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The Neural Tangent Kernel (NTK) has discovered connections between deep neural networks and kernel methods with insights of optimization and generalization.
Finding frequent items in data streams
Charikar, M., Chen, K., and Farach-Colton, M · 2002
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Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
Fei-Fei, L., Fergus, R., and Perona, P · 2004
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Automated flower classification over a large number of classes
Nilsback, M.-E. and Zisserman, A · 2008
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Kernel methods for deep learning
Cho, Y. and Saul, L · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
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Random Features for Large-Scale Kernel Machines
Rahimi, A. and Recht, B · 2009
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The pascal visual object classes (voc) challenge
Everingham, M., Van Gool, L., Williams, C. K., Winn, J., and Zisserman, A · 2010
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Caltech-UCSD Birds 200
Welinder, P., Branson, S., Mita, T., Wah, C., Schroff, F., Belongie, S., and Perona, P · 2010
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Novel dataset for fine-grained image categorization: Stanford dogs
Khosla, A., Jayadevaprakash, N., Yao, B., and Li, F.-F · 2011
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Fast and scalable polynomial kernels via explicit feature maps
Pham, N. and Pagh, R · 2013
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Subspace embeddings for the polynomial kernel
Avron, H., Nguyen, H., and Woodruff, D · 2014
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Food-101–mining discriminative components with random forests
Bossard, L., Guillaumin, M., and Van Gool, L · 2014
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Do we need hundreds of classifiers to solve real world classification problems?
Fernández-Delgado, M., Cernadas, E., Barro, S., and Amorim, D · 2014
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Spherical random features for polynomial kernels
Pennington, J., Yu, F. X. X., and Kumar, S · 2015
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Daniely, A., Frostig, R., and Singer, Y · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Generalization properties of learning with random features
Rudi, A. and Rosasco, L · 2016
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Recursive Sampling for the Nyström Method
Musco, C. and Musco, C · 2017
Cited alongside, same era.
In search of the real inductive bias: On the role of implicit regularization in deep learning
Neyshabur, B., Tomioka, R., and Srebro, N · 2019
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Oblivious sketching of high-degree polynomial kernels
Ahle, T. D., Kapralov, M., Knudsen, J. B., Pagh, R., Velingker, A., Woodruff, D. P., and Zandieh, A · 2020
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Spectra of the conjugate kernel and neural tangent kernel for linear-width neural networks
Fan, Z. and Wang, Z · 2020
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On the similarity between the laplace and neural tangent kernels
Geifman, A., Yadav, A., Kasten, Y., Galun, M., Jacobs, D., and Basri, R · 2020
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Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix
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Jacot, A., Gabriel, F., and Hongler, C · 2018
Cited alongside, same era.
A mean field view of the landscape of two-layer neural networks
Mei, S., Montanari, A., and Nguyen, P.-M · 2018
Cited alongside, same era.
A convergence theory for deep learning via over-parameterization
Allen-Zhu, Z., Li, Y., and Song, Z · 2019
Cited alongside, same era.
On the inductive bias of neural tangent kernels
Bietti, A. and Mairal, J · 2019
Cited alongside, same era.
Generalization bounds of stochastic gradient descent for wide and deep neural networks
Cao, Y. and Gu, Q · 2019
Cited alongside, same era.
On lazy training in differentiable programming
Chizat, L., Oyallon, E., and Bach, F · 2019
Cited alongside, same era.
Gradient descent provably optimizes over-parameterized neural networks
Du, S. S., Zhai, X., Poczos, B., and Singh, A · 2019
Cited alongside, same era.
Han, I., Avron, H., and Shin, J · 2020
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Implicit regularization of random feature models
Jacot, A., Simsek, B., Spadaro, F., Hongler, C., and Gabriel, F · 2020
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Generalized Leverage Score Sampling for Neural Networks
Lee, J., Shen, R., Song, Z., Wang, M., and Yu, Z · 2020
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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 · 2020
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Neural kernels without tangents
Shankar, V., Fang, A., Guo, W., Fridovich-Keil, S., Ragan-Kelley, J., Schmidt, L., and Recht, B · 2020
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Shoham, N. and Avron, H · 2020
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Near Input Sparsity Time Kernel Embeddings via Adaptive Sampling
Woodruff, D. P. and Zandieh, A · 2020
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Predicting training time without training
Zancato, L., Achille, A., Ravichandran, A., Bhotika, R., and Soatto, S · 2020
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Deep neural tangent kernel and laplace kernel have the same RKHS
Chen, L. and Xu, S · 2021
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