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)
(The complete list can be found here.)
A. S. Sheikh*, N. S. Harper*, J. Drefs, Y. Singer, Z. Dai, R.E. Turner and J. Lücke (2019).
STRFs in primary auditory cortex emerge from masking-based statistics of natural sounds.
PLOS Computational Biology 15(1): e1006595 (online access, bibtex)
*joint first authorship.
D. Forster and J. Lücke (2018).
Can clustering scale sublinearly with its clusters? A variational EM acceleration of GMMs and k-means.
International Conference on Artificial Intelligence and Statistics (AISTATS), in press (online access)
R. Holca-Lamarre, J. Lücke* and K. Obermayer* (2017).
Models of Acetylcholine and Dopamine Signals Differentially Improve Neural Representations.
Frontiers in Computational Neuroscience, 11:54 (online access, bibtex)
*joint senior authorship.
T. Monk, C. Savin and J. Lücke (2016).
Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics.
Advances in Neural Information Processing Systems (NIPS), 29: 4278-4286. (online access, bibtex)
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)