Component Extraction Algorithms
3 July 2020
We are looking for two new doctoral coworkers (PhD students) for our lab. Please see advertisement of the positions here.
30 June 2020
We have been granted access to large amounts of computing resources by the Northern German Network for HPC Computing (HLRN).
24 June 2020
Our paper on learning of higher-order statistics for image encoding (Mousavi, Drefs, Lücke, 2020) has been accepted by the International Conference on Machine Learning, Optimization, and Data Science.
20 May 2020
We have received top-up funding for the processing of SARS-CoV-2 EM microscopy images within our BMBF project SPAplus.
1 April 2020
Our project "SPAplus" (collaborative BMBF project, 3 years) has started. We will investigate medical image processing using generative models.
25 March 2020
Our paper "Phase transition for parameter learning of Hidden Markov Models" (Rau et al.) has been made available on arXiv.
15 March 2020
Our research activities shift to home offices due to the Corona crisis
4 March 2020
Our paper "Maximal Causes for Exponential Family Observables" (Mousavi et al.) has been made available on arXiv.
18 Feb 2020
Our abstract "Optimal Inference of Sound Intensities and Sound Components Using Generative Representations" (Monk, Savin, Lücke) has been accepted for the ASA Conference in Chicago, where it will be presented as a talk.
6 Feb 2020
Our project proposal "SPAplus" (collaborative BMBF project, 3 years) has been accepted and will be funded.
1 August 2019
Release of the open source software library "ProSper". The library contains a collection of algorithms for probabilistic sparse coding. For the source code see here. For a description see here.
2 April 2019
Our paper "k-Means as a Variational EM Approximation of Gaussian Mixture Models" was accepted for publication by Pattern Recognition Letters.
14-15 March 2019
Jörg Lücke gave a series of three lectures on Generative Machine Learning at the IK 2019 Spring School.
17 Jan 2019
Our paper "STRFs in primary auditory cortex emerge from masking-based statistics of natural sounds" (Sheikh et al.) has been published by PLOS Computational Biology.
16 July 2018
Our paper "Neural Simpletrons - Learning in the Limit of Few Labels with Directed Generative Networks" (Forster et al.) has been published by Neural Computation.
5 July 2018
Our paper "Truncated Variational Sampling for ‘Black Box’ Optimization of Generative Models" has been presented at the LVA/ICA 2018.
3 July 2018
Our paper "Optimal neural inference of stimulus intensities" (Monk et al.) has been published by Nature's Scientific Reports.
24 March 2018
Our paper "Evolutionary Expectation Maximization" (Guiraud et al.) has been accepted for GECCO 2018.
19 March 2018
Our paper "Truncated Variational Sampling for ‘Black Box’ Optimization of Generative Models" (Lücke et al.) has been accepted for LVA/ICA 2018.
5 March 2018
Our paper "Neural Simpletrons - Learning in the Limit of Few Labels with Directed Generative Networks" (Forster et al.) has been accepted by Neural Computation.
22 Dec 2017
Our paper "Can clustering scale sublinearly with its clusters?" (Forster & Lücke) has been accepted for AISTATS 2018.
30 June 2017
Our paper "Discrete Sparse Coding" (Exarchakis & Lücke) has been accepted by Neural Computation.
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.
Head of lab
Prof. Dr. Jörg Lücke
Arbeitsgruppe Machine Learning
Exzellenzcluster Hearing4all und
Department für Medizinische Physik und Akustik
Fakultät für Medizin und Gesundheitswissenschaften
Carl von Ossietzky Universität Oldenburg
Room 201 (2nd floor)
Building W30 (NeSSy)
(Click images to enlarge)
In this research area we study probabilistic systems that autonomously learn from data and are able to recognize a complex data point as a combination of its components. The project builds up on component extraction approaches such as principle component analysis (PCA), independent component analysis (ICA), sparse coding, and non-negative matrix factorization (NMF). All these approaches assume linear superposition of components, which is a valid assumption in only a limited range of cases (e.g., for sound waveforms). In vision, as well as in other modalities, components interact non-linearly. This project therefore focuses on probabilistic generative models that combine components non-linearly. Furthermore, we advance the prior assumptions of standard approaches and aim at inferring prior structure. Research includes (1) the derivation and investigation of algorithms for non-linear generative models, (2) the development and application of approximation schemes that allow to train such models, and (3) the development of hierarchical extensions of non-linear models and models with structured priors.
Python based source code of the 'Binary Sparse Coding (BSC)', 'Maximal Causes Analysis (MCA)' and 'Spike-and-slab/Gaussian Sparse Coding (GSC)' algorithms are now available for download. The software packages implement MPI based parallelization and can be readily run on a single or multi-core/parallel architecture. Please see the corresponding 'Readme' files for more details about individual packages.
All the packages are available under the Academic Free License (AFL) v3.0.
J. Lücke* and A.-S. Sheikh* (2012).
A Closed-Form EM Algorithm for Sparse Coding and Its Application to Source Separation (arXiv version, bibtex).
International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA), 213-221, 2012.
*joint first authorship
G. Puertas*, J. Bornschein*, and J. Lücke (2010).
The Maximal Causes of Natural Scenes are Edge Filters (pdf, bibtex, supplement).
Advances in Neural Information Processing Systems 23, 1939-1947, 2010.
*joint first authorship
- J.A. Shelton, A.-S. Sheikh, J. Bornschein, P. Sterne and J. Lücke (2015).
Nonlinear Spike-and-slab Sparse Coding for Interpretable Image Encoding
PLoS One 10(5): e0124088 (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)
- G. 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 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 (pdf, bibtex).
Advances in Neural Information Processing Systems 24, 2618-2626, 2011.
- 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. (abstract, poster)
- J. Lücke and J. Eggert (2010).
Expectation Truncation and the Benefits of Preselection in Training Generative Models. (pdf, bibtex, animations, talk).
Journal of Machine Learning Research 11:2855-2900, 2010.
- J. Bornschein and J. Lücke (2009).
Applications of Non-linear Component Extraction to Spectrogram Representations Of Auditory Data
Frontiers in Compuational Neuroscience, Proc. BCCN (online access).
- J. Lücke, R. Turner, M. Sahani, and M. Henniges (2009).
Occlusive Components Analysis (pdf, bibtex, supplementary).
Advances in Neural Information Processing Systems 22, 1069-1077.
- J. Lücke and M. Sahani (2008).
Maximal Causes for Non-linear Component Extraction (pdf, bibtex).
Journal of Machine Learning Research 9:1227-1267.
- J. Lücke and M. Sahani (2007).
Generalized Softmax Networks for Non-Linear Component Extraction (bibtex).
Proc. ICANN, Springer, LNCS 4668, 657-667.
The papers listed above 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.