pith. machine review for the scientific record. sign in

arxiv: 1807.00297 · v1 · submitted 2018-07-01 · 💻 cs.LG · stat.ML

Recognition: unknown

Exponential Convergence of the Deep Neural Network Approximation for Analytic Functions

Weinan E , Qingcan Wang
Authors on Pith no claims yet
classification 💻 cs.LG stat.ML
keywords analyticapproximationconvergencedeepexponentialfunctionsnetworkneural
0
0 comments X
read the original abstract

We prove that for analytic functions in low dimension, the convergence rate of the deep neural network approximation is exponential.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. A Composite Activation Function for Learning Stable Binary Representations

    cs.LG 2026-05 unverdicted novelty 5.0

    HTAF is a sigmoid-tanh composite that approximates the Heaviside function to allow stable gradient training of binary activation networks, yielding ICBMs with stable discretization and competitive performance on image tasks.