Head of lab
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.
Based on first theoretical principles, our group develops novel efficient learning algorithms for standard and novel data models. The resulting algorithms are applied to a range of different domains including acoustic data, visual data, medical data and data of general pattern recognition tasks. Alongside the theoretical and practical algorithm development, we investigate advanced Machine Learning methods as models for neural information processing; and, visa versa, use ideas and insights from the neurosciences to motivate novel research directions in Machine Learning.
We pursue and conduct projects on efficient generative models (including deep generative models) for large-scale unsupervised and semi-supervised learning, autonomous learning and data enhancement.
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.
For any inquiries please contact Jörg Lücke.
(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), 84:124-132. (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)