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The paper discusses the interpretability of attention networks in deep learning, particularly in the context of selective dependence classification (SDC). It introduces a new attention model called Focus-Classify Attention Model (FCAM) and evaluates various attention learning algorithms to improve interpretability. The authors provide quantitative measures of interpretability and demonstrate that attention models can be accurate yet not interpretable, emphasizing the need for a structured approach to unders

Author
lshri2804
Language
EN

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