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Second-order methods

Use second-order statistics to process data.

Papers

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Showing 51–75 of 181 papers

TitleStatusHype
Adaptive Optimization Algorithms for Machine Learning—0
AdaSub: Stochastic Optimization Using Second-Order Information in Low-Dimensional SubspacesCode0
Studying K-FAC Heuristics by Viewing Adam through a Second-Order LensCode0
Stochastic Optimization for Non-convex Problem with Inexact Hessian Matrix, Gradient, and Function—0
Jorge: Approximate Preconditioning for GPU-efficient Second-order Optimization—0
A Homogenization Approach for Gradient-Dominated Stochastic Optimization—0
Decentralized Riemannian Conjugate Gradient Method on the Stiefel Manifold—0
Convolutions and More as Einsum: A Tensor Network Perspective with Advances for Second-Order Methods—0
Near-Optimal Nonconvex-Strongly-Convex Bilevel Optimization with Fully First-Order Oracles—0
Error Feedback Can Accurately Compress PreconditionersCode0
Minibatching Offers Improved Generalization Performance for Second Order Optimizers—0
Sharpened Lazy Incremental Quasi-Newton MethodCode0
Faster Differentially Private Convex Optimization via Second-Order Methods—0
ISAAC Newton: Input-based Approximate Curvature for Newton's MethodCode0
Adaptive Consensus Optimization Method for GANsCode0
Gradient-Boosted Based Structured and Unstructured Learning—0
FOSI: Hybrid First and Second Order OptimizationCode0
Stochastic Dimension-reduced Second-order Methods for Policy Optimization—0
On backpropagating Hessians through ODEs—0
A survey of deep learning optimizers -- first and second order methods—0
Differentially Private Image Classification from FeaturesCode0
The Hypervolume Indicator Hessian Matrix: Analytical Expression, Computational Time Complexity, and SparsityCode0
Extra-Newton: A First Approach to Noise-Adaptive Accelerated Second-Order Methods—0
Explicit Second-Order Min-Max Optimization Methods with Optimal Convergence Guarantee—0
Component-Wise Natural Gradient Descent -- An Efficient Neural Network Optimization—0
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