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pro vyhledávání: '"Wang, JingYu"'
Autor:
Wang, Chengsen, Qi, Qi, Wang, Jingyu, Sun, Haifeng, Zhuang, Zirui, Wu, Jinming, Zhang, Lei, Liao, Jianxin
Human experts typically integrate numerical and textual multimodal information to analyze time series. However, most traditional deep learning predictors rely solely on unimodal numerical data, using a fixed-length window for training and prediction
Externí odkaz:
http://arxiv.org/abs/2412.11376
Explaining the decision-making processes of Artificial Intelligence (AI) models is crucial for addressing their "black box" nature, particularly in tasks like image classification. Traditional eXplainable AI (XAI) methods typically rely on unimodal e
Externí odkaz:
http://arxiv.org/abs/2411.13053
With the rapid advancements in wireless communication fields, including low-altitude economies, 6G, and Wi-Fi, the scale of wireless networks continues to expand, accompanied by increasing service quality demands. Traditional deep reinforcement learn
Externí odkaz:
http://arxiv.org/abs/2410.14481
Autor:
Wang, Yuanyi, Sun, Haifeng, Wang, Chengsen, Zhu, Mengde, Wang, Jingyu, Tang, Wei, Qi, Qi, Zhuang, Zirui, Liao, Jianxin
Anomaly detection in multivariate time series (MTS) is crucial for various applications in data mining and industry. Current industrial methods typically approach anomaly detection as an unsupervised learning task, aiming to identify deviations by es
Externí odkaz:
http://arxiv.org/abs/2410.08877
RISE-SDF: a Relightable Information-Shared Signed Distance Field for Glossy Object Inverse Rendering
Autor:
Zhang, Deheng, Wang, Jingyu, Wang, Shaofei, Mihajlovic, Marko, Prokudin, Sergey, Lensch, Hendrik P. A., Tang, Siyu
In this paper, we propose a novel end-to-end relightable neural inverse rendering system that achieves high-quality reconstruction of geometry and material properties, thus enabling high-quality relighting. The cornerstone of our method is a two-stag
Externí odkaz:
http://arxiv.org/abs/2409.20140
Autor:
Wang, Chengsen, Qi, Qi, Wang, Jingyu, Sun, Haifeng, Zhuang, Zirui, Wu, Jinming, Liao, Jianxin
Time series forecasting has played a pivotal role across various industries, including finance, transportation, energy, healthcare, and climate. Due to the abundant seasonal information they contain, timestamps possess the potential to offer robust g
Externí odkaz:
http://arxiv.org/abs/2409.18696
Accuracy anomaly detection in user-level network traffic is crucial for network security. Compared with existing models that passively detect specific anomaly classes with large labeled training samples, user-level network traffic contains sizeable n
Externí odkaz:
http://arxiv.org/abs/2408.17031
Deadline-aware transmission scheduling in immersive video streaming is crucial. The objective is to guarantee that at least a certain block in multi-links is fully delivered within their deadlines, which is referred to as delivery ratio. Compared wit
Externí odkaz:
http://arxiv.org/abs/2408.17028
Accuracy anomaly detection in user-level social multimedia traffic is crucial for privacy security. Compared with existing models that passively detect specific anomaly classes with large labeled training samples, user-level social multimedia traffic
Externí odkaz:
http://arxiv.org/abs/2408.14884
Autor:
Zhang, Shenglin, Zhu, Pengtian, Ma, Minghua, Wang, Jiagang, Sun, Yongqian, Li, Dongwen, Wang, Jingyu, Guo, Qianying, Hua, Xiaolei, Zhu, Lin, Pei, Dan
Large language models (LLMs) excel at general question-answering (Q&A) but often fall short in specialized domains due to a lack of domain-specific knowledge. Commercial companies face the dual challenges of privacy protection and resource constraint
Externí odkaz:
http://arxiv.org/abs/2408.12247