no code implementations • 4 Mar 2024 • Yudi Zhang, Qi Xu, Lei Zhang
Creating 3D textured meshes using generative artificial intelligence has garnered significant attention recently.
no code implementations • 17 Feb 2024 • Wenxuan Wang, Yihang Su, Jingyuan Huan, Jie Liu, WenTing Chen, Yudi Zhang, Cheng-Yi Li, Kao-Jung Chang, Xiaohan Xin, Linlin Shen, Michael R. Lyu
However, these models are often evaluated on benchmarks that are unsuitable for the Med-MLLMs due to the intricate nature of the real-world diagnostic frameworks, which encompass diverse medical specialties and involve complex clinical decisions.
1 code implementation • 17 Jan 2024 • Meng Fang, Shilong Deng, Yudi Zhang, Zijing Shi, Ling Chen, Mykola Pechenizkiy, Jun Wang
A wide range of real-world applications is characterized by their symbolic nature, necessitating a strong capability for symbolic reasoning.
no code implementations • 6 Dec 2023 • Ziyan Wang, Yali Du, Yudi Zhang, Meng Fang, Biwei Huang
Offline Multi-agent Reinforcement Learning (MARL) is valuable in scenarios where online interaction is impractical or risky.
1 code implementation • 14 Jul 2023 • Libo Qin, Shijue Huang, Qiguang Chen, Chenran Cai, Yudi Zhang, Bin Liang, Wanxiang Che, Ruifeng Xu
Multi-modal sarcasm detection has attracted much recent attention.
no code implementations • 12 Jul 2023 • Qiying Yu, Yudi Zhang, Yuyan Ni, Shikun Feng, Yanyan Lan, Hao Zhou, Jingjing Liu
Self-supervised learning has recently gained growing interest in molecular modeling for scientific tasks such as AI-assisted drug discovery.
no code implementations • NeurIPS 2023 • Yudi Zhang, Yali Du, Biwei Huang, Ziyan Wang, Jun Wang, Meng Fang, Mykola Pechenizkiy
While the majority of current approaches construct the reward redistribution in an uninterpretable manner, we propose to explicitly model the contributions of state and action from a causal perspective, resulting in an interpretable reward redistribution and preserving policy invariance.
no code implementations • 7 Apr 2023 • Fangwei Zhong, Xiao Bi, Yudi Zhang, Wei zhang, Yizhou Wang
However, building a generalizable active tracker that works robustly across different scenarios remains a challenge, especially in unstructured environments with cluttered obstacles and diverse layouts.