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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
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
Adaptive Single-Pass Stochastic Gradient Descent in Input Sparsity Time—0
A Chaos Theory Approach to Understand Neural Network Optimization—0
Noise and Fluctuation of Finite Learning Rate Stochastic Gradient Descent—0
Utility Maximization for Large-Scale Cell-Free Massive MIMO Downlink—0
Second-order Neural Network Training Using Complex-step Directional Derivative—0
Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization—0
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