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Generalization in Kernel Regression Under Realistic Assumptions
It is by now well-established that modern over-parameterized models seem to elude the bias-variance tradeoff and generalize well despite overfitting noise.
Controlling the Inductive Bias of Wide Neural Networks by Modifying the Kernel's Spectrum
We leverage the duality between wide neural networks and Neural Tangent Kernels and propose a preconditioned gradient descent method, which alters the trajectory of GD.
A Kernel Perspective of Skip Connections in Convolutional Networks
Over-parameterized residual networks (ResNets) are amongst the most successful convolutional neural architectures for image processing.
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