Machine Learning

NOTE: This page is not up to date. Please visit our website at the University of Oldenburg for a list of more recent publications.

Peer Reviewed Original Papers (Journals and Conferences)

Z. Dai, G. Exarchakis, and J. Lücke (2013)
What Are the Invariant Occlusive Components of Image Patches? A Probabilistic Generative Approach
Advances in Neural Information Processing Systems 26, in press

J. Bornschein, M. Henniges and J. Lücke (2013)
Are V1 simple cells optimized for visual occlusions? A comparative study.(online access, bibtex)
PLoS Computational Biology 9(6): e1003062.

J. A. Shelton, P. Sterne, J. Bornschein, A.-S. Sheikh,  and J. Lücke (2012).
Why MCA? Nonlinear sparse coding with spike-and-slab prior for neurally plausible image encoding.
Advances in Neural Information Processing Systems 25, 2285-2293.

Z. Dai and J. Lücke (2012)
Autonomous Cleaning of Corrupted Scanned Documents - A Generative Modeling Approach (pdf, bibtex)
Proc. IEEE Computer Vision and Pattern Recognition (CVPR), 3338-3345, oral presentation.
(highest CVPR 2012 reviewer score and Google Student Travel Award)

Z. Dai and J. Lücke (2012)
Unsupervised Learning of Translation Invariant Occlusive Components (pdf, bibtex)
Proc. IEEE Computer Vision and Pattern Recognition (CVPR), 2400-2407.

C. Keck*, C. Savin*, and J. Lücke (2012).
Feedforward Inhibition and Synaptic Scaling – Two Sides of the Same Coin? (online access, bibtex)
PLoS Computational Biology 8(3): e1002432.
*joint first authorshipG.

Exarchakis, M. Henniges, J. Eggert, and J. Lücke (2012).
Ternary Sparse Coding (bibtex).
International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA), 204-212, 2012.

J. Lücke* and A.-S. Sheikh* (2012).
A Closed-Form EM Algorithm for Sparse Coding and Its Application to Source Separation (arXiv version, bibtex, code).
International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA), 213-221, 2012.
*joint first authorship

J. Lücke and M. Henniges (2012).
Closed-Form Entropy Limits – A Tool to Monitor Likelihood Optimization of Probabilistic Generative Models (pdf, bibtex).
AI & Statistics (AISTATS 15), 731-740, 2012.

J. A. Shelton, J. Bornschein, A.-S. Sheikh, P. Berkes, and J. Lücke (2011).
Select and Sample — A Model of Efficient Neural Inference and Learning (pdfbibtex).
Advances in Neural Information Processing Systems 24, 2618-2626, 2011.

G. Puertas*, J. Bornschein*, and J. Lücke (2010). 
The Maximal Causes of Natural Scenes are Edge Filters (pdfbibtexsupplement, code)
Advances in Neural Information Processing Systems 23, 1939-1947, 2010.
*joint first authorship

J. Lücke and J. Eggert (2010). 
Expectation Truncation and the Benefits of Preselection in Training Generative Models. (pdfbibtexanimationstalk). 
Journal of Machine Learning Research 11:2855-2900, 2010.

M. Henniges, G. Puertas, J. Bornschein, J. Eggert, and J. Lücke (2010). 
Binary Sparse Coding (pdf, bibtex, code). 
Proc. LVA/ICA 2010, LNCS 6365, 450-457

C. Keck and J. Lücke (2010). 
Learning of Lateral Connections for Representational Invariant Recognition (pdfbibtex). 
Proc. ICANN 2010, LNCS 6354, 21-30.

J. Lücke, R. Turner, M. Sahani, and M. Henniges (2009). 
Occlusive Components Analysis (pdfbibtexsupplementary). 
Advances in Neural Information Processing Systems 22, 1069-1077.

J. Lücke (2009). 
Receptive Field Self-Organization in a Model of the Fine-Structure in V1 Cortical Columns (online accessbibtex). 
Neural Computation, 21(10):2805-2845.

C. Möller, N. Arai, J. Lücke, and U. Ziemann (2009). 
Hysteresis Effects on the Input-Output Curve of Motor Evoked Potentials (pdfbibtex). 
Clinical Neurophysiology 120(5):1003--1008.

J. D. Bouecke and J. Lücke (2008). 
Learning of Neural Information Routing for Correspondence Finding ( bibtex). 
Proc. ICANN, Springer, LNCS 5164, 557-566.

J. Lücke and M. Sahani (2008). 
Maximal Causes for Non-linear Component Extraction (pdfbibtex). 
Journal of Machine Learning Research 9:1227-1267. 

P. Wolfrum, C. Wolff, J. Lücke, and C. von der Malsburg (2008). 
A Recurrent Dynamic Model for Correspondence-Based Face Recognition (pdfbibtex). 
Journal of Vision 8(7):34, 1-18. 

J. Lücke, C. Keck, and C. von der Malsburg (2008). 
Rapid Convergence to Feature Layer Correspondences. (preprintbibtexdoi). 
Neural Computation 20(10):2441-2463. 

J. Lücke and M. Sahani (2007). 
Generalized Softmax Networks for Non-Linear Component Extraction (bibtex). 
Proc. ICANN, Springer, LNCS 4668, 657-667. 

J. Lücke (2007). 
A Dynamical Model for Receptive Field Self-Organization in V1 Cortical Columns (bibtex). 
Proc. ICANN, Springer, LNCS 4669, 389-398. 

