What question did this study set out to answer?

To review the theoretical foundations, algorithmic evaluations, best practices, and practical applications of post-hoc explanation methods for deep neural networks and nonlinear machine learning.

March 1, 2021Proceedings of the IEEEOpen Access

Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications

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To review the theoretical foundations, algorithmic evaluations, best practices, and practical applications of post-hoc explanation methods for deep neural networks and nonlinear machine learning.
Reviewed the mathematical and theoretical foundations of post-hoc interpretability algorithms for nonlinear models.
Evaluated interpretability methods through theoretical analysis and comparative simulation benchmarks across representative application domains.
Established foundational principles and best-practice guidelines for integrating post-hoc interpretation tools into standard machine learning workflows.
Demonstrated practical utility of explainability methods across diverse application scenarios while defining open challenges in model interpretability.

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Implication

Comprehensive review evaluates post-hoc explainability algorithms and best practices across deep neural networks, highlighting frameworks for reliable model interpretation in science and industry.

Cite This Study

Samek et al. (2021) studied this question.

synapsesocial.com/papers/69dacf420d8d6ef495a3c3d4 https://doi.org/10.1109/jproc.2021.3060483

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Implication

Comprehensive review evaluates post-hoc explainability algorithms and best practices across deep neural networks, highlighting frameworks for reliable model interpretation in science and industry.

Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications

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Discussion

Implication

Cite This Study

Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications

Key Points

Discussion

Implication

Cite This Study

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