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of 7
pro vyhledávání: '"Ding, Ruomeng"'
Reward models trained on human preference data have been proven to be effective for aligning Large Language Models (LLMs) with human intent within the reinforcement learning from human feedback (RLHF) framework. However, the generalization capabiliti
Externí odkaz:
http://arxiv.org/abs/2406.10216
Autor:
Ding, Ruomeng, Zhang, Chaoyun, Wang, Lu, Xu, Yong, Ma, Minghua, Zhang, Wei, Qin, Si, Rajmohan, Saravan, Lin, Qingwei, Zhang, Dongmei
Recent advancements in Large Language Models (LLMs) have revolutionized decision-making by breaking down complex problems into more manageable language sequences referred to as "thoughts". An effective thought design should consider three key perspec
Externí odkaz:
http://arxiv.org/abs/2311.04254
Autor:
Ding, Ruomeng, Zhang, Chaoyun, Wang, Lu, Xu, Yong, Ma, Minghua, Wu, Xiaomin, Zhang, Meng, Chen, Qingjun, Gao, Xin, Gao, Xuedong, Fan, Hao, Rajmohan, Saravan, Lin, Qingwei, Zhang, Dongmei
Root Cause Analysis (RCA) is becoming increasingly crucial for ensuring the reliability of microservice systems. However, performing RCA on modern microservice systems can be challenging due to their large scale, as they usually comprise hundreds of
Externí odkaz:
http://arxiv.org/abs/2310.18740
Autor:
Chen, Yuhang, Zhang, Chaoyun, Ma, Minghua, Liu, Yudong, Ding, Ruomeng, Li, Bowen, He, Shilin, Rajmohan, Saravan, Lin, Qingwei, Zhang, Dongmei
Anomaly detection in multivariate time series data is of paramount importance for ensuring the efficient operation of large-scale systems across diverse domains. However, accurately detecting anomalies in such data poses significant challenges. Exist
Externí odkaz:
http://arxiv.org/abs/2307.00754
This paper describes an approach to the facial action unit (AU) detection. In this work, we present our submission to the Field Affective Behavior Analysis (ABAW) 2021 competition. The proposed method uses the pre-trained JAA model as the feature ext
Externí odkaz:
http://arxiv.org/abs/2107.04389
Autor:
Li, Tianpeng, Wang, Wenjun, Jiao, Pengfei, Wang, Yinghui, Ding, Ruomeng, Wu, Huaming, Pan, Lin, Jin, Di
Publikováno v:
IEEE Transactions on Cybernetics; November 2023, Vol. 53 Issue: 11 p7021-7033, 13p
Akademický článek
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