Graph neural networks for virtual sensing in complex systems: Addressing heterogeneous temporal dynamics
Learning Latent Graph Structures and their Uncertainty
Within a prediction task, Graph Neural Networks (GNNs) use relational information as an
inductive bias to enhance the model's accuracy. As task-relevant relations might be …
inductive bias to enhance the model's accuracy. As task-relevant relations might be …
On the Regularization of Learnable Embeddings for Time Series Processing
In processing multiple time series, accounting for the individual features of each sequence
can be challenging. To address this, modern deep learning methods for time series analysis …
can be challenging. To address this, modern deep learning methods for time series analysis …
グラフニューラルネットワークの最新動向
佐藤竜馬 - 電子情報通信学会 通信ソサイエティマガジン, 2024 - jstage.jst.go.jp
2 概要グラフニューラルネットワークの研究トピックは大きく分けて二種類ある. 第 1 は, 解釈性,
頑健性, 高速化, 汎化性能の解析など, 通常のニューラルネットワークにもあるトピックである …
頑健性, 高速化, 汎化性能の解析など, 通常のニューラルネットワークにもあるトピックである …