Zobrazeno 1 - 10
of 4 012
pro vyhledávání: '"Yi, Qi"'
We propose a geometric series modulated non-Hermitian quasiperiodic lattice model, and explore its localization and topological properties. The results show that with the ever-increasing summation terms of the geometric series, multiple mobility edge
Externí odkaz:
http://arxiv.org/abs/2410.04469
Autor:
Wang, Chen-Yu, Yin, Yi-Han Iris, Zhang, Bin-Bin, Feng, Hua, Zeng, Ming, Xiong, Shao-Lin, Pan, Xiao-Fan, Yang, Jun, Zhang, Yan-Qiu, Li, Chen, Yan, Zhen-Yu, Wang, Chen-Wei, Zheng, Xu-Tao, Liu, Jia-Cong, Wang, Qi-Dong, Yang, Zi-Rui, Li, Long-Hao, Liu, Qi-Ze, Zhao, Zheng-Yang, Hu, Bo, Liu, Yi-Qi, Lu, Si-Yuan, Luo, Zi-You, Cang, Ji-Rong, Cao, De-Zhi, Han, Wen-Tao, Jia, Li-Ping, Pan, Xing-Yu, Tian, Yang, Xu, Ben-Da, Yang, Xiao, Zeng, Zhi
GRB 230812B, detected by the Gamma-Ray Integrated Detectors (GRID) constellation mission, is an exceptionally bright gamma-ray burst (GRB) with a duration of only 3 seconds. Sitting near the traditional boundary ($\sim$ 2 s) between long and short GR
Externí odkaz:
http://arxiv.org/abs/2409.12613
Autor:
Gao, Haihan, Zhang, Rui, Yi, Qi, Yao, Hantao, Li, Haochen, Guo, Jiaming, Peng, Shaohui, Gao, Yunkai, Wang, QiCheng, Hu, Xing, Wen, Yuanbo, Zhang, Zihao, Du, Zidong, Li, Ling, Guo, Qi, Chen, Yunji
Overfitting in RL has become one of the main obstacles to applications in reinforcement learning(RL). Existing methods do not provide explicit semantic constrain for the feature extractor, hindering the agent from learning a unified cross-domain repr
Externí odkaz:
http://arxiv.org/abs/2406.03250
Autor:
Tang, Yehui, Liu, Fangcheng, Ni, Yunsheng, Tian, Yuchuan, Bai, Zheyuan, Hu, Yi-Qi, Liu, Sichao, Jui, Shangling, Han, Kai, Wang, Yunhe
The power of large language models (LLMs) has been demonstrated through numerous data and computing resources. However, the application of language models on mobile devices is facing huge challenge on the computation and memory costs, that is, tiny l
Externí odkaz:
http://arxiv.org/abs/2402.02791
Autor:
Guo, Yuxuan, Hao, Yifan, Zhang, Rui, Zhou, Enshuai, Du, Zidong, Zhang, Xishan, Song, Xinkai, Wen, Yuanbo, Zhao, Yongwei, Zhou, Xuehai, Guo, Jiaming, Yi, Qi, Peng, Shaohui, Huang, Di, Chen, Ruizhi, Guo, Qi, Chen, Yunji
Research on emergent communication between deep-learning-based agents has received extensive attention due to its inspiration for linguistics and artificial intelligence. However, previous attempts have hovered around emerging communication under per
Externí odkaz:
http://arxiv.org/abs/2311.04474
Autor:
Gao, Yunkai, Zhang, Rui, Guo, Jiaming, Wu, Fan, Yi, Qi, Peng, Shaohui, Lan, Siming, Chen, Ruizhi, Du, Zidong, Hu, Xing, Guo, Qi, Li, Ling, Chen, Yunji
Offline meta-reinforcement learning (OMRL) utilizes pre-collected offline datasets to enhance the agent's generalization ability on unseen tasks. However, the context shift problem arises due to the distribution discrepancy between the contexts used
Externí odkaz:
http://arxiv.org/abs/2311.03695
Autor:
Lan, Siming, Zhang, Rui, Yi, Qi, Guo, Jiaming, Peng, Shaohui, Gao, Yunkai, Wu, Fan, Chen, Ruizhi, Du, Zidong, Hu, Xing, Zhang, Xishan, Li, Ling, Chen, Yunji
In the field of multi-task reinforcement learning, the modular principle, which involves specializing functionalities into different modules and combining them appropriately, has been widely adopted as a promising approach to prevent the negative tra
Externí odkaz:
http://arxiv.org/abs/2311.01075
Autor:
Guo, Jiaming, Zhang, Rui, Peng, Shaohui, Yi, Qi, Hu, Xing, Chen, Ruizhi, Du, Zidong, Zhang, Xishan, Li, Ling, Guo, Qi, Chen, Yunji
Deep reinforcement learning (DRL) has led to a wide range of advances in sequential decision-making tasks. However, the complexity of neural network policies makes it difficult to understand and deploy with limited computational resources. Currently,
Externí odkaz:
http://arxiv.org/abs/2311.02104
Autor:
Zheng, Chao, Zhang, Yan-Qiu, Xiong, Shao-Lin, Li, Cheng-Kui, Gao, He, Xue, Wang-Chen, Liu, Jia-Cong, Wang, Chen-Wei, Tan, Wen-Jun, Peng, Wen-Xi, An, Zheng-Hua, Cai, Ce, Ge, Ming-Yu, Guo, Dong-Ya, Huang, Yue, Li, Bing, Li, Ti-Pei, Li, Xiao-Bo, Li, Xin-Qiao, Li, Xu-Fang, Liao, Jin-Yuan, Liu, Cong-Zhan, Lu, Fang-Jun, Ma, Xiang, Qiao, Rui, Song, Li-Ming, Wang, Jin, Wang, Ping, Wang, Xi-Lu, Wang, Yue, Wen, Xiang-Yang, Xiao, Shuo, Xu, Yan-Bing, Xu, Yu-Peng, Yao, Zhi-Guo, Yi, Qi-Bing, Yi, Shu-Xu, You, Yuan, Zhang, Fan, Zhang, Jin-Peng, Zhang, Peng, Zhang, Shu, Zhang, Shuang-Nan, Zhang, Yan-Ting, Zhang, Zhen, Zhao, Xiao-Yun, Zhao, Yi, Zheng, Shi-Jie
The early afterglow of a Gamma-ray burst (GRB) can provide critical information on the jet and progenitor of the GRB. The extreme brightness of GRB 221009A allows us to probe its early afterglow in unprecedented detail. In this letter, we report comp
Externí odkaz:
http://arxiv.org/abs/2310.10522
Autor:
Peng, Shaohui, Hu, Xing, Yi, Qi, Zhang, Rui, Guo, Jiaming, Huang, Di, Tian, Zikang, Chen, Ruizhi, Du, Zidong, Guo, Qi, Chen, Yunji, Li, Ling
Large language models (LLMs) show their powerful automatic reasoning and planning capability with a wealth of semantic knowledge about the human world. However, the grounding problem still hinders the applications of LLMs in the real-world environmen
Externí odkaz:
http://arxiv.org/abs/2309.01352