Navigation

Skiplinks

News

6 June 2019
Our paper "k-Means as a Variational EM Approximation of Gaussian Mixture Models" has been published by Pattern Recognition Letters and is available online (PRLetters, arXiv).

 

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.
 

Contact

Head of lab

Prof. Dr. Jörg Lücke

+49 441 798 5486

+49 441 798-3902

W30 2-201

 

Secretary

tba

+49 441 798-

+49 441 798-3902

W30 2-202

 

Postal Address

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
D-26111 Oldenburg

Office Address

Room 201 (2nd floor)
Building W30 (NeSSy)  
Küpkersweg 74
26129 Oldenburg

Software is available for three areas of research:


Large-scale clustering

Different algorithms using variational acceleration are available. The algorithms are basically applicable to any data where k-means is expected to work well. Results of k-means are usually improved upon in quality, speed or both. Improvements in quality can especially be expected for data with large overlap. Improvements in speed will be observed especially for large scale data, i.e., data with large data spaces, many data points and many clusters. In these cases speedups can be one or two orders of magnitude.

 

Probabilistic Sparse Coding

Sparse Coding algorithms aim at extracting the elementary constituents of data. We make available a range of algorithms which assume binary, discrete or a combination of binary-continuous constituents for data generation. As customary, the data is assumed continuous.

 

Semi-Supervised Learning

We offer an algorithm based on a generative neural network which allows for learning from large datasets with few labels. The aim is to learn as high quality classifiers from few labels as possible. Typical application domains of the approach are hand-written symbols (MNIST, NIST etc) and text document classification.

Websck3ima9h2lsterlfu (petr5eja.wilts@uol.n/j9bdefqva) (Changed: 2019-08-16)