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Second-order methods

Use second-order statistics to process data.

Papers

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Showing 101–110 of 181 papers

TitleStatusHype
FedNL: Making Newton-Type Methods Applicable to Federated Learning—0
Tensor Normal Training for Deep Learning ModelsCode0
Exact Stochastic Second Order Deep Learning—0
Quasi-Newton Quasi-Monte Carlo for variational Bayes—0
Research of Damped Newton Stochastic Gradient Descent Method for Neural Network Training—0
A Distributed Optimisation Framework Combining Natural Gradient with Hessian-Free for Discriminative Sequence Training—0
Learning-Augmented Sketches for Hessians—0
Distributed Second Order Methods with Fast Rates and Compressed Communication—0
Kronecker-factored Quasi-Newton Methods for Deep Learning—0
A Chaos Theory Approach to Understand Neural Network Optimization—0
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