1 citations · 1 across the 9 of their papers we have counts for
9 papers
A Theory on Flow Matching with Neural Networks
Yihan He, Qishuo Yin, Yuan Cao +2
In this work, we develop theoretical foundation for flow matching with neural-network-parameterized conditional velocity fields. We establish convergence guarantees for gradient de…
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…
Transformers and Their Roles as Time Series Foundation Models
Dennis Wu, Yihan He, Yuan Cao +2
We give a comprehensive analysis of transformers as time series foundation models, focusing on their approximation and generalization capabilities. First, we demonstrate that there…
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…
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,…
One-Layer Transformer Provably Learns One-Nearest Neighbor In Context
Zihao Li, Yuan Cao, Cheng Gao +5
Transformers have achieved great success in recent years. Interestingly, transformers have shown particularly strong in-context learning capability -- even without fine-tuning, the…
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…
Hidden Clique Inference in Random Ising Model I: the planted random field Curie-Weiss model
Yihan He, Han Liu, Jianqing Fan
We study the problem of testing and recovering the hidden -clique Ferromagnetic correlation in the planted Random Field Curie-Weiss model (a.k.a. the pRFCW model). The pRFCW mod…
Hidden Clique Inference in Random Ising Model II: the planted Sherrington-Kirkpatrick model
Yihan He, Han Liu, Jianqing Fan
We study the problem of testing and recovering -clique Ferromagnetic mean shift in the planted Sherrington-Kirkpatrick model (i.e., a type of spin glass model) with spins. T…