ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2303.15183
  4. Cited By
Derivative-based Shapley value for global sensitivity analysis and
  machine learning explainability

Derivative-based Shapley value for global sensitivity analysis and machine learning explainability

24 March 2023
Hui Duan
G. Ökten
    FAtt
ArXivPDFHTML

Papers citing "Derivative-based Shapley value for global sensitivity analysis and machine learning explainability"

1 / 1 papers shown
Title
Data-Driven Modelling for Harmonic Current Emission in Low-Voltage Grid
  Using MCReSANet with Interpretability Analysis
Data-Driven Modelling for Harmonic Current Emission in Low-Voltage Grid Using MCReSANet with Interpretability Analysis
Jieyu Yao
Hao Yu
Paul Judge
Jiabin Jia
Sasa Z. Djokic
Verner Püvi
Matti Lehtonen
Jan Meyer
33
0
0
26 Nov 2023
1