most citedEfficient Hyperparameter Optimization in Deep Learning Using a Variable Length Genetic Algorithm

82 citations · 104 across the 6 of their papers we have counts for

collaborators

6 papers

eess.IV20214 cited

Covid-19 Detection from Chest X-ray and Patient Metadata using Graph Convolutional Neural Networks

Thosini Bamunu Mudiyanselage, Nipuna Senanayake, Chunyan Ji +2

The novel corona virus (Covid-19) has introduced significant challenges due to its rapid spreading nature through respiratory transmission. As a result, there is a huge demand for…

cs.SD2021

Infant Vocal Tract Development Analysis and Diagnosis by Cry Signals with CNN Age Classification

Chunyan Ji, Yi Pan

From crying to babbling and then to speech, infant's vocal tract goes through anatomic restructuring. In this paper, we propose a non-invasive fast method of using infant cry signa…

eess.AS20211 cited

Infant Cry Classification with Graph Convolutional Networks

Chunyan Ji, Ming Chen, Bin Li +1

We propose an approach of graph convolutional networks for robust infant cry classification. We construct non-fully connected graphs based on the similarities among the relevant no…

cs.CV20204 cited

PK-GCN: Prior Knowledge Assisted Image Classification using Graph Convolution Networks

Xueli Xiao, Chunyan Ji, Thosini Bamunu Mudiyanselage +1

Deep learning has gained great success in various classification tasks. Typically, deep learning models learn underlying features directly from data, and no underlying relationship…

cs.NE202082 cited

Efficient Hyperparameter Optimization in Deep Learning Using a Variable Length Genetic Algorithm

Xueli Xiao, Ming Yan, Sunitha Basodi +2

Convolutional Neural Networks (CNN) have gained great success in many artificial intelligence tasks. However, finding a good set of hyperparameters for a CNN remains a challenging…

cs.LG202013 cited

Gradient Amplification: An efficient way to train deep neural networks

Sunitha Basodi, Chunyan Ji, Haiping Zhang +1

Improving performance of deep learning models and reducing their training times are ongoing challenges in deep neural networks. There are several approaches proposed to address the…