Zobrazeno 1 - 10
of 1 423
pro vyhledávání: '"Yang Huazhong"'
Autor:
Chen, Jiayu, Yu, Chao, Xie, Yuqing, Gao, Feng, Chen, Yinuo, Yu, Shu'ang, Tang, Wenhao, Ji, Shilong, Mu, Mo, Wu, Yi, Yang, Huazhong, Wang, Yu
Executing precise and agile flight maneuvers is critical for quadrotors in various applications. Traditional quadrotor control approaches are limited by their reliance on flat trajectories or time-consuming optimization, which restricts their flexibi
Externí odkaz:
http://arxiv.org/abs/2412.11764
Autor:
Chen, Jiayu, Yu, Chao, Li, Guosheng, Tang, Wenhao, Yang, Xinyi, Xu, Botian, Yang, Huazhong, Wang, Yu
Multi-UAV pursuit-evasion, where pursuers aim to capture evaders, poses a key challenge for UAV swarm intelligence. Multi-agent reinforcement learning (MARL) has demonstrated potential in modeling cooperative behaviors, but most RL-based approaches r
Externí odkaz:
http://arxiv.org/abs/2409.15866
Autor:
Liu, Enshu, Zhu, Junyi, Lin, Zinan, Ning, Xuefei, Blaschko, Matthew B., Yan, Shengen, Dai, Guohao, Yang, Huazhong, Wang, Yu
The rapid advancement of large language models (LLMs) has led to architectures with billions to trillions of parameters, posing significant deployment challenges due to their substantial demands on memory, processing power, and energy consumption. Sp
Externí odkaz:
http://arxiv.org/abs/2407.00945
Autor:
Fu, Tianyu, Huang, Haofeng, Ning, Xuefei, Zhang, Genghan, Chen, Boju, Wu, Tianqi, Wang, Hongyi, Huang, Zixiao, Li, Shiyao, Yan, Shengen, Dai, Guohao, Yang, Huazhong, Wang, Yu
Sparse attention can effectively mitigate the significant memory and throughput demands of Large Language Models (LLMs) in long contexts. Existing methods typically employ a uniform sparse attention mask, applying the same sparse pattern across diffe
Externí odkaz:
http://arxiv.org/abs/2406.14909
Autor:
Ning, Xuefei, Wang, Zifu, Li, Shiyao, Lin, Zinan, Yao, Peiran, Fu, Tianyu, Blaschko, Matthew B., Dai, Guohao, Yang, Huazhong, Wang, Yu
Teaching to improve student models (e.g., knowledge distillation) is an extensively studied methodology in LLMs. However, for humans, teaching improves not only students but also teachers, by fostering more rigorous and clear reasoning as well as kno
Externí odkaz:
http://arxiv.org/abs/2406.14629
Autor:
Zeng, Jinwei, Yu, Chao, Yang, Xinyi, Ao, Wenxuan, Hao, Qianyue, Yuan, Jian, Li, Yong, Wang, Yu, Yang, Huazhong
The increasingly severe congestion problem in modern cities strengthens the significance of developing city-scale traffic signal control (TSC) methods for traffic efficiency enhancement. While reinforcement learning has been widely explored in TSC, m
Externí odkaz:
http://arxiv.org/abs/2406.02126
Autor:
Zhao, Tianchen, Fang, Tongcheng, Liu, Enshu, Wan, Rui, Soedarmadji, Widyadewi, Li, Shiyao, Lin, Zinan, Dai, Guohao, Yan, Shengen, Yang, Huazhong, Ning, Xuefei, Wang, Yu
Diffusion transformers (DiTs) have exhibited remarkable performance in visual generation tasks, such as generating realistic images or videos based on textual instructions. However, larger model sizes and multi-frame processing for video generation l
Externí odkaz:
http://arxiv.org/abs/2406.02540
Autor:
Liu, Enshu, Zhu, Junyi, Lin, Zinan, Ning, Xuefei, Blaschko, Matthew B., Yekhanin, Sergey, Yan, Shengen, Dai, Guohao, Yang, Huazhong, Wang, Yu
Diffusion Models (DM) and Consistency Models (CM) are two types of popular generative models with good generation quality on various tasks. When training DM and CM, intermediate weight checkpoints are not fully utilized and only the last converged ch
Externí odkaz:
http://arxiv.org/abs/2404.02241
In recent years, there has been significant progress in the development of text-to-image generative models. Evaluating the quality of the generative models is one essential step in the development process. Unfortunately, the evaluation process could
Externí odkaz:
http://arxiv.org/abs/2403.16379
Autor:
Li, Shiyao, Ning, Xuefei, Wang, Luning, Liu, Tengxuan, Shi, Xiangsheng, Yan, Shengen, Dai, Guohao, Yang, Huazhong, Wang, Yu
Post-training quantization (PTQ) has emerged as a promising technique to reduce the cost of large language models (LLMs). Specifically, PTQ can effectively mitigate memory consumption and reduce computational overhead in LLMs. To meet the requirement
Externí odkaz:
http://arxiv.org/abs/2402.18158