Training a Two-Layer ReLU Network Analytically
Neural networks are usually trained with different variants of gradient descent-based optimization algorithms such as the stochastic gradient descent or the Adam optimizer. Recent theoretical work states that the critical points (where the gradient of the loss is zero) of two-layer ReLU networks wit...
Main Author: | Adrian Barbu |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-04-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/23/8/4072 |
Similar Items
-
Gaussian Perturbations in ReLU Networks and the Arrangement of Activation Regions
by: Bálint Daróczy
Published: (2022-03-01) -
Locally linear attributes of ReLU neural networks
by: Ben Sattelberg, et al.
Published: (2023-11-01) -
On the Generative Power of ReLU Network for Generating Similar Strings
by: Mamoona Ghafoor, et al.
Published: (2024-01-01) -
Integrating geometries of ReLU feedforward neural networks
by: Yajing Liu, et al.
Published: (2023-11-01) -
Accelerated analysis on the triple momentum method for a two-layer ReLU neural network
by: Xin Li, et al.
Published: (2024-04-01)