Ferenc Huszar
Title
Cited by
Cited by
Year
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
45612017
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
20182016
Lossy image compression with compressive autoencoders
L Theis, W Shi, A Cunningham, F Huszár
arXiv preprint arXiv:1703.00395, 2017
4162017
Amortised MAP Inference for Image Super-resolution
C Kaae Sřnderby, J Caballero, L Theis, W Shi, F Huszár
arXiv, arXiv: 1610.04490, 2016
303*2016
Bayesian active learning for classification and preference learning
N Houlsby, F Huszár, Z Ghahramani, M Lengyel
arXiv preprint arXiv:1112.5745, 2011
2082011
How (not) to train your generative model: Scheduled sampling, likelihood, adversary?
F Huszár
arXiv preprint arXiv:1511.05101, 2015
1762015
Adaptive Bayesian quantum tomography
F Huszár, NMT Houlsby
Physical Review A 85 (5), 052120, 2012
1132012
Collaborative Gaussian processes for preference learning
N Houlsby, F Huszar, Z Ghahramani, JM Hernández-lobato
Advances in Neural Information Processing Systems, 2096-2104, 2012
1092012
Variational inference using implicit distributions
F Huszár
arXiv preprint arXiv:1702.08235, 2017
922017
Faster gaze prediction with dense networks and fisher pruning
L Theis, I Korshunova, A Tejani, F Huszár
arXiv preprint arXiv:1801.05787, 2018
822018
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
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
722016
Optimally-weighted herding is Bayesian quadrature
F Huszár, D Duvenaud
arXiv preprint arXiv:1204.1664, 2012
652012
Approximate inference for the loss-calibrated Bayesian
S Lacoste–Julien, F Huszár, Z Ghahramani
Proceedings of the Fourteenth International Conference on Artificial …, 2011
482011
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
392013
Super resolution using a generative adversarial network
W Shi, C Ledig, Z Wang, L Theis, F Huszar
US Patent App. 15/706,428, 2018
312018
Note on the quadratic penalties in elastic weight consolidation
F Huszár
Proceedings of the National Academy of Sciences, 201717042, 2018
292018
Training end-to-end video processes
Z Wang, RD Bishop, F Huszar, L Theis
US Patent 10,666,962, 2020
282020
Bruno: A deep recurrent model for exchangeable data
I Korshunova, J Degrave, F Huszár, Y Gal, A Gretton, J Dambre
Advances in Neural Information Processing Systems, 7190-7198, 2018
18*2018
Stochastic outlier selection
JHM Janssens, F Huszár, EO Postma, HJ van den Herik
tech. rep., 2012
182012
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