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Second-order methods
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Use second-order statistics to process data.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
KOALA++: Efficient Kalman-Based Optimization of Neural Networks with Gradient-Covariance Products
Accelerated Training of Federated Learning via Second-Order Methods
Towards Practical Second-Order Optimizers in Deep Learning: Insights from Fisher Information Analysis
Representation Meets Optimization: Training PINNs and PIKANs for Gray-Box Discovery in Systems Pharmacology
FedOSAA: Improving Federated Learning with One-Step Anderson Acceleration
SASSHA: Sharpness-aware Adaptive Second-order Optimization with Stable Hessian Approximation
Online Covariance Matrix Estimation in Sketched Newton Methods
A Survey of Optimization Methods for Training DL Models: Theoretical Perspective on Convergence and Generalization
Distributed Quasi-Newton Method for Fair and Fast Federated Learning
GP-FL: Model-Based Hessian Estimation for Second-Order Over-the-Air Federated Learning
A Learn-to-Optimize Approach for Coordinate-Wise Step Sizes for Quasi-Newton Methods
Gradient Norm Regularization Second-Order Algorithms for Solving Nonconvex-Strongly Concave Minimax Problems
Don't Be So Positive: Negative Step Sizes in Second-Order Methods
Exact, Tractable Gauss-Newton Optimization in Deep Reversible Architectures Reveal Poor Generalization
Improving Stochastic Cubic Newton with Momentum
Second-Order Min-Max Optimization with Lazy Hessians
Unlocking FedNL: Self-Contained Compute-Optimized Implementation
Adversarial Vulnerability as a Consequence of On-Manifold Inseparibility
FLeNS: Federated Learning with Enhanced Nesterov-Newton Sketch
Alternating Iteratively Reweighted $\ell_1$ and Subspace Newton Algorithms for Nonconvex Sparse Optimization
Fed-Sophia: A Communication-Efficient Second-Order Federated Learning Algorithm
Revisiting Scalable Hessian Diagonal Approximations for Applications in Reinforcement Learning
Adaptive and Optimal Second-order Optimistic Methods for Minimax Optimization
Stochastic Newton Proximal Extragradient Method
Kronecker-Factored Approximate Curvature for Physics-Informed Neural Networks
A Gauss-Newton Approach for Min-Max Optimization in Generative Adversarial Networks
Inverse-Free Fast Natural Gradient Descent Method for Deep Learning
SGD with Partial Hessian for Deep Neural Networks Optimization
Second Order Methods for Bandit Optimization and Control
The Challenges of the Nonlinear Regime for Physics-Informed Neural Networks
Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspective
On The Temporal Domain of Differential Equation Inspired Graph Neural Networks
Krylov Cubic Regularized Newton: A Subspace Second-Order Method with Dimension-Free Convergence Rate
Structured Inverse-Free Natural Gradient: Memory-Efficient & Numerically-Stable KFAC
Adapting Newton's Method to Neural Networks through a Summary of Higher-Order Derivatives
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