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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

GPU-Cluster Oldenburg (GOLD)

The GPU-Cluster Oldenburg (GOLD) is a high-performance compute cluster dedicated for highly parallel and short latency computation. To realize optimal performance, GOLD is equipped with 30 state-of-the-art GPUs (14x NVIDIA Geforce Titan Black, 11x NVIDIA Geforce Titan X, 2x NVIDIA Geforce Titan X Pascal, 3x NVIDIA Tesla P100 16GB) with in total 89216 CUDA cores. These are complemented by 144 CPU cores (Intel Xeon). The currently eleven compute nodes of GOLD have each 128-256 GB on board DDR3/DDR4 memory and are Infiband connected.

The cluster was initiated by the Machine Learning group and is currently used, run, and funded by the following groups

  • Machine Learning, Dept of Medical Physics and Acoustics (Prof. Dr. Jörg Lücke)
  • Automatic Speech and Audio Processing, Dept of Medical Physics and Acoustics (Dr. Bernd T. Meyer)
  • Computational Theoretical Physics, Institute of Physics (Prof. Dr. Alexander Hartmann)
  • Acoustics, Dept of Medical Physics and Acoustics (Prof. Dr. Steven van de Par)

All groups gratefully acknowledge support of the GOLD cluster by the IT Services of the University of Oldenburg.

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