3 citations · 5 across the 7 of their papers we have counts for
4 papers · 1 filter
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…
Inverting the Leverage Score Gradient: An Efficient Approximate Newton Method
Chenyang Li, Zhao Song, Zhaoxing Xu +1
Leverage scores have become essential in statistics and machine learning, aiding regression analysis, randomized matrix computations, and various other tasks. This paper delves int…
Exploring the Frontiers of Softmax: Provable Optimization, Applications in Diffusion Model, and Beyond
Yang Cao, Yingyu Liang, Zhenmei Shi +1
The softmax activation function plays a crucial role in the success of large language models (LLMs), particularly in the self-attention mechanism of the widely adopted Transformer…
Fourier Circuits in Neural Networks and Transformers: A Case Study of Modular Arithmetic with Multiple Inputs
Chenyang Li, Yingyu Liang, Zhenmei Shi +2
In the evolving landscape of machine learning, a pivotal challenge lies in deciphering the internal representations harnessed by neural networks and Transformers. Building on recen…