Dec 20, 2017 · We introduce a variant of the Hessian-free method that leverages a block-diagonal approximation of the generalized Gauss-Newton matrix.
Oct 30, 2017 · We introduce a vari- ant of the Hessian-free method that leverages a block-diagonal approximation of the generalized Gauss-Newton matrix. Our method computes ...
We introduce a vari- ant of the Hessian-free method that leverages a block-diagonal approximation of the generalized Gauss-Newton matrix. Our method computes ...
A modular extension of the backpropagation algorithm for computation of the block diagonal of the training objective's Hessian to various levels of refinement.
Dec 20, 2017 · We introduce a vari- ant of the Hessian-free method that leverages a block-diagonal approximation of the generalized Gauss-Newton matrix. Our ...
Sep 5, 2024 · Zhang et al. (2017) proposes a block diagonal Hessian-free method to train neural networks and shows fast convergence rate over first-order ...
Alternatively, Hesssian-free (HF) optimization is an appealing approach to training deep neural networks because of its ability to overcome pathological ...
Dec 20, 2017 · We introduce a variant of the Hessian-free method that leverages a block-diagonal approximation of the generalized Gauss-Newton matrix. Our ...
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In this work we resolve the long-outstanding problem of how to effectively train recurrent neu- ral networks (RNNs) on complex and difficult.
A block-diagonal approximation of the positive-curvature Hessian (BDA-PCH) matrix to measure curvature and devise an efficient scheme utilizing the ...