A two-stage subspace trust region approach for deep neural network ...
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In this paper, we develop a novel second-order method for training feed-forward neural nets. At each iteration, we construct a quadratic approximation to ...
Abstract—In this paper, we develop a novel second-order method for training feed-forward neural nets. At each iteration,.
This approach leads to a fast objective function decay, prevents convergence to saddle points, and alleviates the need for manually tuning parameters. We show ...
TL;DR: In this paper, a second-order method for training feed-forward neural networks is proposed, which minimizes the cost function inside a trust region ...
May 27, 2024 · Bibliographic details on A Two-Stage Subspace Trust Region Approach for Deep Neural Network Training.
This approach leads to a fast objective function decay, prevents convergence to saddle points, and alleviates the need for manually tuning parameters. We show ...
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A Two-Stage Subspace Trust Region Approach for Deep Neural Network Training ... In this paper, we develop a novel second-order method for training feed-forward ...
In this paper we propose a method that allows the trust-region norm to be defined independently of the preconditioner. The method solves the inequality ...
Sep 12, 2024 · We introduce a two-level trust-region method (TLTR) for solving unconstrained nonlinear optimization problems. Our method uses a composite ...