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Found 5 papers, 2 papers with code
Uniform Generalization Bounds on Data-Dependent Hypothesis Sets via PAC-Bayesian Theory on Random Sets
We propose data-dependent uniform generalization bounds by approaching the problem from a PAC-Bayesian perspective.
Generalization Bounds for Heavy-Tailed SDEs through the Fractional Fokker-Planck Equation
Understanding the generalization properties of heavy-tailed stochastic optimization algorithms has attracted increasing attention over the past years.
From Mutual Information to Expected Dynamics: New Generalization Bounds for Heavy-Tailed SGD
This has been successfully applied to generalization theory by exploiting the fractal properties of those dynamics.
Generalization Bounds with Data-dependent Fractal Dimensions
To achieve this goal, we build up on a classical covering argument in learning theory and introduce a data-dependent fractal dimension.
DNN-Based Topology Optimisation: Spatial Invariance and Neural Tangent Kernel
We study the Solid Isotropic Material Penalisation (SIMP) method with a density field generated by a fully-connected neural network, taking the coordinates as inputs.
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