{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:F7BUS63IUYYMVKVVTLLPGXRVX5","short_pith_number":"pith:F7BUS63I","canonical_record":{"source":{"id":"1909.02437","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-09-04T13:58:11Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"80525c693a4951561f1f086fd6c3dbafd5a362528a2d9d62a6edcda87aad18ec","abstract_canon_sha256":"31e130ddb64fac35a590083bbafbd56891b0b852fc29774dfad69d3e01380a2e"},"schema_version":"1.0"},"canonical_sha256":"2fc3497b68a630caaab59ad6f35e35bf58f5787323c9a33d718878e4b05b5fdb","source":{"kind":"arxiv","id":"1909.02437","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1909.02437","created_at":"2026-07-05T00:02:34Z"},{"alias_kind":"arxiv_version","alias_value":"1909.02437v1","created_at":"2026-07-05T00:02:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.02437","created_at":"2026-07-05T00:02:34Z"},{"alias_kind":"pith_short_12","alias_value":"F7BUS63IUYYM","created_at":"2026-07-05T00:02:34Z"},{"alias_kind":"pith_short_16","alias_value":"F7BUS63IUYYMVKVV","created_at":"2026-07-05T00:02:34Z"},{"alias_kind":"pith_short_8","alias_value":"F7BUS63I","created_at":"2026-07-05T00:02:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:F7BUS63IUYYMVKVVTLLPGXRVX5","target":"record","payload":{"canonical_record":{"source":{"id":"1909.02437","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-09-04T13:58:11Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"80525c693a4951561f1f086fd6c3dbafd5a362528a2d9d62a6edcda87aad18ec","abstract_canon_sha256":"31e130ddb64fac35a590083bbafbd56891b0b852fc29774dfad69d3e01380a2e"},"schema_version":"1.0"},"canonical_sha256":"2fc3497b68a630caaab59ad6f35e35bf58f5787323c9a33d718878e4b05b5fdb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:02:34.604671Z","signature_b64":"ovljPSdntXUW5cIZ5nL3dVkBS8GQzk//NJFAmZbcvH3Rw2pFee+EbwMCjoFtmCJ/QXQvmufC1QQxc90iLybcCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2fc3497b68a630caaab59ad6f35e35bf58f5787323c9a33d718878e4b05b5fdb","last_reissued_at":"2026-07-05T00:02:34.604195Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:02:34.604195Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1909.02437","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":"2026-07-05T00:02:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NcntMZQyjW/p+DBmRLFoDUuluoHJUyNdBV6rHxhqWZMdQLF/lQXVPaMxfdMNMntviso09ItM41gTeKtODXPsCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T20:16:56.130488Z"},"content_sha256":"9812908ac288b51e22a2964eebae28da955c30f203f20c0983efa641e3470ad8","schema_version":"1.0","event_id":"sha256:9812908ac288b51e22a2964eebae28da955c30f203f20c0983efa641e3470ad8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:F7BUS63IUYYMVKVVTLLPGXRVX5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ALIME: Autoencoder Based Approach for Local Interpretability","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Davor Runje, Sharath M. Shankaranarayana","submitted_at":"2019-09-04T13:58:11Z","abstract_excerpt":"Machine learning and especially deep learning have garneredtremendous popularity in recent years due to their increased performanceover other methods. The availability of large amount of data has aidedin the progress of deep learning. Nevertheless, deep learning models areopaque and often seen as black boxes. Thus, there is an inherent need tomake the models interpretable, especially so in the medical domain. Inthis work, we propose a locally interpretable method, which is inspiredby one of the recent tools that has gained a lot of interest, called localinterpretable model-agnostic explanation"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.02437","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/1909.02437/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:02:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5wsM1EYwU+Et9au7OVuCIzCnmBnUPnryAF8Z/keW0OBpxmf4VKmGk2lCQToU9EPffa/7aftgeXEsOk8wgAWWBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T20:16:56.131000Z"},"content_sha256":"f4439eca52b21740181ee05dc5f278f404e1601e610102e828dd6e0d0bda582d","schema_version":"1.0","event_id":"sha256:f4439eca52b21740181ee05dc5f278f404e1601e610102e828dd6e0d0bda582d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/F7BUS63IUYYMVKVVTLLPGXRVX5/bundle.json","state_url":"https://pith.science