Low-Rank Adaptation (LoRA), which updates the dense neural network layers with pluggable low-rank matrices, is one of the best performed parameter efficient fine-tuning paradigms. Furthermore, it has ...

This project develops noise-resilient and generalisable AI models by combining low-rank adaptation and manifold learning to jointly model data, parameters, and uncertainty, enabling robust, efficient ...