Lucas Theis
TitleCited byYear
Photo-realistic single image super-resolution using a generative adversarial network
C Ledig, L Theis, F Huszár, J Caballero, A Cunningham, A Acosta, ...
Computer Vision and Pattern Recognition, 2017
25392017
A note on the evaluation of generative models
L Theis, A van den Oord, M Bethge
International Conference on Learning Representations, 2016
4662016
Deep gaze I: Boosting saliency prediction with feature maps trained on imagenet
M Kümmerer, L Theis, M Bethge
arXiv preprint arXiv:1411.1045, 2014
2432014
Lossy Image Compression with Compressive Autoencoders
L Theis, W Shi, A Cunningham, F Huszár
International Conference on Learning Representations, 2017
2202017
Amortised MAP inference for image super-resolution
CK Sřnderby, J Caballero, L Theis, W Shi, F Huszár
International Conference on Learning Representations, 2016
2162016
Generative image modeling using spatial LSTMs
L Theis, M Bethge
Advances in Neural Information Processing Systems 28, 2015
1372015
Benchmarking spike rate inference in population calcium imaging
L Theis, P Berens, E Froudarakis, J Reimer, MR Rosón, T Baden, T Euler, ...
Neuron 90 (3), 471-482, 2016
96*2016
Fast face-swap using convolutional neural networks
I Korshunova, W Shi, J Dambre, L Theis
Proceedings of the IEEE International Conference on Computer Vision, 3677-3685, 2017
612017
Faster gaze prediction with dense networks and Fisher pruning
L Theis, I Korshunova, A Tejani, F Huszár
arXiv preprint arXiv:1801.05787, 2018
432018
Is the deconvolution layer the same as a convolutional layer?
W Shi, J Caballero, L Theis, F Huszar, A Aitken, C Ledig, Z Wang
arXiv preprint arXiv:1609.07009, 2016
422016
Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resize
A Aitken, C Ledig, L Theis, J Caballero, Z Wang, W Shi
arXiv preprint arXiv:1707.02937, 2017
382017
Community-based benchmarking improves spike rate inference from two-photon calcium imaging data
P Berens, J Freeman, T Deneux, N Chenkov, T McColgan, A Speiser, ...
PLoS computational biology 14 (5), e1006157, 2018
352018
In All Likelihood, Deep Belief Is Not Enough
L Theis, S Gerwinn, F Sinz, M Bethge
Journal of Machine Learning Research 12, 3071-3096, 2011
352011
Beyond GLMs: A Generative Mixture Modeling Approach to Neural System Identification
L Theis, AM Chagas, D Arnstein, C Schwarz, M Bethge
PLoS computational biology 9 (11), e1003356, 2013
282013
Functional analysis of ultra high information rates conveyed by rat vibrissal primary afferents
AM Chagas, L Theis, B Sengupta, MC Stüttgen, M Bethge, C Schwarz
Frontiers in Neural Circuits 7, 2013
232013
Mixtures of Conditional Gaussian Scale Mixtures Applied to Multiscale Image Representations
L Theis, R Hosseini, M Bethge
PloS ONE 7 (7), 2012
232012
A trust-region method for stochastic variational inference with applications to streaming data
L Theis, MD Hoffman
Proceedings of the 32nd International Conference on Machine Learning, 2015
172015
Super-resolution using a generative adversarial network
W Shi, C Ledig, Z Wang, L Theis, F Huszar
US Patent App. 15/706,428, 2018
162018
Training sparse natural image models with a fast Gibbs sampler of an extended state space
L Theis, J Sohl-Dickstein, M Bethge
Advances in Neural Information Processing Systems 25, 1133-1141, 2012
72012
Modeling Natural Image Statistics
HE Gerhard, L Theis, M Bethge
Biologically-inspired Computer Vision: Fundamentals and Applications, 2015
62015
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Articles 1–20