{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:3WSJL5MCW6OWJA5RORMMXOJFYV","short_pith_number":"pith:3WSJL5MC","schema_version":"1.0","canonical_sha256":"dda495f582b79d6483b17458cbb925c5478289b9c075d4e52302cb08a260f715","source":{"kind":"arxiv","id":"2206.10611","version":1},"attestation_state":"computed","paper":{"title":"Neural Activation Patterns (NAPs): Visual Explainability of Learned Concepts","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Alex B\\\"auerle, Daniel J\\\"onsson, Timo Ropinski","submitted_at":"2022-06-20T09:05:57Z","abstract_excerpt":"A key to deciphering the inner workings of neural networks is understanding what a model has learned. Promising methods for discovering learned features are based on analyzing activation values, whereby current techniques focus on analyzing high activation values to reveal interesting features on a neuron level. However, analyzing high activation values limits layer-level concept discovery. We present a method that instead takes into account the entire activation distribution. By extracting similar activation profiles within the high-dimensional activation space of a neural network layer, we f"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2206.10611","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-06-20T09:05:57Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"1cf0341d0783821e6d306201651244be90169dc10dc100dcf9fe66a6752d275d","abstract_canon_sha256":"e76807355f4af2ce5c248e569462979aba07b953ac3a9ef6626b73ee94659290"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:33:53.880549Z","signature_b64":"A0P9uzOeAWuP+jz4PcEvjuejivg12AYT3H0fiCsp6ZFX99pjPPg8J16WZ9JB+61tEC5JJOHR3zmZLcQihJuhDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dda495f582b79d6483b17458cbb925c5478289b9c075d4e52302cb08a260f715","last_reissued_at":"2026-07-05T04:33:53.880122Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:33:53.880122Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Neural Activation Patterns (NAPs): Visual Explainability of Learned Concepts","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Alex B\\\"auerle, Daniel J\\\"onsson, Timo Ropinski","submitted_at":"2022-06-20T09:05:57Z","abstract_excerpt":"A key to deciphering the inner workings of neural networks is understanding what a model has learned. Promising methods for discovering learned features are based on analyzing activation values, whereby current techniques focus on analyzing high activation values to reveal interesting features on a neuron level. However, analyzing high activation values limits layer-level concept discovery. We present a method that instead takes into account the entire activation distribution. By extracting similar activation profiles within the high-dimensional activation space of a neural network layer, we f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.10611","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2206.10611/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2206.10611","created_at":"2026-07-05T04:33:53.880181+00:00"},{"alias_kind":"arxiv_version","alias_value":"2206.10611v1","created_at":"2026-07-05T04:33:53.880181+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.10611","created_at":"2026-07-05T04:33:53.880181+00:00"},{"alias_kind":"pith_short_12","alias_value":"3WSJL5MCW6OW","created_at":"2026-07-05T04:33:53.880181+00:00"},{"alias_kind":"pith_short_16","alias_value":"3WSJL5MCW6OWJA5R","created_at":"2026-07-05T04:33:53.880181+00:00"},{"alias_kind":"pith_short_8","alias_value":"3WSJL5MC","created_at":"2026-07-05T04:33:53.880181+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3WSJL5MCW6OWJA5RORMMXOJFYV","json":"https://pith.science/pith/3WSJL5MCW6OWJA5RORMMXOJFYV.json","graph_json":"https://pith.science/api/pith-number/3WSJL5MCW6OWJA5RORMMXOJFYV/graph.json","events_json":"https://pith.science/api/pith-number/3WSJL5MCW6OWJA5RORMMXOJFYV/events.json","paper":"https://pith.science/paper/3WSJL5MC"},"agent_actions":{"view_html":"https://pith.science/pith/3WSJL5MCW6OWJA5RORMMXOJFYV","download_json":"https://pith.science/pith/3WSJL5MCW6OWJA5RORMMXOJFYV.json","view_paper":"https://pith.science/paper/3WSJL5MC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2206.10611&json=true","fetch_graph":"https://pith.science/api/pith-number/3WSJL5MCW6OWJA5RORMMXOJFYV/graph.json","fetch_events":"https://pith.science/api/pith-number/3WSJL5MCW6OWJA5RORMMXOJFYV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3WSJL5MCW6OWJA5RORMMXOJFYV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3WSJL5MCW6OWJA5RORMMXOJFYV/action/storage_attestation","attest_author":"https://pith.science/pith/3WSJL5MCW6OWJA5RORMMXOJFYV/action/author_attestation","sign_citation":"https://pith.science/pith/3WSJL5MCW6OWJA5RORMMXOJFYV/action/citation_signature","submit_replication":"https://pith.science/pith/3WSJL5MCW6OWJA5RORMMXOJFYV/action/replication_record"}},"created_at":"2026-07-05T04:33:53.880181+00:00","updated_at":"2026-07-05T04:33:53.880181+00:00"}