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Updated: Feb 15, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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連続時間グラフニューラルネットワークと深層補強学習による時間的な影響の最大化

Yong Wang1, Mohamad A Alawad2, Raed H C Alfilh3

  • 1School of Information Engineering, Yulin University, Yulin, 719000, Shaanxi, China.

Scientific reports
|February 13, 2026
PubMed
まとめ

TempRL-IMは,タイムレス強化学習フレームワークであり,連続時間グラフニューラルネットワーク (CTGNNs) とダブルディープQネットワーク (DDQN) エージェントを使用して,ダイナミックネットワークにおける影響力を最大化します. このアプローチにより,情報の拡散と推論のスピードが向上します.

キーワード:
連続時間モデリング深層強化学習 (DEP) による学習です.ダイナミックネットワーク ダイナミックネットワーク影響力の最大化中小企業とはタイムグラフニューラルネットワーク

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