| 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 | 14434 | 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 | 7933 | 2016 |
| Lossy image compression with compressive autoencoders L Theis, W Shi, A Cunningham, F Huszár arXiv preprint arXiv:1703.00395, 2017 | 1347 | 2017 |
| Bayesian active learning for classification and preference learning N Houlsby, F Huszár, Z Ghahramani, M Lengyel arXiv preprint arXiv:1112.5745, 2011 | 983 | 2011 |
| Amortised MAP Inference for Image Super-resolution C Kaae Sønderby, J Caballero, L Theis, W Shi, F Huszár ArXiv e-prints, arXiv: 1610.04490, 2016 | 530* | 2016 |
| How (not) to train your generative model: Scheduled sampling, likelihood, adversary? F Huszár arXiv preprint arXiv:1511.05101, 2015 | 350 | 2015 |
| Algorithmic amplification of politics on Twitter F Huszár, SI Ktena, C O’Brien, L Belli, A Schlaikjer, M Hardt Proceedings of the national academy of sciences 119 (1), e2025334119, 2022 | 348 | 2022 |
| Faster gaze prediction with dense networks and fisher pruning L Theis, I Korshunova, A Tejani, F Huszár arXiv preprint arXiv:1801.05787, 2018 | 255 | 2018 |
| Adaptive Bayesian quantum tomography F Huszár, NMT Houlsby Physical Review A—Atomic, Molecular, and Optical Physics 85 (5), 052120, 2012 | 206 | 2012 |
| 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 | 200 | 2016 |
| Note on the quadratic penalties in elastic weight consolidation F Huszár Proceedings of the National Academy of Sciences 115 (11), E2496-E2497, 2018 | 182 | 2018 |
| Super resolution using a generative adversarial network W Shi, C Ledig, Z Wang, L Theis, F Huszar US Patent 11,024,009, 2021 | 175 | 2021 |
| Variational inference using implicit distributions F Huszár arXiv preprint arXiv:1702.08235, 2017 | 164 | 2017 |
| Collaborative Gaussian processes for preference learning N Houlsby, F Huszar, Z Ghahramani, JM Hernández-lobato Advances in Neural Information Processing Systems, 2096-2104, 2012 | 164 | 2012 |
| Optimally-weighted herding is Bayesian quadrature F Huszár, D Duvenaud arXiv preprint arXiv:1204.1664, 2012 | 117 | 2012 |
| Training end-to-end video processes Z Wang, RD Bishop, F Huszar, L Theis US Patent 10,666,962, 2020 | 114 | 2020 |
| Experimental adaptive Bayesian tomography KS Kravtsov, SS Straupe, R I. V., NMT Houlsby, H Ferenc, SP Kulik Physical Review A 87 (6), 062122, 2013 | 99 | 2013 |
| Approximate inference for the loss-calibrated Bayesian S Lacoste–Julien, F Huszár, Z Ghahramani Proceedings of the Fourteenth International Conference on Artificial …, 2011 | 97 | 2011 |
| Stochastic outlier selection J Janssens, F Huszár, E Postma, H Van den Herik Tilburg centre for Creative Computing, techreport 1, 2012, 2012 | 76 | 2012 |
| 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 | 72 | 2019 |
Wenzhe Shi（施闻哲）ML@Elefant AIDirección de correo verificada de elefant.gg
