Trung Le

Geometry-Aware Bayesian Parameter-Efficient Fine-Tuning on the Stiefel Manifold via Stein Variational Gradient Descent

This paper builds on the singular value decomposition factorization of adapters to develop a framework based on Stein variational gradient descent (SVGD), which delivers strong model calibration and attains higher prediction accuracy than SVGD and related uncertainty estimation methods that are formulated in Euclidean...