2 citations · 3 across the 16 of their papers we have counts for
4 papers · 1 filter
Representation and Regression Problems in Neural Networks: Relaxation, Generalization, and Numerics
Kang Liu, Enrique Zuazua
In this work, we address three non-convex optimization problems associated with the training of shallow neural networks (NNs) for exact and approximate representation, as well as f…
Constructive Universal Approximation and Finite Sample Memorization by Narrow Deep ReLU Networks
Martín Hernández, Enrique Zuazua
We present a fully constructive analysis of deep ReLU neural networks for classification and function approximation tasks. First, we prove that any dataset with distinct points…
Universal Approximation of Dynamical Systems by Semi-Autonomous Neural ODEs and Applications
Ziqian Li, Kang Liu, Lorenzo Liverani +1
In this paper, we introduce semi-autonomous neural ordinary differential equations (SA-NODEs), a variation of the vanilla NODEs, employing fewer parameters. We investigate the univ…
Interplay between depth and width for interpolation in neural ODEs
Antonio Álvarez-López, Arselane Hadj Slimane, Enrique Zuazua
Neural ordinary differential equations (neural ODEs) have emerged as a natural tool for supervised learning from a control perspective, yet a complete understanding of their optima…