Lucas Theis

Point Reyes

I am a PhD student at the Max Planck Research School for Neural Information Processing in Tübingen, working in the lab of Matthias Bethge. Topics I am interested in include Bayesian inference, deep learning, natural image statistics, and computational neuroscience.

Curriculum vitae

Contact

lucas@theis.io +49 7071 29 88910

On the web

CrossValidated GitHub Facebook Flickr Pinboard

Publications

L. Theis and M. D. Hoffman
A trust-region method for stochastic variational inference
International Conference for Machine Learning, 2015
#bayesian inference, #lda, #streaming, #svi
RIS 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 RIS BibTex
L. Theis, P. Berens, E. Froudarakis, J. Reimer, M. Roman-Roson, T. Baden, et al.
Supervised learning sets benchmark for robust spike detection from calcium imaging signals
bioRxiv, 2015
#two-photon imaging, #spiking neurons
Code URL DOI PDF RIS BibTex
H. E. Gerhard, L. Theis, and M. Bethge
Modeling Natural Image Statistics
Biologically-inspired Computer Vision—Fundamentals and Applications, Wiley VCH, 2015
#natural image statistics, #mcgsm, #ica, #psychophysics
URL ISBN PDF RIS 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 PDF RIS 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 RIS 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 RIS 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 RIS 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
URL DOI PDF RIS BibTex
L. Theis, S. Gerwinn, F. Sinz, and M. Bethge
In All Likelihood, Deep Belief Is Not Enough
Journal of Machine Learning Research, 12, 2011
#natural image statistics, #deep belief networks, #boltzmann machines, #deep learning
Code PDF RIS BibTex