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Found 3 papers, 1 papers with code
Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models
This necessitates a new branch of research focusing on the parameter-efficient adaptation of PLMs, dubbed as delta tuning in this paper.
Stochastic Anderson Mixing for Nonconvex Stochastic Optimization
Anderson mixing (AM) is an acceleration method for fixed-point iterations.
A Class of Short-term Recurrence Anderson Mixing Methods and Their Applications
We prove that the basic version of ST-AM is equivalent to the full-memory AM in strongly convex quadratic optimization, and with minor changes it has local linear convergence for solving general nonlinear fixed-point problems.
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