Resource efficient neural networks through Hessian based pruning
Neural network pruning is a practical way for reducing the size of trained models and the number of floating-point operations (FLOPs). One way of pruning is to use the relative Hessian trace to calculate sensitivity of each channel, as compared to the more common magnitude pruning approach. However,...
Saved in:
主要作者: | Chong, Jack Huai Jie |
---|---|
其他作者: | Lihui Chen |
格式: | Final Year Project |
語言: | English |
出版: |
Nanyang Technological University
2023
|
主題: | |
在線閱讀: | https://hdl.handle.net/10356/167151 |
標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
機構: | Nanyang Technological University |
語言: | English |
相似書籍
-
Building highly efficient neural networks through weight pruning
由: Low, Xuan Hui
出版: (2022) -
Network pruning for visual food recognition
由: Yao, Yujie
出版: (2020) -
Towards efficient convolutional neural network for embedded hardware via multi-dimensional pruning
由: Kong, Hao, et al.
出版: (2023) -
Stock selection using General Growing and Pruning Radial Basis Function (GGAP-RBF) neural network
由: Ng, Wee Ding.
出版: (2010) -
Robust neural training and pruning algorithms for a class of nonlinear tracking control systems
由: Ni, Jie
出版: (2008)