Kevin Swersky
Kevin Swersky
Google Brain
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Cited by
Cited by
Prototypical networks for few-shot learning
J Snell, K Swersky, RS Zemel
arXiv preprint arXiv:1703.05175, 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
Learning fair representations
R Zemel, Y Wu, K Swersky, T Pitassi, C Dwork
International conference on machine learning, 325-333, 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
Generative moment matching networks
Y Li, K Swersky, R Zemel
International Conference on Machine Learning, 1718-1727, 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
Multi-task bayesian optimization
K Swersky, J Snoek, RP Adams
Curran Associates, Inc., 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
Neural networks for machine learning
G Hinton, N Srivastava, K Swersky
Coursera, video lectures 264 (1), 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
The variational fair autoencoder
C Louizos, K Swersky, Y Li, M Welling, R Zemel
arXiv preprint arXiv:1511.00830, 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
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
Inductive principles for restricted Boltzmann machine learning
B Marlin, K Swersky, B Chen, N Freitas
Proceedings of the thirteenth international conference on artificial …, 2010
Freeze-thaw bayesian optimization
K Swersky, J Snoek, RP Adams
arXiv preprint arXiv:1406.3896, 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
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
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
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
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
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