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ORCIDLaurenz Wiskott
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- affiliation: Ruhr-University Bochum, Germany
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2020 – today
- 2024
[j49]Malte Schilling
, Barbara Hammer, Frank W. Ohl, Helge J. Ritter, Laurenz Wiskott:
Modularity in Nervous Systems - a Key to Efficient Adaptivity for Deep Reinforcement Learning. Cogn. Comput. 16(5): 2358-2373 (2024)- [j48]Max Bauroth, Pavlos Rath-Manakidis, Valentin Langholf, Laurenz Wiskott, Tobias Glasmachers:
tachAId - An interactive tool supporting the design of human-centered AI solutions. Frontiers Artif. Intell. 7 (2024) - [j47]Jan Melchior, Robin Schiewer, Laurenz Wiskott:
Hebbian Descent: A Unified View on Log-Likelihood Learning. Neural Comput. 36(9): 1669-1712 (2024) - [c40]Jan Rathjens, Laurenz Wiskott:
Antagonism between Classification and Reconstruction Processes in Deep Predictive Coding Networks. ESANN 2024 - [c39]Shirin Reyhanian, Zahra Fayyaz, Laurenz Wiskott:
Analysis of a Generative Model of Episodic Memory Based on Hierarchical VQ-VAE and Transformer. ICANN (4) 2024: 77-93 - [c38]Frederik Baucks, Robin Schmucker, Laurenz Wiskott:
Gaining Insights into Course Difficulty Variations Using Item Response Theory. LAK 2024: 450-461 - [c37]Frederik Baucks, Robin Schmucker, Conrad Borchers, Zachary A. Pardos, Laurenz Wiskott:
Gaining Insights into Group-Level Course Difficulty via Differential Course Functioning. L@S 2024: 165-176 - [c36]Raphael C. Engelhardt, Moritz Lange, Laurenz Wiskott, Wolfgang Konen:
Exploring the Reliability of SHAP Values in Reinforcement Learning. xAI (3) 2024: 165-184 - [i30]Jan Rathjens, Laurenz Wiskott:
Classification and Reconstruction Processes in Deep Predictive Coding Networks: Antagonists or Allies? CoRR abs/2401.09237 (2024) - [i29]Moritz Lange, Raphael C. Engelhardt, Wolfgang Konen, Laurenz Wiskott:
Interpretable Brain-Inspired Representations Improve RL Performance on Visual Navigation Tasks. CoRR abs/2402.12067 (2024) - [i28]Pavlos Rath-Manakidis, Frederik Strothmann, Tobias Glasmachers, Laurenz Wiskott:
ProtoP-OD: Explainable Object Detection with Prototypical Parts. CoRR abs/2402.19142 (2024) - [i27]Robin Schiewer, Anand Subramoney, Laurenz Wiskott:
Exploring the limits of Hierarchical World Models in Reinforcement Learning. CoRR abs/2406.00483 (2024) - [i26]Frederik Baucks, Robin Schmucker, Conrad Borchers, Zachary A. Pardos, Laurenz Wiskott:
Gaining Insights into Group-Level Course Difficulty via Differential Course Functioning. CoRR abs/2406.04348 (2024) - [i25]Eddie Seabrook, Laurenz Wiskott:
What is the relationship between Slow Feature Analysis and the Successor Representation? CoRR abs/2409.16991 (2024) - [i24]Raphael C. Engelhardt, Marcel J. Meinen, Moritz Lange, Laurenz Wiskott, Wolfgang Konen:
Putting the Iterative Training of Decision Trees to the Test on a Real-World Robotic Task. CoRR abs/2412.04974 (2024) - [i23]Jan Rathjens, Shirin Reyhanian, David Kappel, Laurenz Wiskott:
Inverting Visual Representations with Detection Transformers. CoRR abs/2412.06534 (2024) - 2023
- [j46]Raphael C. Engelhardt, Marc Oedingen, Moritz Lange, Laurenz Wiskott, Wolfgang Konen:
