Neil Houlsby
Cited by
Cited by
Bayesian active learning for classification and preference learning
N Houlsby, F Huszár, Z Ghahramani, M Lengyel
arXiv preprint arXiv:1112.5745, 2011
Adaptive Bayesian quantum tomography
F Huszár, NMT Houlsby
Physical Review A 85 (5), 052120, 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
Ask the right questions: Active question reformulation with reinforcement learning
C Buck, J Bulian, M Ciaramita, W Gajewski, A Gesmundo, N Houlsby, ...
arXiv preprint arXiv:1705.07830, 2017
Probabilistic matrix factorization with non-random missing data
JM Hernández-Lobato, N Houlsby, Z Ghahramani
International Conference on Machine Learning, 1512-1520, 2014
Experimental adaptive Bayesian tomography
KS Kravtsov, SS Straupe, IV Radchenko, NMT Houlsby, F Huszár, ...
Physical Review A 87 (6), 062122, 2013
Self-supervised gans via auxiliary rotation loss
T Chen, X Zhai, M Ritter, M Lucic, N Houlsby
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019
Cold-start active learning with robust ordinal matrix factorization
N Houlsby, JM Hernández-Lobato, Z Ghahramani
International Conference on Machine Learning, 766-774, 2014
Parameter-efficient transfer learning for NLP
N Houlsby, A Giurgiu, S Jastrzebski, B Morrone, Q De Laroussilhe, ...
arXiv preprint arXiv:1902.00751, 2019
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
Transfer learning with neural automl
C Wong, N Houlsby, Y Lu, A Gesmundo
Advances in Neural Information Processing Systems, 8356-8365, 2018
On self modulation for generative adversarial networks
T Chen, M Lucic, N Houlsby, S Gelly
arXiv preprint arXiv:1810.01365, 2018
Stochastic inference for scalable probabilistic modeling of binary matrices
JM Hernández-Lobato, N Houlsby, Z Ghahramani
International Conference on Machine Learning, 379-387, 2014
Statistical fitting of undrained strength data
NMT Houlsby, GT Houlsby
Géotechnique 63 (14), 1253-1263, 2013
A scalable gibbs sampler for probabilistic entity linking
N Houlsby, M Ciaramita
European Conference on Information Retrieval, 335-346, 2014
Large scale learning of general visual representations for transfer
A Kolesnikov, L Beyer, X Zhai, J Puigcerver, J Yung, S Gelly, N Houlsby
arXiv preprint arXiv:1912.11370, 2019
The visual task adaptation benchmark
X Zhai, J Puigcerver, A Kolesnikov, P Ruyssen, C Riquelme, M Lucic, ...
arXiv preprint arXiv:1910.04867, 2019
Active learning for interactive visualization
T Iwata, N Houlsby, Z Ghahramani
Artificial Intelligence and Statistics, 342-350, 2013
Self-supervised generative adversarial networks
T Chen, X Zhai, M Ritter, M Lucic, N Houlsby
arXiv preprint arXiv:1811.11212 2, 2018
Scalable probabilistic entity-topic modeling
N Houlsby, M Ciaramita
arXiv preprint arXiv:1309.0337, 2013
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