{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:IP6TG5HUTM3VVKW5LQHAQY7SOW","short_pith_number":"pith:IP6TG5HU","canonical_record":{"source":{"id":"1802.00541","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-02-02T02:24:24Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"4bee09dd5857d215e7d3592dbfc140c9f47f68101d900260b2b0d7bdb7909f62","abstract_canon_sha256":"a061a1c8c98bbc6c378ad092fdef8b97180354ded52fe633dcc34d7d97e16d47"},"schema_version":"1.0"},"canonical_sha256":"43fd3374f49b375aaadd5c0e0863f27593c324eaf626856f7f76ca4302aefe52","source":{"kind":"arxiv","id":"1802.00541","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.00541","created_at":"2026-05-18T00:24:32Z"},{"alias_kind":"arxiv_version","alias_value":"1802.00541v1","created_at":"2026-05-18T00:24:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.00541","created_at":"2026-05-18T00:24:32Z"},{"alias_kind":"pith_short_12","alias_value":"IP6TG5HUTM3V","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_16","alias_value":"IP6TG5HUTM3VVKW5","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_8","alias_value":"IP6TG5HU","created_at":"2026-05-18T12:32:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:IP6TG5HUTM3VVKW5LQHAQY7SOW","target":"record","payload":{"canonical_record":{"source":{"id":"1802.00541","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-02-02T02:24:24Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"4bee09dd5857d215e7d3592dbfc140c9f47f68101d900260b2b0d7bdb7909f62","abstract_canon_sha256":"a061a1c8c98bbc6c378ad092fdef8b97180354ded52fe633dcc34d7d97e16d47"},"schema_version":"1.0"},"canonical_sha256":"43fd3374f49b375aaadd5c0e0863f27593c324eaf626856f7f76ca4302aefe52","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:24:32.920982Z","signature_b64":"E4hkMoYOM3pkQ72S0eZ2isV1Xnje5Csw7vcbub/hE+xlGfo5+dg22FZwsa3TQfQq2TJOVwo2/KgUEApZt6x6Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"43fd3374f49b375aaadd5c0e0863f27593c324eaf626856f7f76ca4302aefe52","last_reissued_at":"2026-05-18T00:24:32.920447Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:24:32.920447Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1802.00541","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:24:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DT5tL2GCWhlBOdShE3yjYsYEARE3ASfmp1esNqdEjOmjB6jbgGZmE9JfesrXwWwfTQWHjsXTaKu13OnLmESsCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-25T14:09:18.539582Z"},"content_sha256":"cf79574474b6e8feb69b385bee33491aca3715b1a79cfbcdc0fc456b4ba2b80f","schema_version":"1.0","event_id":"sha256:cf79574474b6e8feb69b385bee33491aca3715b1a79cfbcdc0fc456b4ba2b80f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:IP6TG5HUTM3VVKW5LQHAQY7SOW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Causal Learning and Explanation of Deep Neural Networks via Autoencoded Activations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.AI","authors_text":"Brian Ruttenberg, Jeff Druce, Michael Harradon","submitted_at":"2018-02-02T02:24:24Z","abstract_excerpt":"Deep neural networks are complex and opaque. As they enter application in a variety of important and safety critical domains, users seek methods to explain their output predictions. We develop an approach to explaining deep neural networks by constructing causal models on salient concepts contained in a CNN. We develop methods to extract salient concepts throughout a target network by using autoencoders trained to extract human-understandable representations of network activations. We then build a bayesian causal model using these extracted concepts as variables in order to explain image class"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.00541","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:24:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4sE6j7X56GOc0yGcSuSQ91eerPXKBYAqsnjt6axQFExwLO2KNEyuTONkyIozsMImpdJd5Jf0fissO0RaDmt1Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-25T14:09:18.539934Z"},"content_sha256":"21bd47030cc0ea3bdbc46f1dee6b8d0cf53d8e1ce1da53e37c2204c712d23a1d","schema_version":"1.0","event_id":"sha256:21bd47030cc0ea3bdbc46f1dee6b8d0cf53d8e1ce1da53e37c2204c712d23a1d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IP6TG5HUTM3VVKW5LQHAQY7SOW/bundle.json","state_url":"https://