H. T. Kung

Adaptive tensor product computation slashes MLIP runtime by $2.4\times$ and memory by $2.6\times$ without sacrificing accuracy, revealing computational depth's link to chemical complexity.

ReRound outperforms standard quantization techniques by resolving midpoint ambiguity, achieving superior accuracy in low-bit weight quantization for small LLMs.

Achieve state-of-the-art personalized gaze estimation by intelligently reweighting pre-trained features, rather than learning new ones from scratch.