L. Theis, P. Berens, E. Froudarakis, J. Reimer, M. Roman-Roson, T. Baden, T. Euler, A. S. Tolias, and M. Bethge. Benchmarking spike rate inference in population calcium imaging. Neuron, volume 90, issue 3, pages 471-482, May 2016.

L. Theis, A. van den Oord, and M. Bethge. A note on the evaluation of generative models. International Conference on Learning Representations, April 2016.

L. Theis and M. Bethge. Generative Image Modeling Using Spatial LSTMs. Advances in Neural Information Processing Systems 28, December 2015.

M. Kümmerer, L. Theis, and M. Bethge. Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet. ICLR Workshop, June 2015.

H. E. Gerhard, L. Theis, and M. Bethge. Modeling Natural Image Statistics. Biologically-inspired Computer Vision—Fundamentals and Applications, Wiley VCH, 2015.

S. Sra, R. Hosseini, L. Theis, and M. Bethge. Data modeling with the elliptical gamma distribution. Artificial Intelligence and Statistics, volume 18, 2015.

A. M. Chagas, L. Theis, B. Sengupta, M. Stüttgen, M. Bethge, and C. Schwarz. Functional analysis of ultra high information rates conveyed by rat vibrissal primary afferents. Frontiers in Neural Circuits, volume 7, issue 190, December 2013.

L. Theis, A. M. Chagas, D. Arnstein, C. Schwarz, and M. Bethge. Beyond GLMs: A Generative Mixture Modeling Approach to Neural System Identification. PLoS Computational Biology, volume 9, issue 11, November 2013.

L. Theis, J. Sohl-Dickstein, and M. Bethge. Training sparse natural image models with a fast Gibbs sampler of an extended state space. Advances in Neural Information Processing Systems 25, November 2012.

L. Theis, R. Hosseini, and M. Bethge. Mixtures of Conditional Gaussian Scale Mixtures Applied to Multiscale Image Representations. PLoS ONE, Public Library of Science, volume 7, issue 7, July 2012.

L. Theis, S. Gerwinn, F. Sinz, and M. Bethge. In All Likelihood, Deep Belief Is Not Enough. Journal of Machine Learning Research, volume 12, November 2011.