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Second-order methods

Use second-order statistics to process data.

Papers

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Showing 171–180 of 181 papers

TitleStatusHype
Optimization Methods for Supervised Machine Learning: From Linear Models to Deep LearningCode0
Large batch size training of neural networks with adversarial training and second-order informationCode0
Adapting Newton's Method to Neural Networks through a Summary of Higher-Order DerivativesCode0
Stochastic Newton and Cubic Newton Methods with Simple Local Linear-Quadratic RatesCode0
Limitations of the Empirical Fisher Approximation for Natural Gradient DescentCode0
A Computationally Efficient Sparsified Online Newton MethodCode0
Exact, Tractable Gauss-Newton Optimization in Deep Reversible Architectures Reveal Poor GeneralizationCode0
The Hypervolume Indicator Hessian Matrix: Analytical Expression, Computational Time Complexity, and SparsityCode0
Error Feedback Can Accurately Compress PreconditionersCode0
AdaSub: Stochastic Optimization Using Second-Order Information in Low-Dimensional SubspacesCode0
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