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Second-order methods

Use second-order statistics to process data.

Papers

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Showing 41–50 of 181 papers

TitleStatusHype
Kronecker-Factored Approximate Curvature for Physics-Informed Neural Networks—0
A Gauss-Newton Approach for Min-Max Optimization in Generative Adversarial NetworksCode0
Inverse-Free Fast Natural Gradient Descent Method for Deep Learning—0
SGD with Partial Hessian for Deep Neural Networks OptimizationCode0
Second Order Methods for Bandit Optimization and Control—0
The Challenges of the Nonlinear Regime for Physics-Informed Neural Networks—0
On The Temporal Domain of Differential Equation Inspired Graph Neural Networks—0
Krylov Cubic Regularized Newton: A Subspace Second-Order Method with Dimension-Free Convergence Rate—0
Adapting Newton's Method to Neural Networks through a Summary of Higher-Order DerivativesCode0
A Computationally Efficient Sparsified Online Newton MethodCode0
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