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pro vyhledávání: '"Jhin, Sheo yon"'
Time series forecasting has been an essential field in many different application areas, including economic analysis, meteorology, and so forth. The majority of time series forecasting models are trained using the mean squared error (MSE). However, t
Externí odkaz:
http://arxiv.org/abs/2407.01622
Anomaly detection is an important field that aims to identify unexpected patterns or data points, and it is closely related to many real-world problems, particularly to applications in finance, manufacturing, cyber security, and so on. While anomaly
Externí odkaz:
http://arxiv.org/abs/2306.15489
Neural controlled differential equations (NCDEs), which are continuous analogues to recurrent neural networks (RNNs), are a specialized model in (irregular) time-series processing. In comparison with similar models, e.g., neural ordinary differential
Externí odkaz:
http://arxiv.org/abs/2301.04333
Autor:
Lee, Jaehoon, Jeon, Jinsung, Jhin, Sheo yon, Hyeong, Jihyeon, Kim, Jayoung, Jo, Minju, Seungji, Kook, Park, Noseong
The problem of processing very long time-series data (e.g., a length of more than 10,000) is a long-standing research problem in machine learning. Recently, one breakthrough, called neural rough differential equations (NRDEs), has been proposed and h
Externí odkaz:
http://arxiv.org/abs/2204.08781
Autor:
Jhin, Sheo Yon, Lee, Jaehoon, Jo, Minju, Kook, Seungji, Jeon, Jinsung, Hyeong, Jihyeon, Kim, Jayoung, Park, Noseong
Deep learning inspired by differential equations is a recent research trend and has marked the state of the art performance for many machine learning tasks. Among them, time-series modeling with neural controlled differential equations (NCDEs) is con
Externí odkaz:
http://arxiv.org/abs/2204.08771
Neural networks inspired by differential equations have proliferated for the past several years. Neural ordinary differential equations (NODEs) and neural controlled differential equations (NCDEs) are two representative examples of them. In theory, N
Externí odkaz:
http://arxiv.org/abs/2109.01876
Neural ordinary differential equations (NODEs) presented a new paradigm to construct (continuous-time) neural networks. While showing several good characteristics in terms of the number of parameters and the flexibility in constructing neural network
Externí odkaz:
http://arxiv.org/abs/2105.14953
Akademický článek
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Autor:
Jhin, Sheo Yon, Shin, Heejoo, Kim, Sujie, Hong, Seoyoung, Jo, Minju, Park, Solhee, Park, Noseong, Lee, Seungbeom, Maeng, Hwiyoung, Jeon, Seungmin
Publikováno v:
Knowledge & Information Systems; Mar2024, Vol. 66 Issue 3, p1885-1915, 31p
