Zobrazeno 1 - 10
of 907
pro vyhledávání: '"Wang Xiangfeng"'
Autor:
He, Yuchen, Wang, Xiangfeng
Federated learning is a specific distributed learning paradigm in which a central server aggregates updates from multiple clients' local models, thereby enabling the server to learn without requiring clients to upload their private data, maintaining
Externí odkaz:
http://arxiv.org/abs/2411.01916
Federated continual learning (FCL) aims to learn from sequential data stream in the decentralized federated learning setting, while simultaneously mitigating the catastrophic forgetting issue in classical continual learning. Existing FCL methods usua
Externí odkaz:
http://arxiv.org/abs/2411.01904
Interactive medical image segmentation (IMIS) has shown significant potential in enhancing segmentation accuracy by integrating iterative feedback from medical professionals. However, the limited availability of enough 3D medical data restricts the g
Externí odkaz:
http://arxiv.org/abs/2408.02635
With the rising popularity of Transformer-based large language models (LLMs), reducing their high inference costs has become a significant research focus. One effective approach is to compress the long input contexts. Existing methods typically lever
Externí odkaz:
http://arxiv.org/abs/2406.13618
Autor:
Shen, Chuyun, Li, Wenhao, Chen, Haoqing, Wang, Xiaoling, Zhu, Fengping, Li, Yuxin, Wang, Xiangfeng, Jin, Bo
Radiologists must utilize multiple modal images for tumor segmentation and diagnosis due to the limitations of medical imaging and the diversity of tumor signals. This leads to the development of multimodal learning in segmentation. However, the redu
Externí odkaz:
http://arxiv.org/abs/2401.02717
Autor:
Sheng, Junjie, Huang, Zixiao, Shen, Chuyun, Li, Wenhao, Hua, Yun, Jin, Bo, Zha, Hongyuan, Wang, Xiangfeng
The formidable capacity for zero- or few-shot decision-making in language agents encourages us to pose a compelling question: Can language agents be alternatives to PPO agents in traditional sequential decision-making tasks? To investigate this, we f
Externí odkaz:
http://arxiv.org/abs/2312.03290
The Segmentation Anything Model (SAM) has recently emerged as a foundation model for addressing image segmentation. Owing to the intrinsic complexity of medical images and the high annotation cost, the medical image segmentation (MIS) community has b
Externí odkaz:
http://arxiv.org/abs/2306.08958
Over-generalization is a thorny issue in cognitive science, where people may become overly cautious due to past experiences. Agents in multi-agent reinforcement learning (MARL) also have been found to suffer relative over-generalization (RO) as peopl
Externí odkaz:
http://arxiv.org/abs/2306.05353
The difficulty of appropriately assigning credit is particularly heightened in cooperative MARL with sparse reward, due to the concurrent time and structural scales involved. Automatic subgoal generation (ASG) has recently emerged as a viable MARL ap
Externí odkaz:
http://arxiv.org/abs/2305.10865
In multi-agent reinforcement learning, each agent acts to maximize its individual accumulated rewards. Nevertheless, individual accumulated rewards could not fully reflect how others perceive them, resulting in selfish behaviors that undermine global
Externí odkaz:
http://arxiv.org/abs/2305.06227