7 citations · 25 across the 11 of their papers we have counts for
8 papers · 1 filter
Querying Kernel Methods Suffices for Reconstructing their Training Data
Daniel Barzilai, Yuval Margalit, Eitan Gronich +3
Over-parameterized models have raised concerns about their potential to memorize training data, even when achieving strong generalization. The privacy implications of such memoriza…
On the Reconstruction of Training Data from Group Invariant Networks
Ran Elbaz, Gilad Yehudai, Meirav Galun +1
Reconstructing training data from trained neural networks is an active area of research with significant implications for privacy and explainability. Recent advances have demonstra…
Controlling the Inductive Bias of Wide Neural Networks by Modifying the Kernel's Spectrum
Amnon Geifman, Daniel Barzilai, Ronen Basri +1
Wide neural networks are biased towards learning certain functions, influencing both the rate of convergence of gradient descent (GD) and the functions that are reachable with GD i…
On the Spectral Bias of Convolutional Neural Tangent and Gaussian Process Kernels
Amnon Geifman, Meirav Galun, David Jacobs +1
We study the properties of various over-parametrized convolutional neural architectures through their respective Gaussian process and neural tangent kernels. We prove that, with no…
Spectral Analysis of the Neural Tangent Kernel for Deep Residual Networks
Yuval Belfer, Amnon Geifman, Meirav Galun +1
Deep residual network architectures have been shown to achieve superior accuracy over classical feed-forward networks, yet their success is still not fully understood. Focusing on…
On the Similarity between the Laplace and Neural Tangent Kernels
Amnon Geifman, Abhay Yadav, Yoni Kasten +3
Recent theoretical work has shown that massively overparameterized neural networks are equivalent to kernel regressors that use Neural Tangent Kernels(NTK). Experiments show that t…