Iterative Oblique Decision Trees Deliver Explainable RL Models. Algorithms 16(6): 282 (2023) - [j45]Eddie Seabrook, Laurenz Wiskott:
A Tutorial on the Spectral Theory of Markov Chains. Neural Comput. 35(11): 1713-1796 (2023) - [c35]Frederik Baucks, Laurenz Wiskott:
Mitigating Biases using an Additive Grade Point Model: Towards Trustworthy Curriculum Analytics Measures. DELFI 2023: 12 - [c34]Frederik Baucks, Jonas Leschke, Christian Metzger, Laurenz Wiskott:
Ein Dashboard für die Studienberatung: Technische Infrastruktur und Studienverlaufsplanung im Projekt KI: edu.nrw. DELFI Workshops 2023: 604 - [c33]Raphael C. Engelhardt, Ralitsa Raycheva, Moritz Lange, Laurenz Wiskott, Wolfgang Konen:
Ökolopoly: Case Study on Large Action Spaces in Reinforcement Learning. LOD (2) 2023: 109-123 - [c32]Moritz Lange, Noah Krystiniak, Raphael C. Engelhardt, Wolfgang Konen, Laurenz Wiskott:
Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-visual Environments: A Comparison. LOD (2) 2023: 177-191 - [i22]Moritz Lange, Noah Krystiniak, Raphael C. Engelhardt, Wolfgang Konen, Laurenz Wiskott:
Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-Visual Environments: A Comparison. CoRR abs/2310.04241 (2023) - 2022
- [j44]Hlynur Davíð Hlynsson, Merlin Schüler, Robin Schiewer, Tobias Glasmachers, Laurenz Wiskott:
Latent Representation Prediction Networks. Int. J. Pattern Recognit. Artif. Intell. 36(1): 2251002:1-2251002:30 (2022) - [j43]Zahra Fayyaz, Aya Altamimi, Carina Zoellner, Nicole Klein, Oliver T. Wolf, Sen Cheng, Laurenz Wiskott:
A Model of Semantic Completion in Generative Episodic Memory. Neural Comput. 34(9): 1841-1870 (2022) - [c31]Frederik Baucks, Laurenz Wiskott:
Simulating Policy Changes in Prerequisite-Free Curricula: A Supervised Data-Driven Approach. EDM 2022 - [c30]Pavlos Rath-Manakidis, Hlynur Davíð Hlynsson, Laurenz Wiskott:
Reduction of Variance-related Error through Ensembling: Deep Double Descent and Out-of-Distribution Generalization. ICPRAM 2022: 31-40 - [c29]Raphael C. Engelhardt, Moritz Lange, Laurenz Wiskott, Wolfgang Konen:
Sample-Based Rule Extraction for Explainable Reinforcement Learning. LOD (1) 2022: 330-345 - [i21]Eddie Seabrook, Laurenz Wiskott:
A Tutorial on the Spectral Theory of Markov Chains. CoRR abs/2207.02296 (2022) - 2021
- [c28]Max Menne, Merlin Schüler, Laurenz Wiskott:
Exploring Slow Feature Analysis for Extracting Generative Latent Factors. ICPRAM 2021: 120-131 - [c27]Hlynur Davíð Hlynsson, Laurenz Wiskott:
Reward Prediction for Representation Learning and Reward Shaping. IJCCI 2021: 267-276 - [c26]Robin Schiewer, Laurenz Wiskott:
Modular Networks Prevent Catastrophic Interference in Model-Based Multi-task Reinforcement Learning. LOD 2021: 299-313 - [i20]Hlynur Davíð Hlynsson, Laurenz Wiskott:
Reward prediction for representation learning and reward shaping. CoRR abs/2105.03172 (2021) - [i19]Robin Schiewer, Laurenz Wiskott:
Modular Networks Prevent Catastrophic Interference in Model-Based Multi-Task Reinforcement Learning. CoRR abs/2111.08010 (2021) - [i18]Zahra Fayyaz, Aya Altamimi, Sen Cheng, Laurenz Wiskott:
A model of semantic completion in generative episodic memory. CoRR abs/2111.13537 (2021) - 2020
