hessian

3 papers

• Finding Low-Rank Matrix Weights in DNNs via Riemannian Optimization: RAdaGrad and RAdamW
• Local Curvature Descent: Squeezing More Curvature out of Standard and Polyak Gradient Descent
• Nearly Dimension-Independent Convergence of Mean-Field Black-Box Variational Inference