Speeding up deep neural network training with decoupled and analytic learning
Training deep neural networks usually demands a significantly long period of time. In this thesis, we explore methods in two different areas, i.e., decoupled learning and analytic learning, in order to reduce the training time. In decoupled learning, new methods are proposed to alleviate the sequ...
Saved in:
Main Author: | Zhuang, Huiping |
---|---|
Other Authors: | Lin Zhiping |
Format: | Thesis-Doctor of Philosophy |
Language: | English |
Published: |
Nanyang Technological University
2021
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/153079 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Decoupled neural network training with re-computation and weight prediction
by: Peng, Jiawei, et al.
Published: (2023) -
Fully decoupled neural network learning using delayed gradients
by: Zhuang, Huiping, et al.
Published: (2024) -
Deep learning neural network for image processing
by: Ma, Xueqing
Published: (2020) -
Learn to navigate through deep neural networks
by: Wu, Keyu
Published: (2020) -
Auxiliary network design for local learning in deep neural networks
by: Peng, Jiawei
Published: (2021)