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Learning the principal eigenfunctions of an integral operator defined by a kernel and a data distribution is at the core of many machine learning problems.
Über die praktische auflösung von integralgleichungen mit anwendungen auf randwertaufgaben
Nyström, E. J · 1930
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Inverting modified matrices
Woodbury, M. A · 1950
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Bayesian methods for adaptive models
Mackay, D. J. C · 1992
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Priors for infinite networks
Neal, R. M · 1996
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Crafting papers on machine learning
Langley, P · 2000
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Variational inference in probabilistic models
Lawrence, N. D · 2001
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An introduction to kernel-based learning algorithms
Muller, K.-R., Mika, S., Ratsch, G., Tsuda, K., and Scholkopf, B · 2001
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Using the nyström method to speed up kernel machines
Williams, C. and Seeger, M · 2001
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Learning with kernels: support vector machines, regularization, optimization, and beyond
Schölkopf, B., Smola, A. J., Bach, F., et al · 2002
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Learning eigenfunctions links spectral embedding and kernel PCA
Bengio, Y., Delalleau, O., Roux, N. L., Paiement, J.-F., Vincent, P., and Ouimet, M · 2004
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The curse of highly variable functions for local kernel machines
Bengio, Y., Delalleau, O., and Roux, N · 2005
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Random features for large-scale kernel machines
Rahimi, A. and Recht, B · 2007
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Weighted sums of random kitchen sinks: Replacing minimization with randomization in learning
Rahimi, A. and Recht, B · 2008
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Deep kernel learning
Wilson, A. G., Hu, Z., Salakhutdinov, R., and Xing, E. P · 2016
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Orthogonal random features
Yu, F. X. X., Suresh, A. T., Choromanski, K. M., Holtmann-Rice, D. N., and Kumar, S · 2016
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On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
Cited alongside, same era.
Bayesian deep convolutional networks with many channels are gaussian processes
Novak, R., Xiao, L., Lee, J., Bahri, Y., Yang, G., Hron, J., Abolafia, D. A., Pennington, J., and Sohl-Dickstein, J · 2018
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Spectral inference networks: Unifying deep and spectral learning
Pfau, D., Petersen, S., Agarwal, A., Barrett, D. G., and Stachenfeld, K. L · 2018
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On exact computation with an infinitely wide neural net
Arora, S., Du, S. S., Hu, W., Li, Z., Salakhutdinov, R., and Wang, R · 2019
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Graph neural tangent kernel: Fusing graph neural networks with graph kernels
Du, S. S., Hou, K., Salakhutdinov, R. R., Poczos, B., Wang, R., and Xu, K · 2019
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Deep neural networks as gaussian processes
Lee, J., Bahri, Y., Novak, R., Schoenholz, S. S., Pennington, J., and Sohl-Dickstein, J · 2017
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Stochastic gradient descent as approximate Bayesian inference
Mandt, S., Hoffman, M. D., and Blei, D. M · 2017
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Deep convolutional networks as shallow gaussian processes
Garriga-Alonso, A., Rasmussen, C. E., and Aitchison, L · 2018
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Averaging weights leads to wider optima and better generalization
Izmailov, P., Podoprikhin, D., Garipov, T., Vetrov, D., and Wilson, A. G · 2018
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Neural tangent kernel: Convergence and generalization in neural networks
Jacot, A., Gabriel, F., and Hongler, C · 2018
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Gaussian process behaviour in wide deep neural networks
Matthews, A. G. d. G., Rowland, M., Hron, J., Turner, R. E., and Ghahramani, Z · 2018
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Foong, A. Y., Li, Y., Hernández-Lobato, J. M., and Turner, R. E · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T · 2019
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Approximate inference turns deep networks into Gaussian processes
Khan, M. E. E., Immer, A., Abedi, E., and Korzepa, M · 2019
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A simple baseline for bayesian uncertainty in deep learning
Maddox, W. J., Izmailov, P., Garipov, T., Vetrov, D. P., and Wilson, A. G · 2019
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Neural tangents: Fast and easy infinite neural networks in python
Novak, R., Xiao, L., Hron, J., Lee, J., Alemi, A. A., Sohl-Dickstein, J., and Schoenholz, S. S · 2019
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Eigengame: Pca as a nash equilibrium
Gemp, I., McWilliams, B., Vernade, C., and Graepel, T · 2020
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Neural networks as inter-domain inducing points
Sun, S., Shi, J., and Grosse, R. B · 2020
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Laplace redux-effortless Bayesian deep learning
Daxberger, E., Kristiadi, A., Immer, A., Eschenhagen, R., Bauer, M., and Hennig, P · 2021
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Major advancements in kernel function approximation
Francis, D. P. and Raimond, K · 2021
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