{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:YZIOR2NH5KRULISBOT34TCFARR","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":"732d2ce8ce6203a01475d2ea395f877af3bcbbe8aa637a48076ff4b9557fc1e3","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-05-12T14:39:50Z","title_canon_sha256":"484b4f5cb6e0006868980bbc4308a4ab96a44064478f3668d334521f98d79587"},"schema_version":"1.0","source":{"id":"1605.03835","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1605.03835","created_at":"2026-05-18T01:14:59Z"},{"alias_kind":"arxiv_version","alias_value":"1605.03835v1","created_at":"2026-05-18T01:14:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1605.03835","created_at":"2026-05-18T01:14:59Z"},{"alias_kind":"pith_short_12","alias_value":"YZIOR2NH5KRU","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_16","alias_value":"YZIOR2NH5KRULISB","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_8","alias_value":"YZIOR2NH","created_at":"2026-05-18T12:30:53Z"}],"graph_snapshots":[{"event_id":"sha256:1be20765679014f5eb60c161cc86a2a4a23624c70ef4a243a69bbe8c818ea74e","target":"graph","created_at":"2026-05-18T01:14:59Z","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":"Recent advances in conditional recurrent language modelling have mainly focused on network architectures (e.g., attention mechanism), learning algorithms (e.g., scheduled sampling and sequence-level training) and novel applications (e.g., image/video description generation, speech recognition, etc.) On the other hand, we notice that decoding algorithms/strategies have not been investigated as much, and it has become standard to use greedy or beam search. In this paper, we propose a novel decoding strategy motivated by an earlier observation that nonlinear hidden layers of a deep neural network","authors_text":"Kyunghyun Cho","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-05-12T14:39:50Z","title":"Noisy Parallel Approximate Decoding for Conditional Recurrent Language Model"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1605.03835","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":"sha256:3f086d87ce6ce878c5ad094c415bacaccc69fc36311b2754db0170bab35783db","target":"record","created_at":"2026-05-18T01:14:59Z","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":"732d2ce8ce6203a01475d2ea395f877af3bcbbe8aa637a48076ff4b9557fc1e3","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2016-05-12T14:39:50Z","title_canon_sha256":"484b4f5cb6e0006868980bbc4308a4ab96a44064478f3668d334521f98d79587"},"schema_version":"1.0","source":{"id":"1605.03835","kind":"arxiv","version":1}},"canonical_sha256":"c650e8e9a7eaa345a24174f7c988a08c7ead229e21be916438622ce7d1fd21a9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c650e8e9a7eaa345a24174f7c988a08c7ead229e21be916438622ce7d1fd21a9","first_computed_at":"2026-05-18T01:14:59.375682Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:14:59.375682Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"B39wO7g8Vm8Y0zsx5bnBrJ8RFD/x3v7pUH8GLKItkvd1xR4s882AofdJZuwvqrm7ajlPo9P+SD4UqZNT0V19Dg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:14:59.376351Z","signed_message":"canonical_sha256_bytes"},"source_id":"1605.03835","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3f086d87ce6ce878c5ad094c415bacaccc69fc36311b2754db0170bab35783db","sha256:1be20765679014f5eb60c161cc86a2a4a23624c70ef4a243a69bbe8c818ea74e"],"state_sha256":"eac39d768bd05cc548fa5ad9681cc9046dfdb83837a8a1df2dbd4f0e842f1915"}