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Second-order methods

Use second-order statistics to process data.

Papers

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Showing 1–25 of 181 papers

TitleStatusHype
Towards Practical Second-Order Optimizers in Deep Learning: Insights from Fisher Information AnalysisCode2
Automatic Gradient Descent: Deep Learning without HyperparametersCode2
Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian OptimizationCode2
SASSHA: Sharpness-aware Adaptive Second-order Optimization with Stable Hessian ApproximationCode1
Revisiting Scalable Hessian Diagonal Approximations for Applications in Reinforcement LearningCode1
Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order PerspectiveCode1
Structured Inverse-Free Natural Gradient: Memory-Efficient & Numerically-Stable KFACCode1
MKOR: Momentum-Enabled Kronecker-Factor-Based Optimizer Using Rank-1 UpdatesCode1
Symmetry Teleportation for Accelerated OptimizationCode1
Near out-of-distribution detection for low-resolution radar micro-Doppler signaturesCode1
Communication-Efficient Stochastic Zeroth-Order Optimization for Federated LearningCode1
Second-Order Neural ODE OptimizerCode1
M-FAC: Efficient Matrix-Free Approximations of Second-Order InformationCode1
ADAHESSIAN: An Adaptive Second Order Optimizer for Machine LearningCode1
MiLeNAS: Efficient Neural Architecture Search via Mixed-Level ReformulationCode1
Low Rank Saddle Free Newton: A Scalable Method for Stochastic Nonconvex OptimizationCode1
Second-Order Stochastic Optimization for Machine Learning in Linear TimeCode1
NysAct: A Scalable Preconditioned Gradient Descent using Nystrom ApproximationCode0
KOALA++: Efficient Kalman-Based Optimization of Neural Networks with Gradient-Covariance Products—0
Accelerated Training of Federated Learning via Second-Order Methods—0
Representation Meets Optimization: Training PINNs and PIKANs for Gray-Box Discovery in Systems Pharmacology—0
FedOSAA: Improving Federated Learning with One-Step Anderson Acceleration—0
Online Covariance Matrix Estimation in Sketched Newton MethodsCode0
A Survey of Optimization Methods for Training DL Models: Theoretical Perspective on Convergence and Generalization—0
Distributed Quasi-Newton Method for Fair and Fast Federated Learning—0
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