| 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 | 8879 | 2017 |
| A note on the evaluation of generative models L Theis, A van den Oord, M Bethge International Conference on Learning Representations, 2016 | 969 | 2016 |
| Lossy Image Compression with Compressive Autoencoders L Theis, W Shi, A Cunningham, F Huszár International Conference on Learning Representations, 2017 | 845 | 2017 |
| Amortised MAP inference for image super-resolution CK Sřnderby, J Caballero, L Theis, W Shi, F Huszár International Conference on Learning Representations, 2016 | 428 | 2016 |
| Deep gaze I: Boosting saliency prediction with feature maps trained on imagenet M Kümmerer, L Theis, M Bethge arXiv preprint arXiv:1411.1045, 2014 | 407 | 2014 |
| 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 | 285 | 2017 |
| HoloGAN: Unsupervised learning of 3D representations from natural images T Nguyen-Phuoc, C Li, L Theis, C Richardt, YL Yang Proceedings of the IEEE International Conference on Computer Vision, 7588-7597, 2019 | 284 | 2019 |
| Proceedings of the IEEE conference on computer vision and pattern recognition C Ledig, L Theis, F Huszár, J Caballero, A Cunningham, A Acosta, ... Photo-realistic single image super-resolution using a generative adversarial …, 2017 | 272 | 2017 |
| Generative image modeling using spatial LSTMs L Theis, M Bethge Advances in Neural Information Processing Systems 28, 2015 | 208 | 2015 |
| 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 | 191 | 2016 |
| Faster gaze prediction with dense networks and Fisher pruning L Theis, I Korshunova, A Tejani, F Huszár arXiv preprint arXiv:1801.05787, 2018 | 148 | 2018 |
| 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 | 141 | 2017 |
| 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 | 138 | 2016 |
| 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 | 104 | 2018 |
| Training end-to-end video processes Z Wang, RD Bishop, F Huszar, L Theis US Patent 10,666,962, 2020 | 71 | 2020 |
| 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 | 44 | 2011 |
| 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 | 38 | 2013 |
| 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 | 38 | 2013 |
| 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 | 37 | 2015 |
| Addressing delayed feedback for continuous training with neural networks in CTR prediction SI Ktena, A Tejani, L Theis, PK Myana, D Dilipkumar, F Huszár, S Yoo, ... Proceedings of the 13th ACM conference on recommender systems, 187-195, 2019 | 34 | 2019 |
Ferenc HuszárUniversity of CambridgeVerified email at cam.ac.uk
