
L. Theis,
A. van den Oord, and
M. Bethge
A note on the evaluation of generative models
International Conference on Learning Representations,
2016
#generative modeling
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L. Theis,
P. Berens,
E. Froudarakis,
J. Reimer,
M. RomanRoson,
T. Baden,
et al.
Benchmarking spike rate inference in population calcium imaging
Neuron,
90(3),
471482,
2016
#twophoton imaging,
#spiking neurons
Code
URL
DOI
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Poster
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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
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Poster
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L. Theis and
M. D. Hoffman
A trustregion method for stochastic variational inference with applications to streaming data
Proceedings of the 32nd International Conference on Machine Learning,
2015
#bayesian inference,
#lda,
#streaming,
#svi
Code
URL
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Supplemental
Poster
Talk
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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
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H. E. Gerhard,
L. Theis, and
M. Bethge
Modeling Natural Image Statistics
Biologicallyinspired Computer Vision—Fundamentals and Applications,
Wiley VCH,
2015
#natural image statistics,
#mcgsm,
#ica,
#psychophysics
URL
ISBN
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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,
#mixture modeling
URL
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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
#neuroscience,
#spiking neurons
URL
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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,
#generative modeling
Code
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DOI
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L. Theis,
J. SohlDickstein, 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,
#bayesian inference
Code
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Supplemental
Poster
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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,
#random fields,
#mcgsm,
#mixture models
Code
URL
DOI
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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
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