Feature Analysis for Extracting Generative Latent FactorsMenne, M., Schüler, M., & Wiskott, L.In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods SCITEPRESS - Science and Technology Publications@inproceedings{MenneSchülerWiskott2021, author = {Menne, Max and Schüler, Merlin and Wiskott, Laurenz}, title = {Exploring Slow Feature Analysis for Extracting Generative Latent Factors}, booktitle = {Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods}, publisher = {SCITEPRESS - Science and Technology Publications}, year = {2021}, doi = {10.5220/0010391401200131}, }
Menne, M., Schüler, M., & Wiskott, L.. (2021). Exploring Slow Feature Analysis for Extracting Generative Latent Factors. In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications. http://doi.org/10.5220/00103914012001312020
Improving sensory representations using episodic memoryGörler, R., Wiskott, L., & Cheng, S.Hippocampus, 30(6), 638–656@article{GörlerWiskottCheng2020, author = {Görler, Richard and Wiskott, Laurenz and Cheng, Sen}, title = {Improving sensory representations using episodic memory}, journal = {Hippocampus}, volume = {30}, number = {6}, pages = {638–656}, month = {December}, year = {2020}, doi = {10.1002/hipo.23186}, }
Görler, R., Wiskott, L., & Cheng, S.. (2020). Improving sensory representations using episodic memory. Hippocampus, 30(6), 638–656. http://doi.org/10.1002/hipo.23186Latent Representation Prediction NetworksHlynsson, H. D., Schüler, M., Schiewer, R., Glasmachers, T., & Wiskott, L.arXiv preprint arXiv:2009.09439@article{HlynssonSchülerSchiewerEtAl2020, author = {Hlynsson, Hlynur Davíð and Schüler, Merlin and Schiewer, Robin and Glasmachers, Tobias and Wiskott, Laurenz}, title = {Latent Representation Prediction Networks}, journal = {arXiv preprint arXiv:2009.09439}, year = {2020}, }
Hlynsson, H. D., Schüler, M., Schiewer, R., Glasmachers, T., & Wiskott, L.. (2020). Latent Representation Prediction Networks. arXiv preprint arXiv:2009.09439.Singular Sturm-Liouville Problems with Zero Potential (q=0) and Singular Slow Feature AnalysisRichthofer, S., & Wiskott, L.CoRR e-print arXiv:2011.04765@misc{RichthoferWiskott2020, author = {Richthofer, Stefan and Wiskott, Laurenz}, title = {Singular Sturm-Liouville Problems with Zero Potential (q=0) and Singular Slow Feature Analysis}, howpublished = {e-print arXiv:2011.04765}, year = {2020}, }
Richthofer, S., & Wiskott, L.. (2020). Singular Sturm-Liouville Problems with Zero Potential (q=0) and Singular Slow Feature Analysis. CoRR. e-print arXiv:2011.04765.2019
Improved graph-based SFA: information preservation complements the slowness principleEscalante-B., A. N., & Wiskott, L.Machine Learning@article{Escalante-B.Wiskott2019, author = {Escalante-B., Alberto N. and Wiskott, Laurenz}, title = {Improved graph-based SFA: information preservation complements the slowness principle}, journal = {Machine Learning}, month = {December}, year = {2019}, doi = {10.1007/s10994-019-05860-9}, }
Escalante-B., A. N., & Wiskott, L.. (2019). Improved graph-based SFA: information preservation complements the slowness principle. Machine Learning. http://doi.org/10.1007/s10994-019-05860-9Measuring the Data Efficiency of Deep Learning MethodsHlynsson, H., Escalante-B., A., & Wiskott, L.In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods SCITEPRESS - Science and Technology Publications@inproceedings{HlynssonEscalante-B.Wiskott2019, author = {Hlynsson, Hlynur and Escalante-B., Alberto and Wiskott, Laurenz}, title = {Measuring the Data Efficiency of Deep Learning Methods}, booktitle = {Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods}, publisher = {SCITEPRESS - Science and Technology Publications}, year = {2019}, doi = {10.5220/0007456306910698}, }
