{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:6CPWT3YQR3ZDDFSZFO6NIC3KK2","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":"056ef211b295ba902925b7db3533c504b96c74fc286970368d16e34f64532377","cross_cats_sorted":["cs.IT","math.IT"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-11-06T12:45:32Z","title_canon_sha256":"d99b8ffbb561a9a5814a6eaa08b91d17ec33047e97bb108535870bb0ac136029"},"schema_version":"1.0","source":{"id":"2311.03061","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.03061","created_at":"2026-07-05T07:09:35Z"},{"alias_kind":"arxiv_version","alias_value":"2311.03061v1","created_at":"2026-07-05T07:09:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.03061","created_at":"2026-07-05T07:09:35Z"},{"alias_kind":"pith_short_12","alias_value":"6CPWT3YQR3ZD","created_at":"2026-07-05T07:09:35Z"},{"alias_kind":"pith_short_16","alias_value":"6CPWT3YQR3ZDDFSZ","created_at":"2026-07-05T07:09:35Z"},{"alias_kind":"pith_short_8","alias_value":"6CPWT3YQ","created_at":"2026-07-05T07:09:35Z"}],"graph_snapshots":[{"event_id":"sha256:2278f79f1d04159b3cf204f01afec5480c9cd6bb66ca8c6c87bd6818644e5b31","target":"graph","created_at":"2026-07-05T07:09:35Z","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/2311.03061/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We propose a data-driven approach to explicitly learn the progressive encoding of a continuous source, which is successively decoded with increasing levels of quality and with the aid of correlated side information. This setup refers to the successive refinement of the Wyner-Ziv coding problem. Assuming ideal Slepian-Wolf coding, our approach employs recurrent neural networks (RNNs) to learn layered encoders and decoders for the quadratic Gaussian case. The models are trained by minimizing a variational bound on the rate-distortion function of the successively refined Wyner-Ziv coding problem.","authors_text":"Boris Joukovsky, Brent De Weerdt, Nikos Deligiannis","cross_cats":["cs.IT","math.IT"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-11-06T12:45:32Z","title":"Learned layered coding for Successive Refinement in the Wyner-Ziv Problem"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.03061","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":"sha256:327a71cfa004817e7dccbca0fe6584b5c0c06bc734f2d26973c0c6d449e6dc45","target":"record","created_at":"2026-07-05T07:09:35Z","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":"056ef211b295ba902925b7db3533c504b96c74fc286970368d16e34f64532377","cross_cats_sorted":["cs.IT","math.IT"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-11-06T12:45:32Z","title_canon_sha256":"d99b8ffbb561a9a5814a6eaa08b91d17ec33047e97bb108535870bb0ac136029"},"schema_version":"1.0","source":{"id":"2311.03061","kind":"arxiv","version":1}},"canonical_sha256":"f09f69ef108ef23196592bbcd40b6a56b15e9b8941d600cf4a575712d620ebc4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f09f69ef108ef23196592bbcd40b6a56b15e9b8941d600cf4a575712d620ebc4","first_computed_at":"2026-07-05T07:09:35.495209Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:09:35.495209Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Kz+fKkZIOi8rMfSncS9A8ru3Bs8AQxUcsF25y93mpViAI+b3DVi2swi38n1UX5NfSWT0K603yFRc1llQ3RNZAg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:09:35.495711Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.03061","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:327a71cfa004817e7dccbca0fe6584b5c0c06bc734f2d26973c0c6d449e6dc45","sha256:2278f79f1d04159b3cf204f01afec5480c9cd6bb66ca8c6c87bd6818644e5b31"],"state_sha256":"9b68b93dadae37036ffa0272e825c35b698518d6e29ba3695abbcda566870fef"}