Zobrazeno 1 - 10
of 3 279
pro vyhledávání: '"ZHANG Liwen"'
Current research on tool learning primarily focuses on selecting the most effective tool from a wide array of options, often overlooking cost-effectiveness, a crucial factor in human problem-solving. In this paper, we address the selection of homogen
Externí odkaz:
http://arxiv.org/abs/2406.12429
Autor:
Fu, Ying, Li, Yu, You, Shaodi, Shi, Boxin, Chen, Linwei, Zou, Yunhao, Wang, Zichun, Li, Yichen, Han, Yuze, Zhang, Yingkai, Wang, Jianan, Liu, Qinglin, Yu, Wei, Lv, Xiaoqian, Li, Jianing, Zhang, Shengping, Ji, Xiangyang, Chen, Yuanpei, Zhang, Yuhan, Peng, Weihang, Zhang, Liwen, Xu, Zhe, Gou, Dingyong, Li, Cong, Xu, Senyan, Zhang, Yunkang, Jiang, Siyuan, Lu, Xiaoqiang, Jiao, Licheng, Liu, Fang, Liu, Xu, Li, Lingling, Ma, Wenping, Yang, Shuyuan, Xie, Haiyang, Zhao, Jian, Huang, Shihua, Cheng, Peng, Shen, Xi, Wang, Zheng, An, Shuai, Zhu, Caizhi, Li, Xuelong, Zhang, Tao, Li, Liang, Liu, Yu, Yan, Chenggang, Zhang, Gengchen, Jiang, Linyan, Song, Bingyi, An, Zhuoyu, Lei, Haibo, Luo, Qing, Song, Jie, Liu, Yuan, Li, Qihang, Zhang, Haoyuan, Wang, Lingfeng, Chen, Wei, Luo, Aling, Li, Cheng, Cao, Jun, Chen, Shu, Dou, Zifei, Liu, Xinyu, Zhang, Jing, Zhang, Kexin, Yang, Yuting, Gou, Xuejian, Wang, Qinliang, Liu, Yang, Zhao, Shizhan, Zhang, Yanzhao, Yan, Libo, Guo, Yuwei, Li, Guoxin, Gao, Qiong, Che, Chenyue, Sun, Long, Chen, Xiang, Li, Hao, Pan, Jinshan, Xie, Chuanlong, Chen, Hongming, Li, Mingrui, Deng, Tianchen, Huang, Jingwei, Li, Yufeng, Wan, Fei, Xu, Bingxin, Cheng, Jian, Liu, Hongzhe, Xu, Cheng, Zou, Yuxiang, Pan, Weiguo, Dai, Songyin, Jia, Sen, Zhang, Junpei, Chen, Puhua
The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies. By leveraging the principles of physics to inform and enhance deep learning models, we can develop more robust and ac
Externí odkaz:
http://arxiv.org/abs/2406.10744
A core challenge in survival analysis is to model the distribution of censored time-to-event data, where the event of interest may be a death, failure, or occurrence of a specific event. Previous studies have showed that ranking and maximum likelihoo
Externí odkaz:
http://arxiv.org/abs/2401.02708
Autor:
Luo, Yin, Kong, Qingchao, Xu, Nan, Cao, Jia, Hao, Bao, Qu, Baoyu, Chen, Bo, Zhu, Chao, Zhao, Chenyang, Zhang, Donglei, Feng, Fan, Zhao, Feifei, Sun, Hailong, Yang, Hanxuan, Pan, Haojun, Liu, Hongyu, Guo, Jianbin, Du, Jiangtao, Wang, Jingyi, Li, Junfeng, Sun, Lei, Liu, Liduo, Dong, Lifeng, Liu, Lili, Wang, Lin, Zhang, Liwen, Wang, Minzheng, Wang, Pin, Yu, Ping, Li, Qingxiao, Yan, Rui, Zou, Rui, Li, Ruiqun, Huang, Taiwen, Wang, Xiaodong, Wu, Xiaofei, Peng, Xin, Zhang, Xina, Fang, Xing, Xiao, Xinglin, Hao, Yanni, Dong, Yao, Wang, Yigang, Liu, Ying, Jiang, Yongyu, Wang, Yungan, Wang, Yuqi, Wang, Zhangsheng, Yu, Zhaoxin, Luo, Zhen, Mao, Wenji, Wang, Lei, Zeng, Dajun
As the latest advancements in natural language processing, large language models (LLMs) have achieved human-level language understanding and generation abilities in many real-world tasks, and even have been regarded as a potential path to the artific
Externí odkaz:
http://arxiv.org/abs/2312.14862
Autor:
Zhang, Liwen, Cai, Weige, Liu, Zhaowei, Yang, Zhi, Dai, Wei, Liao, Yujie, Qin, Qianru, Li, Yifei, Liu, Xingyu, Liu, Zhiqiang, Zhu, Zhoufan, Wu, Anbo, Guo, Xin, Chen, Yun
Large language models (LLMs) have demonstrated exceptional performance in various natural language processing tasks, yet their efficacy in more challenging and domain-specific tasks remains largely unexplored. This paper presents FinEval, a benchmark
Externí odkaz:
http://arxiv.org/abs/2308.09975
Autor:
Guo, Yufei, Zhang, Yuhan, Chen, Yuanpei, Peng, Weihang, Liu, Xiaode, Zhang, Liwen, Huang, Xuhui, Ma, Zhe
As one of the energy-efficient alternatives of conventional neural networks (CNNs), spiking neural networks (SNNs) have gained more and more interest recently. To train the deep models, some effective batch normalization (BN) techniques are proposed
Externí odkaz:
http://arxiv.org/abs/2308.08359
Autor:
Guo, Yufei, Liu, Xiaode, Chen, Yuanpei, Zhang, Liwen, Peng, Weihang, Zhang, Yuhan, Huang, Xuhui, Ma, Zhe
Spiking Neural Networks (SNNs) as one of the biology-inspired models have received much attention recently. It can significantly reduce energy consumption since they quantize the real-valued membrane potentials to 0/1 spikes to transmit information t
Externí odkaz:
http://arxiv.org/abs/2308.06787
Although distributional reinforcement learning (DRL) has been widely examined in the past few years, very few studies investigate the validity of the obtained Q-function estimator in the distributional setting. To fully understand how the approximati
Externí odkaz:
http://arxiv.org/abs/2307.16152
Autor:
Guo, Yufei, Chen, Yuanpei, Zhang, Liwen, Liu, Xiaode, Tong, Xinyi, Ou, Yuanyuan, Huang, Xuhui, Ma, Zhe
The Spiking Neural Network (SNN) has attracted more and more attention recently. It adopts binary spike signals to transmit information. Benefitting from the information passing paradigm of SNNs, the multiplications of activations and weights can be
Externí odkaz:
http://arxiv.org/abs/2307.04356