Zobrazeno 1 - 10
of 100
pro vyhledávání: '"Hao, Hongyan"'
Autor:
Xue, Siqiao, Wang, Yan, Chu, Zhixuan, Shi, Xiaoming, Jiang, Caigao, Hao, Hongyan, Jiang, Gangwei, Feng, Xiaoyun, Zhang, James Y., Zhou, Jun
Neural Temporal Point Processes (TPPs) are the prevalent paradigm for modeling continuous-time event sequences, such as user activities on the web and financial transactions. In real-world applications, event data is typically received in a \emph{str
Externí odkaz:
http://arxiv.org/abs/2310.04993
Autor:
Wang, Yan, Chu, Zhixuan, Zhou, Tao, Jiang, Caigao, Hao, Hongyan, Zhu, Minjie, Cai, Xindong, Cui, Qing, Li, Longfei, Zhang, James Y, Xue, Siqiao, Zhou, Jun
Asynchronous time series, also known as temporal event sequences, are the basis of many applications throughout different industries. Temporal point processes(TPPs) are the standard method for modeling such data. Existing TPP models have focused on p
Externí odkaz:
http://arxiv.org/abs/2309.02868
Autor:
Chu, Zhixuan, Hao, Hongyan, Ouyang, Xin, Wang, Simeng, Wang, Yan, Shen, Yue, Gu, Jinjie, Cui, Qing, Li, Longfei, Xue, Siqiao, Zhang, James Y, Li, Sheng
Recent advancements in recommendation systems have shifted towards more comprehensive and personalized recommendations by utilizing large language models (LLM). However, effectively integrating LLM's commonsense knowledge and reasoning abilities into
Externí odkaz:
http://arxiv.org/abs/2308.10837
Autor:
Wang, Yan, Chu, Zhixuan, Ouyang, Xin, Wang, Simeng, Hao, Hongyan, Shen, Yue, Gu, Jinjie, Xue, Siqiao, Zhang, James Y, Cui, Qing, Li, Longfei, Zhou, Jun, Li, Sheng
Recommendation systems aim to provide users with relevant suggestions, but often lack interpretability and fail to capture higher-level semantic relationships between user behaviors and profiles. In this paper, we propose a novel approach that levera
Externí odkaz:
http://arxiv.org/abs/2308.10835
Autor:
Xue, Siqiao, Zhou, Fan, Xu, Yi, Jin, Ming, Wen, Qingsong, Hao, Hongyan, Dai, Qingyang, Jiang, Caigao, Zhao, Hongyu, Xie, Shuo, He, Jianshan, Zhang, James, Mei, Hongyuan
We present WeaverBird, an intelligent dialogue system designed specifically for the finance domain. Our system harnesses a large language model of GPT architecture that has been tuned using extensive corpora of finance-related text. As a result, our
Externí odkaz:
http://arxiv.org/abs/2308.05361
Autor:
Hao, Hongyan, Chu, Zhixuan, Zhu, Shiyi, Jiang, Gangwei, Wang, Yan, Jiang, Caigao, Zhang, James, Jiang, Wei, Xue, Siqiao, Zhou, Jun
Predictive Autoscaling is used to forecast the workloads of servers and prepare the resources in advance to ensure service level objectives (SLOs) in dynamic cloud environments. However, in practice, its prediction task often suffers from performance
Externí odkaz:
http://arxiv.org/abs/2307.15941
Autor:
Xue, Siqiao, Shi, Xiaoming, Chu, Zhixuan, Wang, Yan, Hao, Hongyan, Zhou, Fan, Jiang, Caigao, Pan, Chen, Zhang, James Y., Wen, Qingsong, Zhou, Jun, Mei, Hongyuan
Continuous-time event sequences play a vital role in real-world domains such as healthcare, finance, online shopping, social networks, and so on. To model such data, temporal point processes (TPPs) have emerged as the most natural and competitive mod
Externí odkaz:
http://arxiv.org/abs/2307.08097
Autor:
Xue, Siqiao, Shi, Xiaoming, Hao, Hongyan, Ma, Lintao, Wang, Shiyu, Wang, Shijun, Zhang, James
Publikováno v:
2021 International Joint Conference on Neural Networks (IJCNN) (pp. 1-7). IEEE
Point process is the dominant paradigm for modeling event sequences occurring at irregular intervals. In this paper we aim at modeling latent dynamics of event propagation in graph, where the event sequence propagates in a directed weighted graph who
Externí odkaz:
http://arxiv.org/abs/2211.11758
Publikováno v:
International Conference on Machine Learning (ICML), First Workshop of Pre-training, 2022
Learning user sequence behaviour embedding is very sophisticated and challenging due to the complicated feature interactions over time and high dimensions of user features. Recent emerging foundation models, e.g., BERT and its variants, encourage a l
Externí odkaz:
http://arxiv.org/abs/2207.04648
Autor:
Fei, Dandan, Peng, Yingxi, Tang, Feng, Liu, Zikang, Wang, Ruohua, Chen, Lei, Liu, Xingzhong, Chen, Xiaoqin, Song, Min, Hao, Hongyan
Publikováno v:
In Ceramics International 1 April 2024 50(7) Part B:12017-12027