AdaGram applies a dynamical low-rank integrator to the preconditioner of adaptive gradient methods, achieving full-matrix-like updates with rank 1-5 approximations at low cost on small GLM benchmarks.
A differential equation for modeling nesterov’s accelerated gradient method: Theory and insights
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Dynamic Low-rank Approximation of Full-Matrix Preconditioner for Training Generalized Linear Models
AdaGram applies a dynamical low-rank integrator to the preconditioner of adaptive gradient methods, achieving full-matrix-like updates with rank 1-5 approximations at low cost on small GLM benchmarks.