| Bayesian active learning for classification and preference learning N Houlsby, F Huszár, Z Ghahramani, M Lengyel arXiv preprint arXiv:1112.5745, 2011 | 199 | 2011 |
| Adaptive Bayesian quantum tomography F Huszár, NMT Houlsby Physical Review A 85 (5), 052120, 2012 | 111 | 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 | 106 | 2012 |
| Parameter-efficient transfer learning for NLP N Houlsby, A Giurgiu, S Jastrzebski, B Morrone, Q De Laroussilhe, ... arXiv preprint arXiv:1902.00751, 2019 | 84 | 2019 |
| Probabilistic matrix factorization with non-random missing data JM Hernández-Lobato, N Houlsby, Z Ghahramani International Conference on Machine Learning, 1512-1520, 2014 | 84 | 2014 |
| 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 | 83 | 2017 |
| Experimental adaptive Bayesian tomography KS Kravtsov, SS Straupe, IV Radchenko, NMT Houlsby, F Huszár, ... Physical Review A 87 (6), 062122, 2013 | 74 | 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 | 70 | 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 | 50 | 2014 |
| Transfer learning with neural automl C Wong, N Houlsby, Y Lu, A Gesmundo Advances in Neural Information Processing Systems, 8356-8365, 2018 | 46 | 2018 |
| 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 | 39 | 2013 |
| On self modulation for generative adversarial networks T Chen, M Lucic, N Houlsby, S Gelly arXiv preprint arXiv:1810.01365, 2018 | 35 | 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 | 26 | 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 | 25 | 2019 |
| Statistical fitting of undrained strength data NMT Houlsby, GT Houlsby Géotechnique 63 (14), 1253-1263, 2013 | 25 | 2013 |
| A scalable gibbs sampler for probabilistic entity linking N Houlsby, M Ciaramita European Conference on Information Retrieval, 335-346, 2014 | 21 | 2014 |
| Big transfer (BiT): General visual representation learning A Kolesnikov, L Beyer, X Zhai, J Puigcerver, J Yung, S Gelly, N Houlsby arXiv preprint arXiv:1912.11370, 2019 | 19 | 2019 |
| The visual task adaptation benchmark X Zhai, J Puigcerver, A Kolesnikov, P Ruyssen, C Riquelme, M Lucic, ... arXiv preprint arXiv:1910.04867, 2019 | 19 | 2019 |
| Active learning for interactive visualization T Iwata, N Houlsby, Z Ghahramani Artificial Intelligence and Statistics, 342-350, 2013 | 17 | 2013 |
| Self-supervised generative adversarial networks T Chen, X Zhai, M Ritter, M Lucic, N Houlsby arXiv preprint arXiv:1811.11212 2, 2018 | 13 | 2018 |
Zoubin GhahramaniProfessor, University of Cambridge, and Chief Scientist, UberZweryfikowany adres z eng.cam.ac.uk