pith.science/pith/IP6TG5HUTM3VVKW5LQHAQY7SOW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IP6TG5HUTM3VVKW5LQHAQY7SOW/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-25T14:09:18Z","links":{"resolver":"https://pith.science/pith/IP6TG5HUTM3VVKW5LQHAQY7SOW","bundle":"https://pith.science/pith/IP6TG5HUTM3VVKW5LQHAQY7SOW/bundle.json","state":"https://pith.science/pith/IP6TG5HUTM3VVKW5LQHAQY7SOW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IP6TG5HUTM3VVKW5LQHAQY7SOW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:IP6TG5HUTM3VVKW5LQHAQY7SOW","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"a061a1c8c98bbc6c378ad092fdef8b97180354ded52fe633dcc34d7d97e16d47","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-02-02T02:24:24Z","title_canon_sha256":"4bee09dd5857d215e7d3592dbfc140c9f47f68101d900260b2b0d7bdb7909f62"},"schema_version":"1.0","source":{"id":"1802.00541","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.00541","created_at":"2026-05-18T00:24:32Z"},{"alias_kind":"arxiv_version","alias_value":"1802.00541v1","created_at":"2026-05-18T00:24:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.00541","created_at":"2026-05-18T00:24:32Z"},{"alias_kind":"pith_short_12","alias_value":"IP6TG5HUTM3V","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_16","alias_value":"IP6TG5HUTM3VVKW5","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_8","alias_value":"IP6TG5HU","created_at":"2026-05-18T12:32:31Z"}],"graph_snapshots":[{"event_id":"sha256:21bd47030cc0ea3bdbc46f1dee6b8d0cf53d8e1ce1da53e37c2204c712d23a1d","target":"graph","created_at":"2026-05-18T00:24:32Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Deep neural networks are complex and opaque. As they enter application in a variety of important and safety critical domains, users seek methods to explain their output predictions. We develop an approach to explaining deep neural networks by constructing causal models on salient concepts contained in a CNN. We develop methods to extract salient concepts throughout a target network by using autoencoders trained to extract human-understandable representations of network activations. We then build a bayesian causal model using these extracted concepts as variables in order to explain image class","authors_text":"Brian Ruttenberg, Jeff Druce, Michael Harradon","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-02-02T02:24:24Z","title":"Causal Learning and Explanation of Deep Neural Networks via Autoencoded Activations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.00541","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:cf79574474b6e8feb69b385bee33491aca3715b1a79cfbcdc0fc456b4ba2b80f","target":"record","created_at":"2026-05-18T00:24:32Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"a061a1c8c98bbc6c378ad092fdef8b97180354ded52fe633dcc34d7d97e16d47","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-02-02T02:24:24Z","title_canon_sha256":"4bee09dd5857d215e7d3592dbfc140c9f47f68101d900260b2b0d7bdb7909f62"},"schema_version":"1.0","source":{"id":"1802.00541","kind":"arxiv","version":1}},"canonical_sha256":"43fd3374f49b375aaadd5c0e0863f27593c324eaf626856f7f76ca4302aefe52","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"43fd3374f49b375aaadd5c0e0863f27593c324eaf626856f7f76ca4302aefe52","first_computed_at":"2026-05-18T00:24:32.920447Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:24:32.920447Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"E4hkMoYOM3pkQ72S0eZ2isV1Xnje5Csw7vcbub/hE+xlGfo5+dg22FZwsa3TQfQq2TJOVwo2/KgUEApZt6x6Ag==","signature_status":"signed_v1","signed_at":"2026-05-18T00:24:32.920982Z","signed_message":"canonical_sha256_bytes"},"source_id":"1802.00541","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cf79574474b6e8feb69b385bee33491aca3715b1a79cfbcdc0fc456b4ba2b80f","sha256:21bd47030cc0ea3bdbc46f1dee6b8d0cf53d8e1ce1da53e37c2204c712d23a1d"],"state_sha256":"fc2523f765cb7888842fb20f6ce72f869f22352063e0e45a52371a711b1f514b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kYfc0zEmpIOQ+BE6mOKJYZiONBCz2Hf65U3dEwfysHIsKU1qZCIqn2KynOGRnxdya18ZF0emVrn6JQydnzBsBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-25T14:09:18.541933Z","bundle_sha256":"74743a6834e8e90ad4e15284acbb955380c1d4835cf147581da9fe26cfccd919"}}