• Corpus ID: 219260454

On the Promise of the Stochastic Generalized Gauss-Newton Method for Training DNNs

  title={On the Promise of the Stochastic Generalized Gauss-Newton Method for Training DNNs},
  author={Matilde Gargiani and Andrea Zanelli and Moritz Diehl and Frank Hutter},
Following early work on Hessian-free methods for deep learning, we study a stochastic generalized Gauss-Newton method (SGN) for training DNNs. SGN is a second-order optimization method, with efficient iterations, that we demonstrate to often require substantially fewer iterations than standard SGD to converge. As the name suggests, SGN uses a Gauss-Newton approximation for the Hessian matrix, and, in order to compute an approximate search direction, relies on the conjugate gradient method… 
Bilevel stochastic methods for optimization and machine learning: Bilevel stochastic descent and DARTS
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Flexible Modification of Gauss-Newton Method and Its Stochastic Extension
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no exception. MRP II and JIT=TQC in purchasing and supplier education are covered in Chapter 15. Without proper education MRP II and JIT=TQC will not be successful and will not generate their true
Deep Learning
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    J. Mach. Learn. Res.
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