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cs.LG2024
In-Context Learning with Representations: Contextual Generalization of Trained Transformers
Tong Yang, Yu Huang, Yingbin Liang +1
In-context learning (ICL) refers to a remarkable capability of pretrained large language models, which can learn a new task given a few examples during inference. However, theoreti…
cs.LG2024★ 5 cited
Accelerating Convergence of Score-Based Diffusion Models, Provably
Gen Li, Yu Huang, Timofey Efimov +3
Score-based diffusion models, while achieving remarkable empirical performance, often suffer from low sampling speed, due to extensive function evaluations needed during the sampli…
cs.LG2024
A Theoretical Analysis of Self-Supervised Learning for Vision Transformers
Yu Huang, Zixin Wen, Yuejie Chi +1
Self-supervised learning has become a cornerstone in computer vision, primarily divided into reconstruction-based methods like masked autoencoders (MAE) and discriminative methods…