1 code implementation • 17 Dec 2023 • Zheng Zhang, Sirui Li, Jingcheng Zhou, Junxiang Wang, Abhinav Angirekula, Allen Zhang, Liang Zhao
Besides, existing spatial network representation learning methods can only consider networks embedded in Euclidean space, and can not well exploit the rich geometric information carried by irregular and non-uniform non-Euclidean space.
no code implementations • 11 Jun 2022 • Jingcheng Zhou, Wei Wei, Xing Li, Bowen Pang, Zhiming Zheng
Deep learning utilizing deep neural networks (DNNs) has achieved a lot of success recently in many important areas such as computer vision, natural language processing, and recommendation systems.
no code implementations • 31 Mar 2021 • Jingcheng Zhou, Wei Wei, Zhiming Zheng
First-order methods like stochastic gradient descent(SGD) are recently the popular optimization method to train deep neural networks (DNNs), but second-order methods are scarcely used because of the overpriced computing cost in getting the high-order information.