{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:I55UJOJV7J6TYNHGI54YDYMIGH","short_pith_number":"pith:I55UJOJV","canonical_record":{"source":{"id":"1705.08557","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-05-23T23:17:49Z","cross_cats_sorted":["cs.CL","cs.LG","cs.NE"],"title_canon_sha256":"a08d33ec67415b1720de89b36f3799fe99a5e0367375ada2884601a9f333952a","abstract_canon_sha256":"8220c8baa5c08eb83cf90f430c82513e15ec71e6e39547889b20c478db145be5"},"schema_version":"1.0"},"canonical_sha256":"477b44b935fa7d3c34e6477981e18831f268494a3ce61b70cec415a501afcee4","source":{"kind":"arxiv","id":"1705.08557","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.08557","created_at":"2026-05-18T00:43:45Z"},{"alias_kind":"arxiv_version","alias_value":"1705.08557v1","created_at":"2026-05-18T00:43:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.08557","created_at":"2026-05-18T00:43:45Z"},{"alias_kind":"pith_short_12","alias_value":"I55UJOJV7J6T","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_16","alias_value":"I55UJOJV7J6TYNHG","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_8","alias_value":"I55UJOJV","created_at":"2026-05-18T12:31:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:I55UJOJV7J6TYNHGI54YDYMIGH","target":"record","payload":{"canonical_record":{"source":{"id":"1705.08557","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-05-23T23:17:49Z","cross_cats_sorted":["cs.CL","cs.LG","cs.NE"],"title_canon_sha256":"a08d33ec67415b1720de89b36f3799fe99a5e0367375ada2884601a9f333952a","abstract_canon_sha256":"8220c8baa5c08eb83cf90f430c82513e15ec71e6e39547889b20c478db145be5"},"schema_version":"1.0"},"canonical_sha256":"477b44b935fa7d3c34e6477981e18831f268494a3ce61b70cec415a501afcee4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:43:45.759049Z","signature_b64":"mONbcadbnaT/ERHom2jqn+uYFsBb9V8Mh0LnT5tvhtiTNgxeIGYCUyyL0tF1AlFA7AYhVDBkME05C5bm+/SqCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"477b44b935fa7d3c34e6477981e18831f268494a3ce61b70cec415a501afcee4","last_reissued_at":"2026-05-18T00:43:45.758486Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:43:45.758486Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1705.08557","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":"2026-05-18T00:43:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hfl57Nj2eb/wCGlSU1a+dS/EbvoDx4qJzoVAdsHUd6nOaoRT2b77fLjx2Rv0EgY+ju03dDon6AQIINURO7qrBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T11:39:36.634833Z"},"content_sha256":"6cf9dd54da5dabad0955f90e49b4c8eb2c9671f27a1d2e1ab4967837b4651711","schema_version":"1.0","event_id":"sha256:6cf9dd54da5dabad0955f90e49b4c8eb2c9671f27a1d2e1ab4967837b4651711"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:I55UJOJV7J6TYNHGI54YDYMIGH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Grounded Recurrent Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG","cs.NE"],"primary_cat":"stat.ML","authors_text":"Ankit Vani, David Sontag, Yacine Jernite","submitted_at":"2017-05-23T23:17:49Z","abstract_excerpt":"In this work, we present the Grounded Recurrent Neural Network (GRNN), a recurrent neural network architecture for multi-label prediction which explicitly ties labels to specific dimensions of the recurrent hidden state (we call this process \"grounding\"). The approach is particularly well-suited for extracting large numbers of concepts from text. We apply the new model to address an important problem in healthcare of understanding what medical concepts are discussed in clinical text. Using a publicly available dataset derived from Intensive Care Units, we learn to label a patient's diagnoses a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.08557","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":"2026-05-18T00:43:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Nrgmz1NIg6XavqMaersdfEJvxo5hl54pXQPKp78tuxIFASSLS3atsW8whHPkZUzaNnTL97BCD9RCnuXsRU3fCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T11:39:36.635468Z"},"content_sha256":"743175eb7853ecbcfa891e1ed77053e9488b153454d8829776c1be9bf76b508b","schema_version":"1.0","event_id":"sha256:743175eb7853ecbcfa891e1ed77053e9488b153454d8829776c1be9bf76b508b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/I55UJOJV7J6TYNHGI54YDYMIGH/bundle.json","state_url":"https://pith.science/pith/I55UJOJV7J6TYNHGI54YDYMIGH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/I55UJOJV7J6TYNHGI54YDYMIGH/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":"2026-05-27T11:39:36Z","links":{"resolver":"https://pith.science/pith/I55UJOJV7J6TYNHGI54YDYMIGH","bundle":"https://pith.science/pith/I55UJOJV7J6TYNHGI54YDYMIGH/bundle.json","state":"https://pith.science/pith/I55UJOJV7J6TYNHGI54YDYMIGH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/I55UJOJV7J6TYNHGI54YDYMIGH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:I55UJOJV7J6TYNHGI54YDYMIGH","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":"8220c8baa5c08eb83cf90f430c82513e15ec71e6e39547889b20c478db145be5","cross_cats_sorted":["cs.CL","cs.LG","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-05-23T23:17:49Z","title_canon_sha256":"a08d33ec67415b1720de89b36f3799fe99a5e0367375ada2884601a9f333952a"},"schema_version":"1.0","source":{"id":"1705.08557","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.08557","created_at":"2026-05-18T00:43:45Z"},{"alias_kind":"arxiv_version","alias_value":"1705.08557v1","created_at":"2026-05-18T00:43:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.08557","created_at":"2026-05-18T00:43:45Z"},{"alias_kind":"pith_short_12","alias_value":"I55UJOJV7J6T","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_16","alias_value":"I55UJOJV7J6TYNHG","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_8","alias_value":"I55UJOJV","created_at":"2026-05-18T12:31:21Z"}],"graph_snapshots":[{"event_id":"sha256:743175eb7853ecbcfa891e1ed77053e9488b153454d8829776c1be9bf76b508b","target":"graph","created_at":"2026-05-18T00:43:45Z","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":"In this work, we present the Grounded Recurrent Neural Network (GRNN), a recurrent neural network architecture for multi-label prediction which explicitly ties labels to specific dimensions of the recurrent hidden state (we call this process \"grounding\"). The approach is particularly well-suited for extracting large numbers of concepts from text. We apply the new model to address an important problem in healthcare of understanding what medical concepts are discussed in clinical text. Using a publicly available dataset derived from Intensive Care Units, we learn to label a patient's diagnoses a","authors_text":"Ankit Vani, David Sontag, Yacine Jernite","cross_cats":["cs.CL","cs.LG","cs.NE"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-05-23T23:17:49Z","title":"Grounded Recurrent Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.08557","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":"sha256:6cf9dd54da5dabad0955f90e49b4c8eb2c9671f27a1d2e1ab4967837b4651711","target":"record","created_at":"2026-05-18T00:43:45Z","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":"8220c8baa5c08eb83cf90f430c82513e15ec71e6e39547889b20c478db145be5","cross_cats_sorted":["cs.CL","cs.LG","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-05-23T23:17:49Z","title_canon_sha256":"a08d33ec67415b1720de89b36f3799fe99a5e0367375ada2884601a9f333952a"},"schema_version":"1.0","source":{"id":"1705.08557","kind":"arxiv","version":1}},"canonical_sha256":"477b44b935fa7d3c34e6477981e18831f268494a3ce61b70cec415a501afcee4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"477b44b935fa7d3c34e6477981e18831f268494a3ce61b70cec415a501afcee4","first_computed_at":"2026-05-18T00:43:45.758486Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:43:45.758486Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"mONbcadbnaT/ERHom2jqn+uYFsBb9V8Mh0LnT5tvhtiTNgxeIGYCUyyL0tF1AlFA7AYhVDBkME05C5bm+/SqCw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:43:45.759049Z","signed_message":"canonical_sha256_bytes"},"source_id":"1705.08557","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6cf9dd54da5dabad0955f90e49b4c8eb2c9671f27a1d2e1ab4967837b4651711","sha256:743175eb7853ecbcfa891e1ed77053e9488b153454d8829776c1be9bf76b508b"],"state_sha256":"3c584a5a3e51d4c943a7788fdba46417e57b6960d80f132c4dc7a4be5b9d1dcf"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sB4dXXcBMtntWqqLDH70Ioyixkja/t0M2MSeNFfT+Rkal9MmQ7HE1zAjST2Bjla1Hjbw1CvabPrqSZIn6FC7DA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T11:39:36.638931Z","bundle_sha256":"17a99f251e0f4a253ec387320e35092eb476e2f5360e3e226d4af2397929620b"}}