PhD student, Department of Computer Science, University of Wisconsin - Madison
3 papers at NeurIPS 2025
We propose Vittle, a new visual instruction tuning framework that improves robustness of MLLMs to data distribution shifts by pursuing the minimal sufficient representation.
MxDs show that dense layers are more faithfully represented by mixtures of specialized sublayers than by sparsely activating neurons, while remaining just as interpretable.
