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Second-order methods

Use second-order statistics to process data.

Papers

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Showing 161–170 of 181 papers

TitleStatusHype
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning—0
Bilinear Parameterization For Differentiable Rank-Regularization—0
Bilinear Parameterization for Non-Separable Singular Value Penalties—0
Biologically inspired protection of deep networks from adversarial attacks—0
Block-diagonal Hessian-free Optimization for Training Neural Networks—0
Component-Wise Natural Gradient Descent -- An Efficient Neural Network Optimization—0
Convolutions and More as Einsum: A Tensor Network Perspective with Advances for Second-Order Methods—0
Critical Point-Finding Methods Reveal Gradient-Flat Regions of Deep Network Losses—0
Curvature-corrected learning dynamics in deep neural networks—0
Learning Rates as a Function of Batch Size: A Random Matrix Theory Approach to Neural Network Training—0
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