{"url":"/method/hierarchical-dnn-interpretations","slug":"hierarchical-dnn-interpretations","name":"Agglomerative Contextual Decomposition","full_name":"Agglomerative Contextual Decomposition","full_name_withheld":false,"description_markdown":"**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.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Hierarchical interpretations for neural network predictions","paper":"/paper/hierarchical-interpretations-for-neural","first_author":"Chandan Singh","n_authors":3,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/hierarchical-interpretations-for-neural"},"source":{"url":"http://arxiv.org/abs/1806.05337v2","title":"Hierarchical interpretations for neural network predictions","url_on_a_paper_host":true},"code_snippet_url":"https://github.com/csinva/hierarchical-dnn-interpretations","code_snippet_url_on_a_code_host":true,"categories":[{"area":"General","area_id":"general","collection":"Interpretability","url":"/methods/category/interpretability","pwc_aliases":[]}],"n_papers_tagged":1,"archive_num_papers":1,"papers_newest_first":[{"paper":"/paper/hierarchical-interpretations-for-neural","title":"Hierarchical interpretations for neural network predictions","date":"2018-06-14","arxiv_id":"1806.05337","n_code_links":1,"syntology":null}],"papers_shown":1,"tasks":[{"task":"/task/clustering","name":"Clustering","papers":1},{"task":"/task/feature-importance","name":"Feature Importance","papers":1},{"task":"/task/interpretable-machine-learning","name":"Interpretable Machine Learning","papers":1}],"tasks_shown":3,"n_tasks":3,"usage_by_year":[{"year":"2018","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/hierarchical-dnn-interpretations"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}