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One of the most fundamental aspects of any machine learning algorithm is the training data used by the algorithm.
Harnessing the power of infinitely wide deep nets on small-data tasks
Sanjeev Arora, Simon S Du, Zhiyuan Li, Ruslan Salakhutdinov, Ruosong Wang, and Dingli Yu · 1910
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Note on the generalized inverse of a matrix product
T. N. E. Greville · 1966
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Perturbation Theory of Linear Operators
T. Kato · 1976
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Priors for infinite networks (tech. rep. no. crg-tr-94-1)
Radford M. Neal · 1994
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Using the nyström method to speed up kernel machines
Christopher KI Williams and Matthias Seeger · 2001
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Sparseness of support vector machines
Ingo Steinwart · 2003
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Fast kernel classifiers with online and active learning
Antoine Bordes, Seyda Ertekin, Jason Weston, and Léon Bottou · 2005
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On the nyström method for approximating a gram matrix for improved kernel-based learning
Petros Drineas and Michael W Mahoney · 2005
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Sparse gaussian processes using pseudo-inputs
Edward Snelson and Zoubin Ghahramani · 2006
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Variational learning of inducing variables in sparse gaussian processes
Michalis Titsias · 2009
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Mnist handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
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Convolutional kernel networks
Julien Mairal, Piotr Koniusz, Zaid Harchaoui, and Cordelia Schmid · 2014
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Gradient-based hyperparameter optimization through reversible learning
Dougal Maclaurin, David Duvenaud, and Ryan Adams · 2015
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Jeff M Phillips · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy P. Lillicrap, Koray Kavukcuoglu, and Daan Wierstra · 2016
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Sergey Zagoruyko and Nikos Komodakis · 2016
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Deep metric learning: A survey
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Wide neural networks of any depth evolve as linear models under gradient descent
Jaehoon Lee, Lechao Xiao, Samuel S. Schoenholz, Yasaman Bahri, Roman Novak, Jascha Sohl-Dickstein, and Jeffrey Pennington · 2019
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Bayesian deep convolutional networks with many channels are gaussian processes
Roman Novak, Lechao Xiao, Jaehoon Lee, Yasaman Bahri, Greg Yang, Jiri Hron, Daniel A. Abolafia, Jeffrey Pennington, and Jascha Sohl-Dickstein · 2019
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Using small proxy datasets to accelerate hyperparameter search
Sam Shleifer and Eric Prokop · 2019
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Soft-label dataset distillation and text dataset distillation
Ilia Sucholutsky and Matthias Schonlau · 2019
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JAX: composable transformations of Python+NumPy programs, 2018
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Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clement Hongler · 2018
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Deep neural networks as gaussian processes
Jaehoon Lee, Yasaman Bahri, Roman Novak, Sam Schoenholz, Jeffrey Pennington, and Jascha Sohl-dickstein · 2018
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Gaussian process behaviour in wide deep neural networks
Alexander G. de G. Matthews, Jiri Hron, Mark Rowland, Richard E. Turner, and Zoubin Ghahramani · 2018
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Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros · 2018
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Deep convolutional networks as shallow gaussian processes
Adrià Garriga-Alonso, Laurence Aitchison, and Carl Edward Rasmussen · 2019
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Flexible dataset distillation: Learn labels instead of images
Ondrej Bohdal, Yongxin Yang, and Timothy Hospedales · 2020
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Coresets via bilevel optimization for continual learning and streaming
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Instahide: Instance-hiding schemes for private distributed learning, 2020
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Finite versus infinite neural networks: an empirical study
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Optimizing millions of hyperparameters by implicit differentiation
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Neural tangents: Fast and easy infinite neural networks in python
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Neural kernels without tangents
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On the infinite width limit of neural networks with a standard parameterization
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Dataset condensation with gradient matching
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2020
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