| Prototypical networks for few-shot learning J Snell, K Swersky, RS Zemel arXiv preprint arXiv:1703.05175, 2017 | 2060 | 2017 |
| Taking the human out of the loop: A review of Bayesian optimization B Shahriari, K Swersky, Z Wang, RP Adams, N De Freitas Proceedings of the IEEE 104 (1), 148-175, 2015 | 1845 | 2015 |
| Learning fair representations R Zemel, Y Wu, K Swersky, T Pitassi, C Dwork International conference on machine learning, 325-333, 2013 | 815 | 2013 |
| Scalable bayesian optimization using deep neural networks J Snoek, O Rippel, K Swersky, R Kiros, N Satish, N Sundaram, M Patwary, ... International conference on machine learning, 2171-2180, 2015 | 582 | 2015 |
| Generative moment matching networks Y Li, K Swersky, R Zemel International Conference on Machine Learning, 1718-1727, 2015 | 566 | 2015 |
| Neural networks for machine learning lecture 6a overview of mini-batch gradient descent G Hinton, N Srivastava, K Swersky Cited on 14 (8), 2012 | 479* | 2012 |
| Multi-task bayesian optimization K Swersky, J Snoek, RP Adams Curran Associates, Inc., 2013 | 471 | 2013 |
| Meta-learning for semi-supervised few-shot classification M Ren, E Triantafillou, S Ravi, J Snell, K Swersky, JB Tenenbaum, ... arXiv preprint arXiv:1803.00676, 2018 | 371 | 2018 |
| Neural networks for machine learning G Hinton, N Srivastava, K Swersky Coursera, video lectures 264 (1), 2012 | 333 | 2012 |
| Predicting deep zero-shot convolutional neural networks using textual descriptions J Lei Ba, K Swersky, S Fidler Proceedings of the IEEE International Conference on Computer Vision, 4247-4255, 2015 | 325 | 2015 |
| The variational fair autoencoder C Louizos, K Swersky, Y Li, M Welling, R Zemel arXiv preprint arXiv:1511.00830, 2015 | 315 | 2015 |
| Lecture 6a overview of mini–batch gradient descent G Hinton, N Srivastava, K Swersky Coursera Lecture slides https://class. coursera. org/neuralnets-2012-001 …, 2012 | 207 | 2012 |
| Input warping for bayesian optimization of non-stationary functions J Snoek, K Swersky, R Zemel, R Adams International Conference on Machine Learning, 1674-1682, 2014 | 169 | 2014 |
| Inductive principles for restricted Boltzmann machine learning B Marlin, K Swersky, B Chen, N Freitas Proceedings of the thirteenth international conference on artificial …, 2010 | 169 | 2010 |
| Freeze-thaw bayesian optimization K Swersky, J Snoek, RP Adams arXiv preprint arXiv:1406.3896, 2014 | 168 | 2014 |
| Meta-dataset: A dataset of datasets for learning to learn from few examples E Triantafillou, T Zhu, V Dumoulin, P Lamblin, U Evci, K Xu, R Goroshin, ... arXiv preprint arXiv:1903.03096, 2019 | 134 | 2019 |
| Big self-supervised models are strong semi-supervised learners T Chen, S Kornblith, K Swersky, M Norouzi, G Hinton arXiv preprint arXiv:2006.10029, 2020 | 101 | 2020 |
| Your classifier is secretly an energy based model and you should treat it like one W Grathwohl, KC Wang, JH Jacobsen, D Duvenaud, M Norouzi, ... arXiv preprint arXiv:1912.03263, 2019 | 90 | 2019 |
| On autoencoders and score matching for energy based models K Swersky, MA Ranzato, D Buchman, ND Freitas, BM Marlin Proceedings of the 28th International Conference on Machine Learning (ICML …, 2011 | 90 | 2011 |
| A tutorial on stochastic approximation algorithms for training restricted Boltzmann machines and deep belief nets K Swersky, B Chen, B Marlin, N De Freitas 2010 Information Theory and Applications Workshop (ITA), 1-10, 2010 | 74 | 2010 |
Richard ZemelProfessor of Computer Science, University of TorontoVerified email at cs.toronto.edu
