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Second-order methods

Use second-order statistics to process data.

Papers

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Showing 91–100 of 181 papers

TitleStatusHype
Practical Newton-Type Distributed Learning using Gradient Based Approximations—0
Preconditioners for the Stochastic Training of Neural Fields—0
Provable Regret Bounds for Deep Online Learning and Control—0
Deep Reinforcement Learning via L-BFGS Optimization—0
Quasi-Newton Optimization Methods For Deep Learning Applications—0
Quasi-Newton Quasi-Monte Carlo for variational Bayes—0
QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models—0
Representation Meets Optimization: Training PINNs and PIKANs for Gray-Box Discovery in Systems Pharmacology—0
Research of Damped Newton Stochastic Gradient Descent Method for Neural Network Training—0
RES: Regularized Stochastic BFGS Algorithm—0
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