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.