12 citations · 17 across the 7 of their papers we have counts for
5 papers · 1 filter
Neural Scaling Laws of Transformer Operator Network
Haoran Yan, Zhongjie Shi, Yuanzhe Xi +2
Transformers have emerged as powerful architectures for learning solution operators of physical systems. Empirically the prediction error has been observed to decrease when the dat…
Learning Theory of Transformers: Local-to-Global Approximation via Softmax Partition of Unity
Zhongjie Shi, Wenjing Liao
This paper investigates the learning theory of Transformer networks for regression tasks on the compact Euclidean domain and -dimensional compact Riemannian manifolds.…
Nonlinear functional regression by functional deep neural network with kernel embedding
Zhongjie Shi, Jun Fan, Linhao Song +2
Recently, deep learning has been widely applied in functional data analysis (FDA) with notable empirical success. However, the infinite dimensionality of functional data necessitat…
Learning Theory of Distribution Regression with Neural Networks
Zhongjie Shi, Zhan Yu, Ding-Xuan Zhou
In this paper, we aim at establishing an approximation theory and a learning theory of distribution regression via a fully connected neural network (FNN). In contrast to the classi…
Distributed Gradient Descent for Functional Learning
Zhan Yu, Jun Fan, Zhongjie Shi +1
In recent years, different types of distributed and parallel learning schemes have received increasing attention for their strong advantages in handling large-scale data informatio…