Methods › General › Interpretability › Agglomerative Contextual Decomposition
Agglomerative Contextual Decomposition
Introduced by Chandan Singh et al. in Hierarchical interpretations for neural network predictions
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Agglomerative Contextual Decomposition (ACD) is an interpretability method that produces hierarchical interpretations for a single prediction made by a neural network, by scoring interactions and building them into a tree. Given a prediction from a trained neural network, ACD produces a hierarchical clustering of the input features, along with the contribution of each cluster to the final prediction. This hierarchy is optimized to identify clusters of features that the DNN learned are predictive.
Papers archive 2025-07-28
1 shown of 1, 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.
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Hierarchical interpretations for neural network predictions 14 Jun 2018 · 1 repository · arXiv:1806.05337
Tasks archive 2025-07-28
3 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Clustering | 1 |
| Feature Importance | 1 |
| Interpretable Machine Learning | 1 |
Usage over time archive 2025-07-28
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
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections