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Kevin Swersky
Kevin Swersky
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Title
Cited by
Cited by
Year
Prototypical networks for few-shot learning
J Snell, K Swersky, R Zemel
Advances in neural information processing systems 30, 2017
48482017
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
32222015
Learning fair representations
R Zemel, Y Wu, K Swersky, T Pitassi, C Dwork
International conference on machine learning, 325-333, 2013
14012013
Neural networks for machine learning lecture 6a overview of mini-batch gradient descent
G Hinton, N Srivastava, K Swersky
Cited on 14 (8), 2, 2012
993*2012
Big self-supervised models are strong semi-supervised learners
T Chen, S Kornblith, K Swersky, M Norouzi, GE Hinton
Advances in neural information processing systems 33, 22243-22255, 2020
9882020
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
9442018
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
8962015
Generative moment matching networks
Y Li, K Swersky, R Zemel
International conference on machine learning, 1718-1727, 2015
8042015
Multi-task bayesian optimization
K Swersky, J Snoek, RP Adams
Advances in neural information processing systems 26, 2013
6532013
Neural networks for machine learning
G Hinton, N Srivastava, K Swersky
Coursera, video lectures 264 (1), 2146-2153, 2012
5212012
The variational fair autoencoder
C Louizos, K Swersky, Y Li, M Welling, R Zemel
arXiv preprint arXiv:1511.00830, 2015
5132015
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
4152015
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
3842019
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
2552019
Freeze-thaw Bayesian optimization
K Swersky, J Snoek, RP Adams
arXiv preprint arXiv:1406.3896, 2014
2342014
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
2322012
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
2202014
Flexibly fair representation learning by disentanglement
E Creager, D Madras, JH Jacobsen, M Weis, K Swersky, T Pitassi, ...
International conference on machine learning, 1436-1445, 2019
1952019
Inductive principles for restricted Boltzmann machine learning
B Marlin, K Swersky, B Chen, N Freitas
Proceedings of the thirteenth international conference on artificial …, 2010
1922010
Learning memory access patterns
M Hashemi, K Swersky, J Smith, G Ayers, H Litz, J Chang, C Kozyrakis, ...
International Conference on Machine Learning, 1919-1928, 2018
1422018
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