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cs.LG2023
Dual Gauss-Newton Directions for Deep Learning
Vincent Roulet, Mathieu Blondel
Inspired by Gauss-Newton-like methods, we study the benefit of leveraging the structure of deep learning objectives, namely, the composition of a convex loss function and of a nonl…
cs.LG2020
An Elementary Approach to Convergence Guarantees of Optimization Algorithms for Deep Networks
Vincent Roulet, Zaid Harchaoui
We present an approach to obtain convergence guarantees of optimization algorithms for deep networks based on elementary arguments and computations. The convergence analysis revolv…