/pith/F7BUS63IUYYMVKVVTLLPGXRVX5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/F7BUS63IUYYMVKVVTLLPGXRVX5/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":"2026-08-09T20:16:56Z","links":{"resolver":"https://pith.science/pith/F7BUS63IUYYMVKVVTLLPGXRVX5","bundle":"https://pith.science/pith/F7BUS63IUYYMVKVVTLLPGXRVX5/bundle.json","state":"https://pith.science/pith/F7BUS63IUYYMVKVVTLLPGXRVX5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/F7BUS63IUYYMVKVVTLLPGXRVX5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:F7BUS63IUYYMVKVVTLLPGXRVX5","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":"31e130ddb64fac35a590083bbafbd56891b0b852fc29774dfad69d3e01380a2e","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-09-04T13:58:11Z","title_canon_sha256":"80525c693a4951561f1f086fd6c3dbafd5a362528a2d9d62a6edcda87aad18ec"},"schema_version":"1.0","source":{"id":"1909.02437","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1909.02437","created_at":"2026-07-05T00:02:34Z"},{"alias_kind":"arxiv_version","alias_value":"1909.02437v1","created_at":"2026-07-05T00:02:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.02437","created_at":"2026-07-05T00:02:34Z"},{"alias_kind":"pith_short_12","alias_value":"F7BUS63IUYYM","created_at":"2026-07-05T00:02:34Z"},{"alias_kind":"pith_short_16","alias_value":"F7BUS63IUYYMVKVV","created_at":"2026-07-05T00:02:34Z"},{"alias_kind":"pith_short_8","alias_value":"F7BUS63I","created_at":"2026-07-05T00:02:34Z"}],"graph_snapshots":[{"event_id":"sha256:f4439eca52b21740181ee05dc5f278f404e1601e610102e828dd6e0d0bda582d","target":"graph","created_at":"2026-07-05T00:02:34Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1909.02437/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Machine learning and especially deep learning have garneredtremendous popularity in recent years due to their increased performanceover other methods. The availability of large amount of data has aidedin the progress of deep learning. Nevertheless, deep learning models areopaque and often seen as black boxes. Thus, there is an inherent need tomake the models interpretable, especially so in the medical domain. Inthis work, we propose a locally interpretable method, which is inspiredby one of the recent tools that has gained a lot of interest, called localinterpretable model-agnostic explanation","authors_text":"Davor Runje, Sharath M. Shankaranarayana","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-09-04T13:58:11Z","title":"ALIME: Autoencoder Based Approach for Local Interpretability"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.02437","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":"sha256:9812908ac288b51e22a2964eebae28da955c30f203f20c0983efa641e3470ad8","target":"record","created_at":"2026-07-05T00:02:34Z","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":"31e130ddb64fac35a590083bbafbd56891b0b852fc29774dfad69d3e01380a2e","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-09-04T13:58:11Z","title_canon_sha256":"80525c693a4951561f1f086fd6c3dbafd5a362528a2d9d62a6edcda87aad18ec"},"schema_version":"1.0","source":{"id":"1909.02437","kind":"arxiv","version":1}},"canonical_sha256":"2fc3497b68a630caaab59ad6f35e35bf58f5787323c9a33d718878e4b05b5fdb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2fc3497b68a630caaab59ad6f35e35bf58f5787323c9a33d718878e4b05b5fdb","first_computed_at":"2026-07-05T00:02:34.604195Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:02:34.604195Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ovljPSdntXUW5cIZ5nL3dVkBS8GQzk//NJFAmZbcvH3Rw2pFee+EbwMCjoFtmCJ/QXQvmufC1QQxc90iLybcCA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:02:34.604671Z","signed_message":"canonical_sha256_bytes"},"source_id":"1909.02437","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9812908ac288b51e22a2964eebae28da955c30f203f20c0983efa641e3470ad8","sha256:f4439eca52b21740181ee05dc5f278f404e1601e610102e828dd6e0d0bda582d"],"state_sha256":"c52c2ce8334df65524e899bea32f7a79009a3b9a4ec107959b967bc9d3eeb299"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Dj21FrVmr4phRnSj1AAxOfBMgtX4gD5QVwxfK6UhZY3adWbOKFJSJzpudAKupUZTB2hsMAeLXGOEIOwp4GT0BA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T20:16:56.136810Z","bundle_sha256":"cf1e707ace731794316c35cbea9be1084bd30427040120f5a3019ddf05143cde"}}