4 papers · 1 filter
Transformers Simulate MLE for Sequence Generation in Bayesian Networks
Yuan Cao, Yihan He, Dennis Wu +3
Transformers have achieved significant success in various fields, notably excelling in tasks involving sequential data like natural language processing. Despite these achievements,…
Transformers versus the EM Algorithm in Multi-class Clustering
Yihan He, Hong-Yu Chen, Yuan Cao +2
LLMs demonstrate significant inference capacities in complicated machine learning tasks, using the Transformer model as its backbone. Motivated by the limited understanding of such…
Learning Spectral Methods by Transformers
Yihan He, Yuan Cao, Hong-Yu Chen +3
Transformers demonstrate significant advantages as the building block of modern LLMs. In this work, we study the capacities of Transformers in performing unsupervised learning. We…
Global Convergence in Training Large-Scale Transformers
Cheng Gao, Yuan Cao, Zihao Li +5
Despite the widespread success of Transformers across various domains, their optimization guarantees in large-scale model settings are not well-understood. This paper rigorously an…