- [j42]Alberto N. Escalante-B., Laurenz Wiskott:
Improved graph-based SFA: information preservation complements the slowness principle. Mach. Learn. 109(5): 999-1037 (2020) - [c25]Jan Bollenbacher, Florian Soulier, Beate Rhein, Laurenz Wiskott:
Investigating Parallelization of MAML. DS 2020: 294-306 - [i17]Hlynur Davíð Hlynsson, Merlin Schüler, Robin Schiewer, Tobias Glasmachers, Laurenz Wiskott:
Latent Representation Prediction Networks. CoRR abs/2009.09439 (2020) - [i16]Stefan Richthofer, Laurenz Wiskott:
Singular Sturm-Liouville Problems with Zero Potential (q=0) and Singular Slow Feature Analysis. CoRR abs/2011.04765 (2020)
2010 – 2019
- 2019
- [c24]Merlin Schüler, Hlynur Davíð Hlynsson, Laurenz Wiskott:
Gradient-based Training of Slow Feature Analysis by Differentiable Approximate Whitening. ACML 2019: 316-331 - [c23]Laurenz Wiskott, Fabian Schönfeld:
Laplacian Matrix for Dimensionality Reduction and Clustering. eBISS 2019: 93-119 - [c22]Hlynur Davíð Hlynsson, Alberto N. Escalante-B., Laurenz Wiskott:
Measuring the Data Efficiency of Deep Learning Methods. ICPRAM 2019: 691-698 - [c21]Hlynur Davíð Hlynsson, Laurenz Wiskott:
Learning Gradient-Based ICA by Neurally Estimating Mutual Information. KI 2019: 182-187 - [i15]Hlynur Davíð Hlynsson, Laurenz Wiskott:
Learning gradient-based ICA by neurally estimating mutual information. CoRR abs/1904.09858 (2019) - [i14]Jan Melchior, Laurenz Wiskott:
Hebbian-Descent. CoRR abs/1905.10585 (2019) - [i13]Jan Melchior, Mehdi Bayati, Amir Hossein Azizi, Sen Cheng, Laurenz Wiskott:
A Hippocampus Model for Online One-Shot Storage of Pattern Sequences. CoRR abs/1905.12937 (2019) - [i12]Hlynur Davíð Hlynsson, Alberto N. Escalante-B., Laurenz Wiskott:
Measuring the Data Efficiency of Deep Learning Methods. CoRR abs/1907.02549 (2019) - [i11]Laurenz Wiskott, Fabian Schönfeld:
Laplacian Matrix for Dimensionality Reduction and Clustering. CoRR abs/1909.08381 (2019) - 2018
- [j41]Jing Fang, Naima Rüther, Christian Bellebaum, Laurenz Wiskott, Sen Cheng:
The Interaction between Semantic Representation and Episodic Memory. Neural Comput. 30(2) (2018) - [j40]Björn Weghenkel, Laurenz Wiskott:
Slowness as a Proxy for Temporal Predictability: An Empirical Comparison. Neural Comput. 30(5) (2018) - [c20]Jan Freiwald, Mahdie Karbasi, Steffen Zeiler, Jan Melchior, Varun Raj Kompella, Laurenz Wiskott, Dorothea Kolossa:
Utilizing Slow Feature Analysis for Lipreading. ITG Symposium on Speech Communication 2018: 1-5 - [i10]Stefan Richthofer, Laurenz Wiskott:
Global Navigation Using Predictable and Slow Feature Analysis in Multiroom Environments, Path Planning and Other Control Tasks. CoRR abs/1805.08565 (2018) - [i9]Merlin Schüler, Hlynur Davíð Hlynsson, Laurenz Wiskott:
Gradient-based Training of Slow Feature Analysis by Differentiable Approximate Whitening. CoRR abs/1808.08833 (2018) - 2017
- [j39]Björn Weghenkel, Asja Fischer, Laurenz Wiskott:
Graph-based predictable feature analysis. Mach. Learn. 106(9-10): 1359-1380 (2017) - [i8]Varun Raj Kompella, Laurenz Wiskott:
Intrinsically Motivated Acquisition of Modular Slow Features for Humanoids in Continuous and Non-Stationary Environments. CoRR abs/1701.04663 (2017) - [i7]Stefan Richthofer, Laurenz Wiskott:
