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Found 3 papers, 1 papers with code
Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances
For a two-layer overparameterized network of width $ r^*+ h =: m $ we explicitly describe the manifold of global minima: it consists of $ T(r^*, m) $ affine subspaces of dimension at least $ h $ that are connected to one another.
Kernel Alignment Risk Estimator: Risk Prediction from Training Data
Under a natural universality assumption (that the KRR moments depend asymptotically on the first two moments of the observations) we capture the mean and variance of the KRR predictor.
Implicit Regularization of Random Feature Models
We investigate, by means of random matrix theory, the connection between Gaussian RF models and Kernel Ridge Regression (KRR).
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