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Second-order methods

Use second-order statistics to process data.

Papers

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Showing 151–175 of 181 papers

TitleStatusHype
A Homogenization Approach for Gradient-Dominated Stochastic Optimization—0
Alternating direction method of multipliers for regularized multiclass support vector machines—0
A Mini-Block Fisher Method for Deep Neural Networks—0
Amortized Proximal Optimization—0
A Newton-CG based barrier method for finding a second-order stationary point of nonconvex conic optimization with complexity guarantees—0
A Novel Fast Exact Subproblem Solver for Stochastic Quasi-Newton Cubic Regularized Optimization—0
Approximate Newton Methods and Their Local Convergence—0
A scaled gradient projection method for Bayesian learning in dynamical systems—0
A survey of deep learning optimizers -- first and second order methods—0
A Survey of Optimization Methods for Training DL Models: Theoretical Perspective on Convergence and Generalization—0
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning—0
Bilinear Parameterization For Differentiable Rank-Regularization—0
Bilinear Parameterization for Non-Separable Singular Value Penalties—0
Biologically inspired protection of deep networks from adversarial attacks—0
Block-diagonal Hessian-free Optimization for Training Neural Networks—0
Component-Wise Natural Gradient Descent -- An Efficient Neural Network Optimization—0
Convolutions and More as Einsum: A Tensor Network Perspective with Advances for Second-Order Methods—0
Critical Point-Finding Methods Reveal Gradient-Flat Regions of Deep Network Losses—0
Curvature-corrected learning dynamics in deep neural networks—0
Learning Rates as a Function of Batch Size: A Random Matrix Theory Approach to Neural Network Training—0
Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization—0
Decentralized Riemannian Conjugate Gradient Method on the Stiefel Manifold—0
DDPNOpt: Differential Dynamic Programming Neural Optimizer—0
Distributed Quasi-Newton Method for Fair and Fast Federated Learning—0
Distributed Second Order Methods with Fast Rates and Compressed Communication—0
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