PFAx: Predictable Feature Analysis to Perform Control. CoRR abs/1712.00634 (2017) - 2016
- [j38]Jan Melchior, Asja Fischer, Laurenz Wiskott:
How to Center Deep Boltzmann Machines. J. Mach. Learn. Res. 17: 99:1-99:61 (2016) - [j37]Alberto N. Escalante-B., Laurenz Wiskott:
Theoretical Analysis of the Optimal Free Responses of Graph-Based SFA for the Design of Training Graphs. J. Mach. Learn. Res. 17: 157:1-157:36 (2016) - [i6]Alberto N. Escalante-B., Laurenz Wiskott:
Improved graph-based SFA: Information preservation complements the slowness principle. CoRR abs/1601.03945 (2016) - [i5]Björn Weghenkel, Asja Fischer, Laurenz Wiskott:
Graph-based Predictable Feature Analysis. CoRR abs/1602.00554 (2016) - 2015
- [j36]Fabian Schönfeld, Laurenz Wiskott:
Modeling place field activity with hierarchical slow feature analysis. Frontiers Comput. Neurosci. 9: 51 (2015) - [j35]Torsten Neher, Sen Cheng, Laurenz Wiskott:
Memory Storage Fidelity in the Hippocampal Circuit: The Role of Subregions and Input Statistics. PLoS Comput. Biol. 11(5) (2015) - [c19]Stefan Richthofer, Laurenz Wiskott:
Predictable Feature Analysis. ICMLA 2015: 190-196 - [i4]Alberto N. Escalante-B., Laurenz Wiskott:
Theoretical Analysis of the Optimal Free Responses of Graph-Based SFA for the Design of Training Graphs. CoRR abs/1509.08329 (2015) - 2014
- [j34]Henning Sprekeler, Tiziano Zito, Laurenz Wiskott:
An extension of slow feature analysis for nonlinear blind source separation. J. Mach. Learn. Res. 15(1): 921-947 (2014) - [j33]Sven Dähne, Niko Wilbert, Laurenz Wiskott:
Slow Feature Analysis on Retinal Waves Leads to V1 Complex Cells. PLoS Comput. Biol. 10(5) (2014) - [j32]Laurenz Wiskott, Rolf P. Würtz, Günter Westphal:
Elastic Bunch Graph Matching. Scholarpedia 9(3): 10587 (2014) - [c18]Björn Weghenkel, Laurenz Wiskott:
Learning predictive partitions for continuous feature spaces. ESANN 2014 - [c17]Nan Wang, Laurenz Wiskott, Dirk Jancke:
Modeling correlations in spontaneous activity of visual cortex with centered Gaussian-binary deep Boltzmann machines. ICLR (Workshop Poster) 2014 - [r1]Laurenz Wiskott:
Slow Feature Analysis. Encyclopedia of Computational Neuroscience 2014 - [i3]Nan Wang, Jan Melchior, Laurenz Wiskott:
Gaussian-binary Restricted Boltzmann Machines on Modeling Natural Image Statistics. CoRR abs/1401.5900 (2014) - 2013
- [j31]Fabian Schönfeld, Laurenz Wiskott:
RatLab: an easy to use tool for place code simulations. Frontiers Comput. Neurosci. 7: 104 (2013) - [j30]Amir Hossein Azizi, Laurenz Wiskott, Sen Cheng:
A computational model for preplay in the hippocampus. Frontiers Comput. Neurosci. 7: 161 (2013) - [j29]Alberto N. Escalante, Laurenz Wiskott:
How to solve classification and regression problems on high-dimensional data with a supervised extension of slow feature analysis. J. Mach. Learn. Res. 14(1): 3683-3719 (2013) - [j28]Niko Wilbert, Tiziano Zito, Rike-Benjamin Schuppner, Zbigniew Jedrzejewski-Szmek, Laurenz Wiskott, Pietro Berkes:
Building extensible frameworks for data processing: The case of MDP, Modular toolkit for Data Processing. J. Comput. Sci. 4(5): 345-351 (2013) - [j27]Norbert Krüger, Peter Janssen, Sinan Kalkan, Markus Lappe, Ales Leonardis, Justus H. Piater, Antonio Jose Rodríguez-Sánchez, Laurenz Wiskott:
