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Found 4 papers, 2 papers with code
Characterizing Overfitting in Kernel Ridgeless Regression Through the Eigenspectrum
We derive new bounds for the condition number of kernel matrices, which we then use to enhance existing non-asymptotic test error bounds for kernel ridgeless regression in the over-parameterized regime for a fixed input dimension.
Injectivity of ReLU networks: perspectives from statistical physics
Recent work connects this problem to spherical integral geometry giving rise to a conjectured sharp injectivity threshold for $\alpha = \frac{m}{n}$ by studying the expected Euler characteristic of a certain random set.
Learning Multiscale Convolutional Dictionaries for Image Reconstruction
To close the performance gap, we thus propose a multiscale convolutional dictionary structure.
On the Empirical Neural Tangent Kernel of Standard Finite-Width Convolutional Neural Network Architectures
The Neural Tangent Kernel (NTK) is an important milestone in the ongoing effort to build a theory for deep learning.
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