574 citations · 2.1k across the 29 of their papers we have counts for
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stat.ML2019★ 12 cited
Non-Gaussian processes and neural networks at finite widths
Sho Yaida
Gaussian processes are ubiquitous in nature and engineering. A case in point is a class of neural networks in the infinite-width limit, whose priors correspond to Gaussian processe…
stat.ML2019★ 41 cited
Robust Learning with Jacobian Regularization
Judy Hoffman, Daniel A. Roberts, Sho Yaida
Design of reliable systems must guarantee stability against input perturbations. In machine learning, such guarantee entails preventing overfitting and ensuring robustness of model…
stat.ML2018★ 43 cited
Fluctuation-dissipation relations for stochastic gradient descent
Sho Yaida
The notion of the stationary equilibrium ensemble has played a central role in statistical mechanics. In machine learning as well, training serves as generalized equilibration that…