Deep Hierarchies in the Primate Visual Cortex: What Can We Learn for Computer Vision? IEEE Trans. Pattern Anal. Mach. Intell. 35(8): 1847-1871 (2013) - [j26]Ha Quang Minh, Laurenz Wiskott:
Multivariate Slow Feature Analysis and Decorrelation Filtering for Blind Source Separation. IEEE Trans. Image Process. 22(7): 2737-2750 (2013) - [i2]Jan Melchior, Asja Fischer, Nan Wang, Laurenz Wiskott:
How to Center Binary Restricted Boltzmann Machines. CoRR abs/1311.1354 (2013) - [i1]Stefan Richthofer, Laurenz Wiskott:
Predictable Feature Analysis. CoRR abs/1311.2503 (2013) - 2012
- [j25]Alberto N. Escalante, Laurenz Wiskott:
Slow Feature Analysis: Perspectives for Technical Applications of a Versatile Learning Algorithm. Künstliche Intell. 26(4): 341-348 (2012) - [c16]Nan Wang, Jan Melchior, Laurenz Wiskott:
An analysis of Gaussian-binary restricted Boltzmann machines for natural images. ESANN 2012 - 2011
- [j24]Henning Sprekeler, Laurenz Wiskott:
A Theory of Slow Feature Analysis for Transformation-Based Input Signals with an Application to Complex Cells. Neural Comput. 23(2): 303-335 (2011) - [j23]Mathias Franzius, Niko Wilbert, Laurenz Wiskott:
Invariant Object Recognition and Pose Estimation with Slow Feature Analysis. Neural Comput. 23(9): 2289-2323 (2011) - [j22]Peter A. Appleby, Gerd Kempermann, Laurenz Wiskott:
The Role of Additive Neurogenesis and Synaptic Plasticity in a Hippocampal Memory Model with Grid-Cell Like Input. PLoS Comput. Biol. 7(1) (2011) - [j21]Laurenz Wiskott, Pietro Berkes, Mathias Franzius, Henning Sprekeler, Niko Wilbert:
Slow feature analysis. Scholarpedia 6(4): 5282 (2011) - [c15]Ha Quang Minh, Laurenz Wiskott:
Slow feature analysis and decorrelation filtering for separating correlated sources. ICCV 2011: 866-873 - [c14]Alberto N. Escalante, Laurenz Wiskott:
Heuristic Evaluation of Expansions for Non-linear Hierarchical Slow Feature Analysis. ICMLA (1) 2011: 133-138 - 2010
- [j20]Robert Legenstein, Niko Wilbert, Laurenz Wiskott:
Reinforcement Learning on Slow Features of High-Dimensional Input Streams. PLoS Comput. Biol. 6(8) (2010) - [c13]Alberto N. Escalante, Laurenz Wiskott:
Gender and Age Estimation from Synthetic Face Images. IPMU 2010: 240-249
2000 – 2009
- 2008
- [j19]Tiziano Zito, Niko Wilbert, Laurenz Wiskott, Pietro Berkes:
Modular toolkit for Data Processing (MDP): a Python data processing framework. Frontiers Neuroinformatics 2: 8 (2008) - [c12]Mathias Franzius, Niko Wilbert, Laurenz Wiskott:
Invariant Object Recognition with Slow Feature Analysis. ICANN (1) 2008: 961-970 - 2007
- [j18]Mathias Franzius, Roland Vollgraf, Laurenz Wiskott:
From grids to places. J. Comput. Neurosci. 22(3): 297-299 (2007) - [j17]Tobias Blaschke, Tiziano Zito, Laurenz Wiskott:
Independent Slow Feature Analysis and Nonlinear Blind Source Separation. Neural Comput. 19(4): 994-1021 (2007) - [j16]Henning Sprekeler, Christian Michaelis, Laurenz Wiskott:
Slowness: An Objective for Spike-Timing-Dependent Plasticity? PLoS Comput. Biol. 3(6) (2007) - [j15]Mathias Franzius, Henning Sprekeler, Laurenz Wiskott:
Slowness and Sparseness Lead to Place, Head-Direction, and Spatial-View Cells. PLoS Comput. Biol. 3(8) (2007) - 2006
