| An image is worth 16x16 words: Transformers for image recognition at scale A Dosovitskiy, L Beyer, A Kolesnikov, D Weissenborn, X Zhai, ... arXiv preprint arXiv:2010.11929, 2020 | 291 | 2020 |
| Bayesian active learning for classification and preference learning N Houlsby, F Huszár, Z Ghahramani, M Lengyel arXiv preprint arXiv:1112.5745, 2011 | 260 | 2011 |
| Parameter-efficient transfer learning for NLP N Houlsby, A Giurgiu, S Jastrzebski, B Morrone, Q De Laroussilhe, ... International Conference on Machine Learning, 2790-2799, 2019 | 160 | 2019 |
| Adaptive Bayesian quantum tomography F Huszár, NMT Houlsby Physical Review A 85 (5), 052120, 2012 | 118 | 2012 |
| Collaborative Gaussian processes for preference learning N Houlsby, F Huszar, Z Ghahramani, J Hernández-lobato Advances in neural information processing systems 25, 2096-2104, 2012 | 117 | 2012 |
| Self-supervised gans via auxiliary rotation loss T Chen, X Zhai, M Ritter, M Lucic, N Houlsby Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 111 | 2019 |
| 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 6 (2), 8, 2019 | 110 | 2019 |
| 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 | 106 | 2017 |
| Probabilistic matrix factorization with non-random missing data JM Hernández-Lobato, N Houlsby, Z Ghahramani International Conference on Machine Learning, 1512-1520, 2014 | 95 | 2014 |
| Experimental adaptive Bayesian tomography KS Kravtsov, SS Straupe, IV Radchenko, NMT Houlsby, F Huszár, ... Physical Review A 87 (6), 062122, 2013 | 75 | 2013 |
| Transfer learning with neural automl C Wong, N Houlsby, Y Lu, A Gesmundo arXiv preprint arXiv:1803.02780, 2018 | 60 | 2018 |
| Cold-start active learning with robust ordinal matrix factorization N Houlsby, JM Hernández-Lobato, Z Ghahramani International Conference on Machine Learning, 766-774, 2014 | 52 | 2014 |
| Underspecification presents challenges for credibility in modern machine learning A D'Amour, K Heller, D Moldovan, B Adlam, B Alipanahi, A Beutel, ... arXiv preprint arXiv:2011.03395, 2020 | 49 | 2020 |
| On self modulation for generative adversarial networks T Chen, M Lucic, N Houlsby, S Gelly arXiv preprint arXiv:1810.01365, 2018 | 47 | 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 | 46 | 2013 |
| 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 | 43 | 2019 |
| Statistical fitting of undrained strength data NMT Houlsby, GT Houlsby Géotechnique 63 (14), 1253-1263, 2013 | 29 | 2013 |
| Self-supervised learning of video-induced visual invariances M Tschannen, J Djolonga, M Ritter, A Mahendran, N Houlsby, S Gelly, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 27 | 2020 |
| Stochastic inference for scalable probabilistic modeling of binary matrices JM Hernández-Lobato, N Houlsby, Z Ghahramani International Conference on Machine Learning, 379-387, 2014 | 27 | 2014 |
| A large-scale study of representation learning with the visual task adaptation benchmark X Zhai, J Puigcerver, A Kolesnikov, P Ruyssen, C Riquelme, M Lucic, ... arXiv preprint arXiv:1910.04867, 2019 | 21 | 2019 |
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleZweryfikowany adres z eng.cam.ac.uk
