Efficient Multimedia Representation and Processing for Artificial Intelligence on Computing Edges

Project Member(s): Zhang, G., Xu, M.

Funding or Partner Organisation: Nippon Telegraph and Telephone Corporation
Nippon Telegraph and Telephone Corporation

Start year: 2021

Publications:

Zhang, H, Xu, M, Zhang, G & Niwa, K 2024, 'SSFG: Stochastically Scaling Features and Gradients for Regularizing Graph Convolutional Networks', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 2, pp. 2223-2234.
View/Download from: Publisher's site

FOR Codes: Expanding Knowledge in the Mathematical Sciences, Knowledge Representation and Machine Learning, Machine learning

Acknowledgement of Country

UTS acknowledges the Gadigal people of the Eora Nation, the Boorooberongal people of the Dharug Nation, the Bidiagal people and the Gamaygal people, upon whose ancestral lands our university stands. We would also like to pay respect to the Elders both past and present, acknowledging them as the traditional custodians of knowledge for these lands.