49 citations · 52 across the 2 of their papers we have counts for
4 papers · 1 filter
Eliminating all bad Local Minima from Loss Landscapes without even adding an Extra Unit
Jascha Sohl-Dickstein, Kenji Kawaguchi
Recent work has noted that all bad local minima can be removed from neural network loss landscapes, by adding a single unit with a particular parameterization. We show that the cor…
Depth with Nonlinearity Creates No Bad Local Minima in ResNets
Kenji Kawaguchi, Yoshua Bengio
In this paper, we prove that depth with nonlinearity creates no bad local minima in a type of arbitrarily deep ResNets with arbitrary nonlinear activation functions, in the sense t…
Generalization in Machine Learning via Analytical Learning Theory
Kenji Kawaguchi, Yoshua Bengio, Vikas Verma +1
This paper introduces a novel measure-theoretic theory for machine learning that does not require statistical assumptions. Based on this theory, a new regularization method in deep…
Bayesian Optimization with Exponential Convergence
Kenji Kawaguchi, Leslie Pack Kaelbling, Tomás Lozano-Pérez
This paper presents a Bayesian optimization method with exponential convergence without the need of auxiliary optimization and without the delta-cover sampling. Most Bayesian optim…