C. Möller, J. Lücke, J. Zhu, P. M. Faustmann, and C. von der Malsburg (2007). 
Glial Cells for Information Routing? (pdf, bibtex). 
Cognitive Systems Research 8:28-35. 

J. Lücke and C. von der Malsburg (2006). 
Rapid Correspondence Finding in Networks of Cortical Columns (pdfbibtex). 
Proc. ICANN, Springer, LNCS 4131, 668-677. 

J. Lücke and J. D. Bouecke (2005). 
Dynamics of Cortical Columns - Self-Organization of Receptive Fields. (pdfbibtex). 
Proc. ICANN, Springer, LNCS 3696, 31-37. 

J. Lücke (2005). 
Dynamics of Cortical Columns - Sensitive Decision Making. (pdfbibtex). 
Proc. ICANN, Springer, LNCS 3696, 25-30. 

J. Lücke (2004). 
Hierarchical self-organization of minicolumnar receptive fields (pdfbibtex). 
Neural Networks 17:1377-1389. 

J. Lücke (2004). 
Clustering with minicolumnar receptive field self-organization (pdfbibtex). 
Proc. IJCNN, IEEE/Omnipress, 3113-3118.

J. Lücke and C. von der Malsburg (2004). 
Rapid processing and unsupervised learning in a model of the cortical macrocolumn (pdfbibtex). 
Neural Computation, 16(3):501-533. 

J. Lücke, C. von der Malsburg, and R. P. Würtz (2002). 
Macrocolumns as Decision Units (pdfbibtex). 
Proc. ICANN, Springer, LNCS 2415, 57-62. 

J. Lücke (2001). 
Hilberticus - a Tool Deciding an Elementary Sublanguage of Set Theory (pdfbibtex). 
Proc. IJCAR, Springer, LNCS/LNAI 2083, 690-695.

Books

J. Lücke (2005). 
Information Processing and Learning in Networks of Cortical Columns (contentslink to publisherbibtexpdf). 
Shaker Verlag, ISBN 3-8322-3966-9, Dissertation. (For related work, please see PDFs above.) 

Conference Abstracts

Z. Dai, J. Shelton, J. Bornschein,  A.-S. Sheikh, and J. Lücke (2011). 
Combining approximate inference methods for efficient learning on large computer clusters (abstractposter). 
Extended Abstract, NIPS Workshop: Big Learning.

J. Bornschein, M. Henniges, G. Puertas, and J. Lücke (2011).
Sparse codes of V1 simple-cells and the emergence of globular receptive fields – a comparative study
Proc. COSYNE. (abstractposter

C. Keck, C. Savin and J. Lücke (2011).
Input normalization and synaptic scaling - two sides of the same coin (abstractposter)
Proc. COSYNE. 

Z. Dai, and J. Lücke (2010).  
A Probabilistic Generative Approach to Invariant Visual Inference andLearning  
Frontiers Comp Neurosci, Proceedings BCCN. (abstractposteronline access

J. Bornschein, Z. Dai, and J. Lücke (2010).  
Approximate EM Learning on Large Computer Clusters (extended abstractposter
Extended Abstract, NIPS Workshop: Learning on Cores, Clusters and Clouds. 

J. Bornschein, M. Henniges, G. Puertas, J. Lücke (2010)  
Binary Hidden Variables and Sparse Sensory Coding  
Frontiers Comp Neurosci, Proceedings BCCN. (online access)

J. Bornschein and J. Lücke (2009). 
Applications of Non-linear Component Extraction to Spectrogram Representations Of Auditory Data 
Frontiers in Compuational Neuroscience, Proc. BCCN (posteronline access). 

C. Keck, J. D. Bouecke, and J. Lücke (2009). 
Learning of Lateral Connections for Representational Invariant Recognition 
Frontiers in Compuational Neuroscience, Proc. BCCN (online access).

C. Möller, N. Arai, J. Lücke, and U. Ziemann (2009). 
Hysteresis Effects of Cortico-Spinal Excitability During TMS Stimulation 
Frontiers in Compuational Neuroscience, Proc. BCCN (online access).

J. Lücke and M. Sahani (2007). 
Learning in a Generative Model with Competitive Combination Is Approximated by (Soft-)WTA Networks 
Proc. COSYNE.

J. Lücke, C. Keck, P. Wolfrum, C. Wolff, J. D. Bouecke, and C. von der Malsburg (2007). 
Neural Feature Layers Can Establish Correspondences In Physiological Time 
Proc. COSYNE.

J. Lücke (2006). 
Learning of Representations in a Canonical Model of Cortical Columns 
Proc. COSYNE.

J. Lücke (2005). 
Dynamics of Cortical Macrocolumns - An Abstract Derivation. 
Proc. COSYNE

Didactic Tools

J. Lücke , J. D. Bouecke, M. Benzke, A. Dimek, A. Mitchkowski and S. Muras (2007).
Homepage of the Hilberticus Project: www.hilberticus.org
Related publication: Hilberticus - a Tool Deciding an Elementary Sublanguage of Set Theory (pdf)

Diploma Thesis

J. Lücke, (1999).
Applications of Non-commutative Geometry to the Theory of Elementary Particles.
Library of the Centre de Physique Théorique, CNRS, Marseille-Luminy, France .

 

Copyright notice

The papers listed on the left have been published after peer review in different journals. These journals remain the only definitive repository of the content. Copyright and all rights therein are usually retained by the respective publishers. These materials may not be copied or reposted without their explicit permission. Use for scholarly purposes only.