- [j14]Pietro Berkes, Laurenz Wiskott:
On the Analysis and Interpretation of Inhomogeneous Quadratic Forms as Receptive Fields. Neural Comput. 18(8): 1868-1895 (2006) - [j13]Tobias Blaschke, Pietro Berkes, Laurenz Wiskott:
What Is the Relation Between Slow Feature Analysis and Independent Component Analysis? Neural Comput. 18(10): 2495-2508 (2006) - 2004
- [j12]Tobias Blaschke, Laurenz Wiskott:
CuBICA: independent component analysis by simultaneous third- and fourth-order cumulant diagonalization. IEEE Trans. Signal Process. 52(5): 1250-1256 (2004) - [c11]Tobias Blaschke, Laurenz Wiskott:
Independent Slow Feature Analysis and Nonlinear Blind Source Separation. ICA 2004: 742-749 - [c10]Tobias Blaschke, Laurenz Wiskott:
Nonlinear Blind Source Separation by Integrating Independent Component Analysis and Slow Feature Analysis. NIPS 2004: 177-184 - 2003
- [j11]Laurenz Wiskott:
Slow Feature Analysis: A Theoretical Analysis of Optimal Free Responses. Neural Comput. 15(9): 2147-2177 (2003) - 2002
- [j10]Laurenz Wiskott, Terrence J. Sejnowski:
Slow Feature Analysis: Unsupervised Learning of Invariances. Neural Comput. 14(4): 715-770 (2002) - [c9]Pietro Berkes, Laurenz Wiskott:
Applying Slow Feature Analysis to Image Sequences Yields a Rich Repertoire of Complex Cell Properties. ICANN 2002: 81-86 - [c8]Tobias Blaschke, Laurenz Wiskott:
An Improved Cumulant Based Method for Independent Component Analysis. ICANN 2002: 1087-1093
1990 – 1999
- 1999
- [j9]Laurenz Wiskott:
Learning invariance manifolds. Neurocomputing 26-27: 925-932 (1999) - [j8]Laurenz Wiskott:
Segmentation from motion: combining Gabor- and Mallat-wavelets to overcome the aperture and correspondence problems. Pattern Recognit. 32(10): 1751-1766 (1999) - [j7]Laurenz Wiskott:
The role of topographical constraints in face recognition. Pattern Recognit. Lett. 20(1): 89-96 (1999) - 1998
- [j6]Laurenz Wiskott, Terrence J. Sejnowski:
Constrained Optimization for Neural Map Formation: A Unifying Framework for Weight Growth and Normalization. Neural Comput. 10(3): 671-716 (1998) - 1997
- [j5]Laurenz Wiskott, Jean-Marc Fellous, Norbert Krüger, Christoph von der Malsburg:
Face Recognition by Elastic Bunch Graph Matching. IEEE Trans. Pattern Anal. Mach. Intell. 19(7): 775-779 (1997) - [j4]Laurenz Wiskott:
Phantom faces for face analysis. Pattern Recognit. 30(6): 837-846 (1997) - [c7]Laurenz Wiskott:
Segmentation from Motion: Combining Gabor- and Mallat-Wavelets to Overcome Aperture and Correspondence Problem. CAIP 1997: 329-336 - [c6]Laurenz Wiskott, Jean-Marc Fellous, Norbert Krüger, Christoph von der Malsburg:
Face Recognition by Elastic Bunch Graph Matching. CAIP 1997: 456-463 - [c5]Laurenz Wiskott:
Phantom Faces for Face Analysis. CAIP 1997: 480-487 - [c4]Laurenz Wiskott, Terrence J. Sejnowski:
Objective Functions for Neural Map Formation. ICANN 1997: 243-248 - [c3]Laurenz Wiskott, Jean-Marc Fellous, Norbert Krüger, Christoph von der Malsburg:
Face Recognition by Elastic Bunch Graph Matching. ICIP (1) 1997: 129-132 - [c2]Laurenz Wiskott:
Phantom faces for face analysis. ICIP (3) 1997: 308-311 - 1996
- [j3]Laurenz Wiskott, Christoph von der Malsburg:
Recognizing Faces by Dynamic Link Matching. NeuroImage 4(3): S14-S18 (1996) - [c1]