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Amnon Geifman
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Frequency bias in neural networks for input of non-uniform density
R Basri, M Galun, A Geifman, D Jacobs, Y Kasten, S Kritchman
International Conference on Machine Learning, 685-694, 2020
1322020
On the similarity between the laplace and neural tangent kernels
A Geifman, A Yadav, Y Kasten, M Galun, D Jacobs, R Basri
Advances in Neural Information Processing Systems, 2020
782020
Algebraic characterization of essential matrices and their averaging in multiview settings
Y Kasten, A Geifman, M Galun, R Basri
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019
272019
Gpsfm: Global projective sfm using algebraic constraints on multi-view fundamental matrices
Y Kasten, A Geifman, M Galun, R Basri
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019
242019
Averaging essential and fundamental matrices in collinear camera settings
A Geifman, Y Kasten, M Galun, R Basri
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
142020
Spectral analysis of the neural tangent kernel for deep residual networks
Y Belfer, A Geifman, M Galun, R Basri
arXiv preprint arXiv:2104.03093, 2021
92021
A kernel perspective of skip connections in convolutional networks
D Barzilai, A Geifman, M Galun, R Basri
arXiv preprint arXiv:2211.14810, 2022
82022
On the spectral bias of convolutional neural tangent and gaussian process kernels
A Geifman, M Galun, D Jacobs, B Ronen
Advances in Neural Information Processing Systems 35, 11253-11265, 2022
72022
Controlling the Inductive Bias of Wide Neural Networks by Modifying the Kernel's Spectrum
A Geifman, D Barzilai, R Basri, M Galun
arXiv preprint arXiv:2307.14531, 2023
12023
On the Spectral Bias of Convolutional Neural Tangent and Gaussian Process Kernels – Supplementary Material –
A Geifman, M Galun, D Jacobs, R Basri
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Articles 1–10