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Contextual Decomposition Explanation Penalization

CDEP

2 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Contextual Decomposition Explanation Penalization (CDEP) is a method which leverages existing explanation techniques for neural networks in order to prevent a model from learning unwanted relationships and ultimately improve predictive accuracy. Given particular importance scores, CDEP works by allowing the user to directly penalize importances of certain features, or interactions. This forces the neural network to not only produce the correct prediction, but also the correct explanation for that prediction

Source: Interpretations are useful: penalizing explanations to...See Code · laura-rieger/deep-explanation-penalization

Papers archive 2025-07-28

2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

5 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Age Estimation1
Decision Making1
Deep Learning1
Explainable Artificial Intelligence (XAI)1
Explainable artificial intelligence1

Usage over time archive 2025-07-28

Papers per year tagged with CDEP: 2019 to 2023, peak 1 1 0 2019: 1 paper 2019 2020: 0 papers 2020 2021: 0 papers 2021 2022: 0 papers 2022 2023: 1 paper 2023
Papers per year the archive tags with this method, by the paper's archive date (2 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Interpretability

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