1 citations · 1 across the 4 of their papers we have counts for
4 papers
GARA: A novel approach to Improve Genetic Algorithms' Accuracy and Efficiency by Utilizing Relationships among Genes
Zhaoning Shi, Meng Xiang, Zhaoyang Hai +2
Genetic algorithms have played an important role in engineering optimization. Traditional GAs treat each gene separately. However, biophysical studies of gene regulatory networks r…
Generating meta-learning tasks to evolve parametric loss for classification learning
Zhaoyang Hai, Xiabi Liu, Yuchen Ren +1
The field of meta-learning has seen a dramatic rise in interest in recent years. In existing meta-learning approaches, learning tasks for training meta-models are usually collected…
Mining the Weights Knowledge for Optimizing Neural Network Structures
Mengqiao Han, Xiabi Liu, Zhaoyang Hai +1
Knowledge embedded in the weights of the artificial neural network can be used to improve the network structure, such as in network compression. However, the knowledge is set up by…
Evolving parametrized Loss for Image Classification Learning on Small Datasets
Zhaoyang Hai, Xiabi Liu
This paper proposes a meta-learning approach to evolving a parametrized loss function, which is called Meta-Loss Network (MLN), for training the image classification learning on sm…