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Second-order methods

Use second-order statistics to process data.

Papers

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Showing 131–140 of 181 papers

TitleStatusHype
KOALA++: Efficient Kalman-Based Optimization of Neural Networks with Gradient-Covariance Products—0
A block coordinate descent optimizer for classification problems exploiting convexity—0
Accelerated Projected Gradient Method for the Optimization of Cell-Free Massive MIMO Downlink—0
Accelerated Training of Federated Learning via Second-Order Methods—0
Accelerating SGD for Distributed Deep-Learning Using Approximated Hessian Matrix—0
Accelerating Stochastic Probabilistic Inference—0
A Chaos Theory Approach to Understand Neural Network Optimization—0
A comparison of second-order methods for deep convolutional neural networks—0
Adaptive and Optimal Second-order Optimistic Methods for Minimax Optimization—0
A Learn-to-Optimize Approach for Coordinate-Wise Step Sizes for Quasi-Newton Methods—0
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