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Found 4 papers, 1 papers with code
Universal Graph Random Features
This includes many of the most popular examples of kernels defined on the nodes of a graph.
Repelling Random Walks
We present a novel quasi-Monte Carlo mechanism to improve graph-based sampling, coined repelling random walks.
Simplex Random Features
We present Simplex Random Features (SimRFs), a new random feature (RF) mechanism for unbiased approximation of the softmax and Gaussian kernels by geometrical correlation of random projection vectors.
Why flatness does and does not correlate with generalization for deep neural networks
As an alternative to flatness measures, we use a function based picture and propose using the log of Bayesian prior upon initialization, $\log P(f)$, as a predictor of the generalization when a DNN converges on function $f$ after training to zero error.