Hlynsson, H., Escalante-B., A., & Wiskott, L.. (2019). Measuring the Data Efficiency of Deep Learning Methods. In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications. http://doi.org/10.5220/0007456306910698Learning Gradient-Based ICA by Neurally Estimating Mutual InformationHlynsson, H. D. ′ið, & Wiskott, L.In C. Benzmüller & Stuckenschmidt, H. (Eds.), KI 2019: Advances in Artificial Intelligence (pp. 182–187) Cham: Springer International Publishing@inproceedings{HlynssonWiskott2019, author = {Hlynsson, Hlynur Dav′ið and Wiskott, Laurenz}, title = {Learning Gradient-Based ICA by Neurally Estimating Mutual Information}, booktitle = {KI 2019: Advances in Artificial Intelligence}, editor = {Benzmüller, Christoph and Stuckenschmidt, Heiner}, pages = {182–187}, publisher = {Springer International Publishing}, address = {Cham}, year = {2019}, }
Hlynsson, H. D. ′ið, & Wiskott, L.. (2019). Learning Gradient-Based ICA by Neurally Estimating Mutual Information. In C. Benzmüller & Stuckenschmidt, H. (Eds.), KI 2019: Advances in Artificial Intelligence (pp. 182–187). Cham: Springer International Publishing.Learning gradient-based ICA by neurally estimating mutual informationHlynsson, H. D., & Wiskott, L.arXiv, arXiv–1904.09858@article{HlynssonWiskott2019b, author = {Hlynsson, Hlynur Davíð and Wiskott, Laurenz}, title = {Learning gradient-based ICA by neurally estimating mutual information}, journal = {arXiv}, pages = {arXiv–1904.09858}, year = {2019}, }
Hlynsson, H. D., & Wiskott, L.. (2019). Learning gradient-based ICA by neurally estimating mutual information. arXiv, arXiv–1904.09858.Measuring the Data Efficiency of Deep Learning MethodsHlynsson, H. D., Wiskott, L., & others,arXiv, arXiv–1907@article{HlynssonWiskottothers2019, author = {Hlynsson, Hlynur Davíð and Wiskott, Laurenz and others}, title = {Measuring the Data Efficiency of Deep Learning Methods}, journal = {arXiv}, pages = {arXiv–1907}, year = {2019}, }
Hlynsson, H. D., Wiskott, L., & others,. (2019). Measuring the Data Efficiency of Deep Learning Methods. arXiv, arXiv–1907.Gradient-based Training of Slow Feature Analysis by Differentiable Approximate WhiteningSchüler, M., Hlynsson, H. D. ′ið, & Wiskott, L.In W. S. Lee & Suzuki, T. (Eds.), Proceedings of The Eleventh Asian Conference on Machine Learning (Vol. 101, pp. 316–331) Nagoya, Japan: PMLR@inproceedings{SchülerHlynssonWiskott2019, author = {Schüler, Merlin and Hlynsson, Hlynur Dav′ið and Wiskott, Laurenz}, title = {Gradient-based Training of Slow Feature Analysis by Differentiable Approximate Whitening}, booktitle = {Proceedings of The Eleventh Asian Conference on Machine Learning}, editor = {Lee, Wee Sun and Suzuki, Taiji}, pages = {316–331}, publisher = {PMLR}, volume = {101}, series = {Proceedings of Machine Learning Research}, address = {Nagoya, Japan}, month = {}, year = {2019}, }
Schüler, M., Hlynsson, H. D. ′ið, & Wiskott, L.. (2019). Gradient-based Training of Slow Feature Analysis by Differentiable Approximate Whitening. In W. S. Lee & Suzuki, T. (Eds.), Proceedings of The Eleventh Asian Conference on Machine Learning (Vol. 101, pp. 316–331). Nagoya, Japan: PMLR. Retrieved from http://proceedings.mlr.press/v101/schuler19a.html2018
Storage fidelity for sequence memory in the hippocampal circuitBayati, M., Neher, T., Melchior, J., Diba, K., Wiskott, L., & Cheng, S.PLOS ONE, 13(10), e0204685@article{BayatiNeherMelchiorEtAl2018, author = {Bayati, Mehdi and Neher, Torsten and Melchior, Jan and Diba, Kamran and Wiskott, Laurenz and Cheng, Sen}, title = {Storage fidelity for sequence memory in the hippocampal circuit}, journal = {PLOS ONE}, volume = {13}, number = {10}, pages = {e0204685}, month = {October}, year = {2018}, doi = {10.1371/journal.pone.0204685}, }
2023