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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
Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian OptimizationCode2
Automatic Gradient Descent: Deep Learning without HyperparametersCode2
Towards Practical Second-Order Optimizers in Deep Learning: Insights from Fisher Information AnalysisCode2
M-FAC: Efficient Matrix-Free Approximations of Second-Order InformationCode1
SASSHA: Sharpness-aware Adaptive Second-order Optimization with Stable Hessian ApproximationCode1
MKOR: Momentum-Enabled Kronecker-Factor-Based Optimizer Using Rank-1 UpdatesCode1
Second-Order Stochastic Optimization for Machine Learning in Linear TimeCode1
Low Rank Saddle Free Newton: A Scalable Method for Stochastic Nonconvex OptimizationCode1
Revisiting Scalable Hessian Diagonal Approximations for Applications in Reinforcement LearningCode1
Second-Order Neural ODE OptimizerCode1
Symmetry Teleportation for Accelerated OptimizationCode1
Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order PerspectiveCode1
ADAHESSIAN: An Adaptive Second Order Optimizer for Machine LearningCode1
Near out-of-distribution detection for low-resolution radar micro-Doppler signaturesCode1
Communication-Efficient Stochastic Zeroth-Order Optimization for Federated LearningCode1
MiLeNAS: Efficient Neural Architecture Search via Mixed-Level ReformulationCode1
Structured Inverse-Free Natural Gradient: Memory-Efficient & Numerically-Stable KFACCode1
Accelerating Stochastic Probabilistic Inference—0
Adaptive and Optimal Second-order Optimistic Methods for Minimax Optimization—0
Alternating direction method of multipliers for regularized multiclass support vector machines—0
A block coordinate descent optimizer for classification problems exploiting convexity—0
A Flexible Tensor Block Coordinate Ascent Scheme for Hypergraph Matching—0
Adversarial Vulnerability as a Consequence of On-Manifold Inseparibility—0
Accelerating SGD for Distributed Deep-Learning Using Approximated Hessian Matrix—0
A Homogenization Approach for Gradient-Dominated Stochastic Optimization—0
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