35 citations · 45 across the 5 of their papers we have counts for
6 papers
Limiting fluctuation and trajectorial stability of multilayer neural networks with mean field training
Huy Tuan Pham, Phan-Minh Nguyen
The mean field (MF) theory of multilayer neural networks centers around a particular infinite-width scaling, where the learning dynamics is closely tracked by the MF limit. A rando…
Global Convergence of Three-layer Neural Networks in the Mean Field Regime
Huy Tuan Pham, Phan-Minh Nguyen
In the mean field regime, neural networks are appropriately scaled so that as the width tends to infinity, the learning dynamics tends to a nonlinear and nontrivial dynamical limit…
Analysis of feature learning in weight-tied autoencoders via the mean field lens
Phan-Minh Nguyen
Autoencoders are among the earliest introduced nonlinear models for unsupervised learning. Although they are widely adopted beyond research, it has been a longstanding open problem…
A Note on the Global Convergence of Multilayer Neural Networks in the Mean Field Regime
Huy Tuan Pham, Phan-Minh Nguyen
In a recent work, we introduced a rigorous framework to describe the mean field limit of the gradient-based learning dynamics of multilayer neural networks, based on the idea of a…
Mean Field Limit of the Learning Dynamics of Multilayer Neural Networks
Phan-Minh Nguyen
Can multilayer neural networks -- typically constructed as highly complex structures with many nonlinearly activated neurons across layers -- behave in a non-trivial way that yet s…
State Evolution for Approximate Message Passing with Non-Separable Functions
Raphael Berthier, Andrea Montanari, Phan-Minh Nguyen
Given a high-dimensional data matrix , Approximate Message Passing (AMP) algorithms construct sequences of vectors ${\boldsymbol u}^t\in{…