<ul data-eligibleForWebStory="false"><li>COALA is a new framework for context-aware low-rank approximation in neural networks, aiming to overcome numerical instabilities seen in existing methods.</li><li>Existing methods rely on classical formulas that can lead to degraded approximation quality or numerically singular matrices.</li><li>COALA proposes an inversion-free regularized framework based on stable decompositions to address these limitations.</li><li>The method is capable of handling challenging scenarios like large calibration matrices, nearly singular activation matrices, and insufficient data for unique approximation, providing explicit error bounds.</li></ul>

COALA: Numerically Stable and Efficient Framework for Context-Aware Low-Rank Approximation

Discover more