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Found 2 papers, 1 papers with code
Latent SHAP: Toward Practical Human-Interpretable Explanations
Model agnostic feature attribution algorithms (such as SHAP and LIME) are ubiquitous techniques for explaining the decisions of complex classification models, such as deep neural networks.
Source Model Selection for Deep Learning in the Time Series Domain
They then select the most similar source task and use the model trained on it for transfer learning.
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