Zobrazeno 1 - 10
of 142
pro vyhledávání: '"Cheng, Mingyue"'
Advancements in self-supervised pre-training (SSL) have significantly advanced the field of learning transferable time series representations, which can be very useful in enhancing the downstream task. Despite being effective, most existing works str
Externí odkaz:
http://arxiv.org/abs/2403.12372
For the advancements of time series classification, scrutinizing previous studies, most existing methods adopt a common learning-to-classify paradigm - a time series classifier model tries to learn the relation between sequence inputs and target labe
Externí odkaz:
http://arxiv.org/abs/2403.12371
Autor:
Cheng, Mingyue, Zhang, Hao, Yang, Jiqian, Liu, Qi, Li, Li, Huang, Xin, Song, Liwei, Li, Zhi, Huang, Zhenya, Chen, Enhong
Large language model evaluation plays a pivotal role in the enhancement of its capacity. Previously, numerous methods for evaluating large language models have been proposed in this area. Despite their effectiveness, these existing works mainly focus
Externí odkaz:
http://arxiv.org/abs/2403.08305
Autor:
Cheng, Mingyue, Zhang, Hao, Liu, Qi, Yuan, Fajie, Li, Zhi, Huang, Zhenya, Chen, Enhong, Zhou, Jun, Li, Longfei
Sequential recommender systems (SRS) could capture dynamic user preferences by modeling historical behaviors ordered in time. Despite effectiveness, focusing only on the \textit{collaborative signals} from behaviors does not fully grasp user interest
Externí odkaz:
http://arxiv.org/abs/2403.07623
This paper introduces ConvTimeNet, a novel deep hierarchical fully convolutional network designed to serve as a general-purpose model for time series analysis. The key design of this network is twofold, designed to overcome the limitations of traditi
Externí odkaz:
http://arxiv.org/abs/2403.01493
Recent efforts have been dedicated to enhancing time series forecasting accuracy by introducing advanced network architectures and self-supervised pretraining strategies. Nevertheless, existing approaches still exhibit two critical drawbacks. Firstly
Externí odkaz:
http://arxiv.org/abs/2402.16516
Generating user-friendly explanations regarding why an item is recommended has become increasingly common, largely due to advances in language generation technology, which can enhance user trust and facilitate more informed decision-making when using
Externí odkaz:
http://arxiv.org/abs/2312.15661
Sequential recommender systems (SRS) have gained widespread popularity in recommendation due to their ability to effectively capture dynamic user preferences. One default setting in the current SRS is to uniformly consider each historical behavior as
Externí odkaz:
http://arxiv.org/abs/2311.00388
Autor:
Jiang, Junzhe, Qu, Shang, Cheng, Mingyue, Liu, Qi, Liu, Zhiding, Zhang, Hao, Zhang, Rujiao, Zhang, Kai, Li, Rui, Li, Jiatong, Gao, Min
Recommender systems are indispensable in the realm of online applications, and sequential recommendation has enjoyed considerable prevalence due to its capacity to encapsulate the dynamic shifts in user interests. However, previous sequential modelin
Externí odkaz:
http://arxiv.org/abs/2309.10435
Autor:
Fu, Junchen, Yuan, Fajie, Song, Yu, Yuan, Zheng, Cheng, Mingyue, Cheng, Shenghui, Zhang, Jiaqi, Wang, Jie, Pan, Yunzhu
Adapters, a plug-in neural network module with some tunable parameters, have emerged as a parameter-efficient transfer learning technique for adapting pre-trained models to downstream tasks, especially for natural language processing (NLP) and comput
Externí odkaz:
http://arxiv.org/abs/2305.15036