Kevin Swersky
Kevin Swersky
Google Brain
Verified email at cs.toronto.edu - Homepage
Title
Cited by
Cited by
Year
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
13232015
Prototypical networks for few-shot learning
J Snell, K Swersky, R Zemel
Advances in neural information processing systems, 4077-4087, 2017
12352017
Learning fair representations
R Zemel, Y Wu, K Swersky, T Pitassi, C Dwork
International Conference on Machine Learning, 325-333, 2013
6212013
Generative moment matching networks
Y Li, K Swersky, R Zemel
International Conference on Machine Learning, 1718-1727, 2015
4642015
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
4492015
Multi-task bayesian optimization
K Swersky, J Snoek, RP Adams
Advances in neural information processing systems, 2004-2012, 2013
3682013
Neural networks for machine learning lecture 6a overview of mini-batch gradient descent
G Hinton, N Srivastava, K Swersky
Cited on 14 (8), 2012
317*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
2702015
Neural networks for machine learning
G Hinton, N Srivastava, K Swersky
Coursera, video lectures 264 (1), 2012
2652012
The variational fair autoencoder
C Louizos, K Swersky, Y Li, M Welling, R Zemel
arXiv preprint arXiv:1511.00830, 2015
2272015
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
2112018
Inductive principles for restricted Boltzmann machine learning
B Marlin, K Swersky, B Chen, N Freitas
Proceedings of the Thirteenth International Conference on Artificial …, 2010
1562010
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
1422014
Freeze-thaw Bayesian optimization
K Swersky, J Snoek, RP Adams
arXiv preprint arXiv:1406.3896, 2014
1392014
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
1382012
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
822011
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
732010
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
692019
Fast exact inference for recursive cardinality models
D Tarlow, K Swersky, RS Zemel, RP Adams, BJ Frey
arXiv preprint arXiv:1210.4899, 2012
622012
Raiders of the lost architecture: Kernels for Bayesian optimization in conditional parameter spaces
K Swersky, D Duvenaud, J Snoek, F Hutter, MA Osborne
arXiv preprint arXiv:1409.4011, 2014
492014
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