Ferenc Huszár
TitleCited byYear
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
26212017
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
11142016
Lossy image compression with compressive autoencoders
L Theis, W Shi, A Cunningham, F Huszár
arXiv preprint arXiv:1703.00395, 2017
2272017
Amortised map inference for image super-resolution
CK Sřnderby, J Caballero, L Theis, W Shi, F Huszár
arXiv preprint arXiv:1610.04490, 2016
2202016
Bayesian active learning for classification and preference learning
N Houlsby, F Huszár, Z Ghahramani, M Lengyel
arXiv preprint arXiv:1112.5745, 2011
1242011
How (not) to train your generative model: Scheduled sampling, likelihood, adversary?
F Huszár
arXiv preprint arXiv:1511.05101, 2015
1232015
Adaptive Bayesian quantum tomography
F Huszár, NMT Houlsby
Physical Review A 85 (5), 052120, 2012
962012
Collaborative Gaussian processes for preference learning
N Houlsby, F Huszar, Z Ghahramani, JM Hernández-lobato
Advances in Neural Information Processing Systems, 2096-2104, 2012
882012
Variational inference using implicit distributions
F Huszár
arXiv preprint arXiv:1702.08235, 2017
672017
Experimental adaptive Bayesian tomography
KS Kravtsov, SS Straupe, R I. V., NMT Houlsby, H Ferenc, SP Kulik
Physical Review A 87 (6), 062122, 2013
662013
Optimally-weighted herding is Bayesian quadrature
F Huszár, D Duvenaud
arXiv preprint arXiv:1204.1664, 2012
522012
Faster gaze prediction with dense networks and fisher pruning
L Theis, I Korshunova, A Tejani, F Huszár
arXiv preprint arXiv:1801.05787, 2018
452018
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
422016
Approximate inference for the loss-calibrated Bayesian
S Lacoste–Julien, F Huszár, Z Ghahramani
Proceedings of the Fourteenth International Conference on Artificial …, 2011
352011
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
292013
SUPER RESOLUTION USING A GENERATIVE ADVERSARIAL NETWORK
W Shi, C Ledig, Z Wang, L Theis, F Huszar
US Patent App. 15/706,428, 2018
162018
Note on the quadratic penalties in elastic weight consolidation
F Huszár
Proceedings of the National Academy of Sciences, 201717042, 2018
142018
Mind reading by machine learning: A doubly Bayesian method for inferring mental representations
F Huszár, U Noppeney, M Lengyel
32nd Annual Conference of the Cognitive Science Society (COGSCI 2010), 2810-2815, 2010
142010
In silico Evolutionary Developmental Neurobiology and the Origin of Natural Language
E Szathmáry, Z Szathmáry, P Ittzés, GŐ Orbaán, I Zachár, F Huszár, ...
Emergence of communication and language, 151-187, 2007
112007
An alternative update rule for generative adversarial networks
F Huszar
Unpublished note (retrieved on 7 Oct 2016), 2016
92016
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Articles 1–20