2 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.LG2024
Fundamental Limits of Prompt Tuning Transformers: Universality, Capacity and Efficiency
Jerry Yao-Chieh Hu, Wei-Po Wang, Ammar Gilani +3
We investigate the statistical and computational limits of prompt tuning for transformer-based foundation models. Our key contributions are prompt tuning on \emph{single-head} tran…
cs.LG2024★ 2 cited
Outlier-Efficient Hopfield Layers for Large Transformer-Based Models
Jerry Yao-Chieh Hu, Pei-Hsuan Chang, Robin Luo +4
We introduce an Outlier-Efficient Modern Hopfield Model (termed ) and use it to address the outlier inefficiency problem of {training} gigantic transformer-base…