Machine Learning

Our Research

Our group develops theoretical models and practical technology for the processing of sensory data. We pursue and conduct projects on non-linear dictionary learning, large-scale unsupervised and semi-supervised learning, and inference and learning in neural circuits. Application domains of our learning and inference technology are acoustic data, visual data, and medical data.

We are part of the cluster of excellence Hearing4all and the Department of Medical Physics and Acoustics at the School of Medicine and Health Sciences.

The group was previously located at the TU Berlin and at the Goethe-University Frankfurt am Main. This website is currently under construction. For more details on our research please refer, for now, to our previous website at Frankfurt.

For any inquiries please contact Jörg Lücke.

Selected Recent Publications (Journals)

Sheikh, A.-S., Lücke, J. (2016).
Select-and-Sample for Spike-and-Slab Sparse Coding.
Advances in Neural Information Processing Systems (NIPS), 29: 3927-3935. (online accessbibtex)

Monk, T., Savin, C., and Lücke, J. (2016).
Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics.
Advances in Neural Information Processing Systems (NIPS), 29: 4278-4286. (online accessbibtex)

J.A. Shelton, A.-S. Sheikh, J. Bornschein, P. Sterne, J. Lücke (2015).
Nonlinear Spike-and-slab Sparse Coding for Interpretable Image Encoding
PLoS One 10(5): e0124088 (online accessbibtex)

Z. Dai and J. Lücke (2014).
Autonomous Document Cleaning – A Generative Approach to Reconstruct Strongly Corrupted Scanned Texts.
IEEE Transactions on Pattern Analysis and Machine Intelligence 36(10): 1950-1962. (online access, bibtex)

A.-S. Sheikh, J. A. Shelton, J. Lücke (2014).
A Truncated EM Approach for Spike-and-Slab Sparse Coding.
Journal of Machine Learning Research, 15:2653-2687. (online access, bibtex)

M. Henniges, R. E. Turner, M. Sahani, J. Eggert, J. Lücke (2014).
Efficient Occlusive Components Analysis.
Journal of Machine Learning Research, 15:2689-2722. (online access, bibtex)

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

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

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

Selected Recent Publications (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, 243-251. (online access, bibtex)

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. (online access, bibtex)

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

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

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.
Advances in Neural Information Processing Systems 24, 2618-2626. (pdfbibtex).

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

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

COPYRIGHT NOTICE

The papers listed above have been published after peer review in different journals or conference proceedings. These journals or proceedings 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.

 

News

  • 12 July 2017
    A new research position for a doctoral student has become available (see here).

  • 11 July 2017
    The Machine Learning group seeks a new student assistant to administrate our local IT system (see here).

  • 7 June 2017
    Our paper "Models of acetylcholine and dopamine signals differentially improve neural representations" (Holca-Lamarre et al.) has been accepted by the journal Frontiers in Neuroscience.

  • 25 May 2017
    Our paper "Binary non-negative matrix deconvolution for audio dictionary learning" (Drgas et al.) has been accepted by the journal IEEE Transactions on Audio, Speech and Language Processing.

  • 8 Mar 2017
    Our paper "GP-select: Accelerating EM using adaptive subspace preselection" (Shelton et al.) has been accepted for publication by Neural Computation.

  • 4 Feb 2017
    Our paper "Truncated Variational EM for Semi-Supervised Neural Simpletrons" (Forster & Lücke) has been accepted for the IJCNN 2017 conference.

  • 9 Dec 2016
    Jörg Lücke gave the talk "Probabilistic Inference and the Brain: Towards General, Scalable, and Deep Approximations" at the NIPS Workshop "Brains and Bits"

  • 9 Dec 2016
    We presented three posters about our research at the NIPS Workshop "Brains and Bits"

  • 8 Dec 2016
    Our Paper ""Select-and-Sample for Spike-and-Slab Sparse Coding." (Sheikh & Lücke) was presented at the NIPS 2016 main conference.

  • 5 Dec 2016
    Our Paper "Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics." (Monk et al.) was presented at the NIPS 2016 main conference.

  • 1 Dec 2016
    Georgios Exarchakis successfully defended his PhD Thesis. Congratulations!

  • 16 Nov 2016
    Enrico Guiraud starts his joint PhD project with our group and CERN (his main work place).

  • 14 Nov 2016
    Abdul-Saboor Sheikh successfully defended his PhD Thesis. Congratulations!

  • 12 Aug 2016
    Our paper "Select-and-Sample for Spike-and-Slab Sparse Coding." (Sheikh & Lücke) is accepted for the NIPS 2016 main conference.

  • 12 Aug 2016
    Our paper "Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics." (Monk et al.) is accepted for the NIPS 2016 main conference.

  • 10-11 Aug 2016
    The international workshop "New Algorithms and Tools for Probabilistic Machine Learning" takes place at the Machine Learning Lab at Oldenburg University. Thanks to all developers and contributors!

  • 8-9 Aug 2016
    The international symposium "Advanced Machine Learning Methods for Pattern Recognition and Sensory Data Analysis" takes place in the NeSSy research building of Oldenburg University. Thanks to all speakers and participants!

  • 5 July 2016
    The paper "Speaker Tracking for Hearing Aids" (Thiemann et al.) is accepted for the IEEE International Workshop on Machine Learning for Signal Processing.

  • 2 June 2016
    Joanna Luberadzka has successfully defended her Master thesis in Hearing Technology and Audiology. Congratulations!

  • 16 May 2016
    Hamid Mousavi (MSc in Optimization) has joined the group as a PhD student.

  • 27 Feb 2016
    Our abstract by Savin et al. about optimal coding and intrinsic plasticity is presented at the COSYNE conference.

  • 9 Oct 2015
    A new research position for a doctoral student has become available.