Ferenc Huszar
Tytuł
Cytowane przez
Cytowane przez
Rok
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
51462017
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
23022016
Lossy image compression with compressive autoencoders
L Theis, W Shi, A Cunningham, F Huszár
arXiv preprint arXiv:1703.00395, 2017
4772017
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
321*2016
Bayesian active learning for classification and preference learning
N Houlsby, F Huszár, Z Ghahramani, M Lengyel
arXiv preprint arXiv:1112.5745, 2011
2362011
How (not) to train your generative model: Scheduled sampling, likelihood, adversary?
F Huszár
arXiv preprint arXiv:1511.05101, 2015
1862015
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
C Ledig, L Theis, F Huszár, J Caballero, A Cunningham, A Acosta
IEEE,, 2017
1192017
Adaptive Bayesian quantum tomography
F Huszár, NMT Houlsby
Physical Review A 85 (5), 052120, 2012
1162012
Collaborative Gaussian processes for preference learning
N Houlsby, F Huszar, Z Ghahramani, JM Hernández-lobato
Advances in Neural Information Processing Systems, 2096-2104, 2012
1122012
Variational inference using implicit distributions
F Huszár
arXiv preprint arXiv:1702.08235, 2017
962017
Faster gaze prediction with dense networks and fisher pruning
L Theis, I Korshunova, A Tejani, F Huszár
arXiv preprint arXiv:1801.05787, 2018
872018
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
762016
Experimental adaptive Bayesian tomography
KS Kravtsov, SS Straupe, R I. V., NMT Houlsby, H Ferenc, SP Kulik
Physical Review A 87 (6), 062122, 2013
742013
Optimally-weighted herding is Bayesian quadrature
F Huszár, D Duvenaud
arXiv preprint arXiv:1204.1664, 2012
712012
Approximate inference for the loss-calibrated Bayesian
S Lacoste–Julien, F Huszár, Z Ghahramani
Proceedings of the Fourteenth International Conference on Artificial …, 2011
512011
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
422013
Super resolution using a generative adversarial network
W Shi, C Ledig, Z Wang, L Theis, F Huszar
US Patent App. 15/706,428, 2018
392018
Training end-to-end video processes
Z Wang, RD Bishop, F Huszar, L Theis
US Patent 10,666,962, 2020
372020
Note on the quadratic penalties in elastic weight consolidation
F Huszár
Proceedings of the National Academy of Sciences, 201717042, 2018
312018
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
232018
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