| Photo-realistic single image super-resolution using a generative adversarial network C Ledig, L Theis, F Huszár, J Caballero, A Cunningham, A Acosta, ... Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 5187 | 2017 |
| Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network W Shi, J Caballero, F Huszár, J Totz, AP Aitken, R Bishop, D Rueckert, ... Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 2321 | 2016 |
| Lossy image compression with compressive autoencoders L Theis, W Shi, A Cunningham, F Huszár arXiv preprint arXiv:1703.00395, 2017 | 483 | 2017 |
| Amortised MAP Inference for Image Super-resolution C Kaae Sřnderby, J Caballero, L Theis, W Shi, F Huszár ArXiv e-prints, arXiv: 1610.04490, 2016 | 326* | 2016 |
| Bayesian active learning for classification and preference learning N Houlsby, F Huszár, Z Ghahramani, M Lengyel arXiv preprint arXiv:1112.5745, 2011 | 238 | 2011 |
| How (not) to train your generative model: Scheduled sampling, likelihood, adversary? F Huszár arXiv preprint arXiv:1511.05101, 2015 | 188 | 2015 |
| 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) C Ledig, L Theis, F Huszár, J Caballero, A Cunningham, A Acosta IEEE,, 2017 | 118 | 2017 |
| Adaptive Bayesian quantum tomography F Huszár, NMT Houlsby Physical Review A 85 (5), 052120, 2012 | 116 | 2012 |
| Collaborative Gaussian processes for preference learning N Houlsby, F Huszar, Z Ghahramani, JM Hernández-lobato Advances in Neural Information Processing Systems, 2096-2104, 2012 | 112 | 2012 |
| Variational inference using implicit distributions F Huszár arXiv preprint arXiv:1702.08235, 2017 | 96 | 2017 |
| Faster gaze prediction with dense networks and fisher pruning L Theis, I Korshunova, A Tejani, F Huszár arXiv preprint arXiv:1801.05787, 2018 | 87 | 2018 |
| 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 | 77 | 2016 |
| Experimental adaptive Bayesian tomography KS Kravtsov, SS Straupe, R I. V., NMT Houlsby, H Ferenc, SP Kulik Physical Review A 87 (6), 062122, 2013 | 74 | 2013 |
| Optimally-weighted herding is Bayesian quadrature F Huszár, D Duvenaud arXiv preprint arXiv:1204.1664, 2012 | 74 | 2012 |
| Approximate inference for the loss-calibrated Bayesian S Lacoste–Julien, F Huszár, Z Ghahramani Proceedings of the Fourteenth International Conference on Artificial …, 2011 | 51 | 2011 |
| Cognitive tomography reveals complex, task-independent mental representations NMT Houlsby, F Huszár, MM Ghassemi, G Orbán, DM Wolpert, M Lengyel Current Biology 23 (21), 2169-2175, 2013 | 42 | 2013 |
| Super resolution using a generative adversarial network W Shi, C Ledig, Z Wang, L Theis, F Huszar US Patent App. 15/706,428, 2018 | 39 | 2018 |
| Training end-to-end video processes Z Wang, RD Bishop, F Huszar, L Theis US Patent 10,666,962, 2020 | 37 | 2020 |
| Note on the quadratic penalties in elastic weight consolidation F Huszár Proceedings of the National Academy of Sciences, 201717042, 2018 | 32 | 2018 |
| Bruno: A deep recurrent model for exchangeable data I Korshunova, J Degrave, F Huszár, Y Gal, A Gretton, J Dambre arXiv preprint arXiv:1802.07535, 2018 | 23 | 2018 |
Lucas TheisGoogleVerified email at google.com
