no code implementations • 31 May 2024 • Mohammed-Khalil Ghali, Abdelrahman Farrag, Hajar Sakai, Hicham El Baz, Yu Jin, Sarah Lam
In the rapidly evolving field of healthcare and beyond, the integration of generative AI in Electronic Health Records (EHRs) represents a pivotal advancement, addressing a critical gap in current information extraction techniques.
1 code implementation • 26 Oct 2023 • Yifei Peng, Yu Jin, Zhexu Luo, Yao-Xiang Ding, Wang-Zhou Dai, Zhong Ren, Kun Zhou
There are two levels of symbol grounding problems among the core challenges: the first is symbol assignment, i. e. mapping latent factors of neural visual generators to semantic-meaningful symbolic factors from the reasoning systems by learning from limited labeled data.
1 code implementation • 23 Aug 2023 • Chengguo Yuan, Yu Jin, Zongzhen Wu, Fanting Wei, Yangzirui Wang, Lan Chen, Xiao Wang
Additionally, a bottleneck Transformer is introduced to facilitate the fusion of the dual-stream information.
no code implementations • 18 Oct 2021 • Yongshun Zhang, Jiayi Zhang, Yu Jin, Stefano Buzzi, Bo Ai
In this paper, a general framework for deep learning-based power control methods for max-min, max-product and max-sum-rate optimization in uplink cell-free massive multiple-input multiple-output (CF mMIMO) systems is proposed.
no code implementations • 18 Jan 2021 • Yu Jin, Rui Peng, Jinfeng Wang
Protecting endangered species has been an important issue in ecology.
Dynamical Systems
1 code implementation • 20 May 2018 • Yu Jin, Joseph F. JaJa
In this work, we develop a new approach to learn graph-level representations, which includes a combination of unsupervised and supervised learning components.