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Second-order methods

Use second-order statistics to process data.

Papers

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Showing 51–60 of 181 papers

TitleStatusHype
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
Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization—0
Decentralized Riemannian Conjugate Gradient Method on the Stiefel Manifold—0
Approximate Newton Methods and Their Local Convergence—0
DDPNOpt: Differential Dynamic Programming Neural Optimizer—0
A Homogenization Approach for Gradient-Dominated Stochastic Optimization—0
Distributed Quasi-Newton Method for Fair and Fast Federated Learning—0
Distributed Second Order Methods with Fast Rates and Compressed Communication—0
Adaptive Second Order Coresets for Data-efficient Machine Learning—0
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