Publications by L. Theis

Journal Articles


R. Hosseini, S. Sra, L. Theis, and M. Bethge
Inference and Mixture Modeling with the Elliptical Gamma Distribution
Computational Statistics and Data Analysis, 101, 29-43, 2016
URL, DOI, BibTex
L. Theis, P. Berens, E. Froudarakis, J. Reimer, M. Roman-Roson, T. Baden, T. Euler, A. S. Tolias, et al.
Benchmarking spike rate inference in population calcium imaging
Neuron, 90(3), 471-482, 2016
#two-photon imaging, #spiking neurons
Code, URL, DOI, BibTex
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, 7(190), 2013
URL, PDF, BibTex
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, 9(11), 2013
#generalized linear model, #spiking neurons, #mixture models
Code, URL, DOI, PDF, BibTex
L. Theis, R. Hosseini, and M. Bethge
Mixtures of Conditional Gaussian Scale Mixtures Applied to Multiscale Image Representations
PLoS ONE, 7(7), 2012
#natural image statistics, #gaussian scale mixtures, #random fields, #mcgsm
Code, DOI, PDF, BibTex
L. Theis, S. Gerwinn, F. Sinz, and M. Bethge
In All Likelihood, Deep Belief Is Not Enough
Journal of Machine Learning Research, 12, 3071-3096, 2011
#natural image statistics, #deep belief networks, #boltzmann machines, #deep learning
Code, PDF, BibTex

Conference Papers


L. Theis, A. van den Oord, and M. Bethge
A note on the evaluation of generative models
International Conference on Learning Representations (arXiv:1511.01844), 2016
URL, PDF, BibTex
L. Theis and M. D. Hoffman
A trust-region method for stochastic variational inference with applications to streaming data
International Conference on Machine Learning, 2015
#bayesian inference, #lda, #streaming, #svi
PDF, Supplemental, BibTex
L. Theis and M. Bethge
Generative Image Modeling Using Spatial LSTMs
Advances in Neural Information Processing Systems 28, 2015
#deep learning, #generative modeling, #natural image statistics, #lstm, #mcgsm
Code, URL, PDF, Supplemental, BibTex
M. Kümmerer, L. Theis, and M. Bethge
Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet
ICLR Workshop, 2015
#saliency, #deep learning
URL, PDF, BibTex
S. Sra, R. Hosseini, L. Theis, and M. Bethge
Data modeling with the elliptical gamma distribution
Artificial Intelligence and Statistics, 2015
#density estimation, #natural image statistics
URL, BibTex
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, 2012
#natural image statistics, #ica, #overcompleteness
Code, PDF, Supplemental, Poster, BibTex

Book Chapters


H. E. Gerhard, L. Theis, and M. Bethge
Modeling Natural Image Statistics
Biologically-inspired Computer Vision—Fundamentals and Applications (to appear), Wiley VCH, 2015, ISBN 978-3527412648
#natural image statistics, #mcgsm, #ica, #psychophysics
URL, ISBN, PDF, BibTex

Abstracts


L. Theis, D. Arnstein, A. M. Chagas, C. Schwarz, and M. Bethge
Beyond GLMs: a generative mixture modeling approach to neural system identification
Computational and Systems Neuroscience, 2013
#generalized linear model, #spiking neurons, #mixture models
Poster, BibTex
L. Theis, R. Hosseini, and M. Bethge
Mixtures of conditional Gaussian scale mixtures: the best model for natural images
Frontiers in Computational Neuroscience, 2012
#natural image statistics, #gaussian scale mixtures, #mixture models
URL, DOI, Poster, BibTex
L. Theis, D. Arnstein, A. M. Chagas, C. Schwarz, and M. Bethge
Beyond GLMs: a generative mixture modeling approach to neural system identification
Frontiers in Computational Neuroscience, 2012
#generalized linear model, #spiking neurons, #mixture models
URL, DOI, Poster, BibTex
L. Theis, S. Gerwinn, F. Sinz, and M. Bethge
Likelihood Estimation in Deep Belief Networks
Frontiers in Computational Neuroscience, 2010
#deep belief networks, #likelihood estimation, #natural image statistics
Code, URL, DOI, BibTex
University of Tuebingen BCCN CIN MPI