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Deep kernel learning provides an elegant and principled framework for combining the structural properties of deep learning algorithms with the flexibility of kernel methods.
Yaqing Wang and Quanming Yao · 1904
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Gaussian processes for regression
Christopher KI Williams and Carl Edward Rasmussen · 1996
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Learning to learn: Introduction and overview
Sebastian Thrun and Lorien Pratt · 1998
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Separating style and content with bilinear models
Joshua B Tenenbaum and William T Freeman · 2000
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Learning with kernels: support vector machines, regularization, optimization, and beyond
Bernhard Scholkopf and Alexander J Smola · 2001
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Using the nyström method to speed up kernel machines
Christopher KI Williams and Matthias Seeger · 2001
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A perspective view and survey of meta-learning
Ricardo Vilalta and Youssef Drissi · 2002
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Support vector machines
Ingo Steinwart and Andreas Christmann · 2008
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Siamese neural networks for one-shot image recognition
Gregory Koch, Richard Zemel, and Ruslan Salakhutdinov · 2015
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Kernel interpolation for scalable structured gaussian processes (kiss-gp)
Andrew Wilson and Hannes Nickisch · 2015
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al · 2016
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Deep kernel learning
Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, and Eric P Xing · 2016
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David Ha, Andrew Dai, and Quoc V Le · 2016
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Zhenguo Li, Fengwei Zhou, Fei Chen, and Hang Li · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Meta-learning with differentiable closed-form solvers
Luca Bertinetto, Joao F Henriques, Philip HS Torr, and Andrea Vedaldi · 2018
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Film: Visual reasoning with a general conditioning layer
Ethan Perez, Florian Strub, Harm De Vries, Vincent Dumoulin, and Aaron Courville · 2018
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Unsupervised feature learning via non-parametric instance-level discrimination, 2018
Zhirong Wu, Yuanjun Xiong, Stella Yu, and Dahua Lin · 2018
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Learning to compare: Relation network for few-shot learning
Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip HS Torr, and Timothy M Hospedales · 2018
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A closer look at few-shot classification
Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, and Jia-Bin Huang · 2019
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Finite rank deep kernel learning
Sambarta Dasgupta, Kumar Sricharan, and Ashok Srivastava
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Marta Garnelo, Jonathan Schwarz, Dan Rosenbaum, Fabio Viola, Danilo J Rezende, SM Eslami, and Yee Whye Teh
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Few-shot regression via learned basis functions
Yi Loo, Swee Kiat Lim, Gemma Roig, and Ngai-Man Cheung · 2019
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Meta-dataset: A dataset of datasets for learning to learn from few examples
Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, and Hugo Larochelle · 2019
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