{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:FJAYYQX2DEOJAJVG6MCS2ZMTB2","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":"a5059a46977518e196e5f848fc27cf061129f9b263aa244360ffc61e24d73cf8","cross_cats_sorted":["cs.CL","cs.LG","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2016-07-08T17:35:51Z","title_canon_sha256":"b510e5a5c08b57b87d9469b18cb7d7904551fef64eaa64e48361b43c6766e2d2"},"schema_version":"1.0","source":{"id":"1607.02467","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1607.02467","created_at":"2026-05-18T00:54:52Z"},{"alias_kind":"arxiv_version","alias_value":"1607.02467v2","created_at":"2026-05-18T00:54:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1607.02467","created_at":"2026-05-18T00:54:52Z"},{"alias_kind":"pith_short_12","alias_value":"FJAYYQX2DEOJ","created_at":"2026-05-18T12:30:15Z"},{"alias_kind":"pith_short_16","alias_value":"FJAYYQX2DEOJAJVG","created_at":"2026-05-18T12:30:15Z"},{"alias_kind":"pith_short_8","alias_value":"FJAYYQX2","created_at":"2026-05-18T12:30:15Z"}],"graph_snapshots":[{"event_id":"sha256:3e94fec33927f2e2769f870822c27d8f2d71867e1596aaa3b82e752777c4697e","target":"graph","created_at":"2026-05-18T00:54:52Z","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":"We introduce LL-RNNs (Log-Linear RNNs), an extension of Recurrent Neural Networks that replaces the softmax output layer by a log-linear output layer, of which the softmax is a special case. This conceptually simple move has two main advantages. First, it allows the learner to combat training data sparsity by allowing it to model words (or more generally, output symbols) as complex combinations of attributes without requiring that each combination is directly observed in the training data (as the softmax does). Second, it permits the inclusion of flexible prior knowledge in the form of a prior","authors_text":"Chunyang Xiao, Marc Dymetman","cross_cats":["cs.CL","cs.LG","cs.NE"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2016-07-08T17:35:51Z","title":"Log-Linear RNNs: Towards Recurrent Neural Networks with Flexible Prior Knowledge"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1607.02467","kind":"arxiv","version":2},"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:efc93744491598dd88f1ea8b10e971aeeaae495a0837ab6722e878e6dfbb2a58","target":"record","created_at":"2026-05-18T00:54:52Z","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":"a5059a46977518e196e5f848fc27cf061129f9b263aa244360ffc61e24d73cf8","cross_cats_sorted":["cs.CL","cs.LG","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2016-07-08T17:35:51Z","title_canon_sha256":"b510e5a5c08b57b87d9469b18cb7d7904551fef64eaa64e48361b43c6766e2d2"},"schema_version":"1.0","source":{"id":"1607.02467","kind":"arxiv","version":2}},"canonical_sha256":"2a418c42fa191c9026a6f3052d65930e8da606054171e7b956f343ef33d465a0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2a418c42fa191c9026a6f3052d65930e8da606054171e7b956f343ef33d465a0","first_computed_at":"2026-05-18T00:54:52.739733Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:54:52.739733Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fAi2GR6/6gOlD5/1w0OACVzJwxq+VpJBiB5dIjU3GHACQUI7nbORY5KghXSYYo8UXJWqqDfchH3zwPKXK7LYDA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:54:52.740126Z","signed_message":"canonical_sha256_bytes"},"source_id":"1607.02467","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:efc93744491598dd88f1ea8b10e971aeeaae495a0837ab6722e878e6dfbb2a58","sha256:3e94fec33927f2e2769f870822c27d8f2d71867e1596aaa3b82e752777c4697e"],"state_sha256":"96b4cb5bc9d330c4e1dab15fa740b6a6fbbfcabb94f0c462dd3327b0ac46dd29"}