The website of the Laboratory for Artificial Intelligence is currently being migrated.
Below you can find a selection of publications from recent years.

A full list of all publications is currently available here.

A selection of papers of the last three years is the following:

H. Mousavi, J. Lücke (2025).
Linear and Nonlinear Generative Models for 'Zero-Shot'Image Denoising in the Limit of Few Photons.
Journal of Mathematical Imaging and Vision 67(3): 1-17. (online access, bibtex)

S.Salwig*, J. Drefs* and J. Lücke (2024).
Zero-shot denoising of microscopy images recorded at high-resolution limits.
PLOS Computational Biology 20(6): e1012192 (online access, bibtex)
*joint first authorship.

D. Velychko, S. Damm, Z. Dai, A. Fischer and J. Lücke (2024).
Learning Sparse Codes with Entropy-Based ELBOs.
Int. Conf. on Artificial Intelligence and Statistics (AISTATS), 2089-2097, 2024. (online access, bibtex)

H. Mousavi, J. Drefs, F. Hirschberger, J. Lücke (2023).
Generic Unsupervised Optimization for a Latent Variable Model with Exponential Family Observables.
Journal of Machine Learning Research 24(285):1−59. (online access, bibtex)

S. Damm*, D. Forster, D. Velychko, Z. Dai, A. Fischer and J. Lücke* (2023).
The ELBO of Variational Autoencoders Converges to a Sum of Entropies.
Int. Conf. on Artificial Intelligence and Statistics (AISTATS), 3931-3960, 2023. (online access, bibtex).
*joint main contributions

J. Drefs*, E. Guiraud*, F. Panagiotou, J. Lücke (2023).
Direct Evolutionary Optimization of Variational Autoencoders With Binary Latents.
European Conference on Machine Learning, 357-372. (pdf, bibtex).
*joint first authorship

F. Hirschberger*, D. Forster* and J. Lücke (2022).
A Variational EM Acceleration for Efficient Clustering at Very Large Scales.
IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12):9787-9801 (online access, bibtex).
*joint first authorship.

J. Drefs, E. Guiraud and J. Lücke (2022).
Evolutionary Variational Optimization of Generative Models.
Journal of Machine Learning Research 23(21):1-51 (online access, bibtex).

Selected other papers:

J. Lücke and D. Forster (2019).
k-means as a variational EM approximation of Gaussian mixture models.
Pattern Recognition Letters 125:349-356 (online access, bibtex, arXiv).

F. Hutter*, J. Lücke*, L. Schmidt-Thieme* (2015).
Beyond manual tuning of hyperparameters.
KI - Künstliche Intelligenz 29 (4), 329-337.
*alphabetical order

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)

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.
The Journal of Machine Learning Research 15 (1), 2689-2722.

J Lücke, J Eggert (2010).
Expectation truncation and the benefits of preselection in training generative models.
The Journal of Machine Learning Research 11, 2855-2900.

J. Lücke, C. von der Malsburg (2004).
Rapid processing and unsupervised learning in a model of the cortical macrocolumn.
Neural Computation 16 (3), 501-533.