{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:RMOKYMY5ZWIGZCLG6WYSVGAOCF","short_pith_number":"pith:RMOKYMY5","schema_version":"1.0","canonical_sha256":"8b1cac331dcd906c8966f5b12a980e1151bd96d37cbd1d2409580b7e3d2690eb","source":{"kind":"arxiv","id":"2404.00885","version":1},"attestation_state":"computed","paper":{"title":"Modeling Output-Level Task Relatedness in Multi-Task Learning with Feedback Mechanism","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Fangtai Guo, Feng Gao, Jun Xu, Tianlei Jin, Xiangming Xi","submitted_at":"2024-04-01T03:27:34Z","abstract_excerpt":"Multi-task learning (MTL) is a paradigm that simultaneously learns multiple tasks by sharing information at different levels, enhancing the performance of each individual task. While previous research has primarily focused on feature-level or parameter-level task relatedness, and proposed various model architectures and learning algorithms to improve learning performance, we aim to explore output-level task relatedness. This approach introduces a posteriori information into the model, considering that different tasks may produce correlated outputs with mutual influences. We achieve this by inc"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2404.00885","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-04-01T03:27:34Z","cross_cats_sorted":[],"title_canon_sha256":"49c9dbed6deea1e80ff71130f56ab8c0d04952651bfbde1055ee5b4370d663ff","abstract_canon_sha256":"2fed644477a1fea757795b30cb225ca277e7151e49269a7770928b686f2d040d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:02:58.019073Z","signature_b64":"wmPKYQe8RQ5MtM6JgBPeYsQzXW/Ms0Z338psGZJ6miaA3EaE3v6CwIBDuMJaDXNjnTmq4MDzUx9bibvDGIavBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8b1cac331dcd906c8966f5b12a980e1151bd96d37cbd1d2409580b7e3d2690eb","last_reissued_at":"2026-07-05T08:02:58.018635Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:02:58.018635Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Modeling Output-Level Task Relatedness in Multi-Task Learning with Feedback Mechanism","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Fangtai Guo, Feng Gao, Jun Xu, Tianlei Jin, Xiangming Xi","submitted_at":"2024-04-01T03:27:34Z","abstract_excerpt":"Multi-task learning (MTL) is a paradigm that simultaneously learns multiple tasks by sharing information at different levels, enhancing the performance of each individual task. While previous research has primarily focused on feature-level or parameter-level task relatedness, and proposed various model architectures and learning algorithms to improve learning performance, we aim to explore output-level task relatedness. This approach introduces a posteriori information into the model, considering that different tasks may produce correlated outputs with mutual influences. We achieve this by inc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.00885","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/2404.00885/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2404.00885","created_at":"2026-07-05T08:02:58.018681+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.00885v1","created_at":"2026-07-05T08:02:58.018681+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.00885","created_at":"2026-07-05T08:02:58.018681+00:00"},{"alias_kind":"pith_short_12","alias_value":"RMOKYMY5ZWIG","created_at":"2026-07-05T08:02:58.018681+00:00"},{"alias_kind":"pith_short_16","alias_value":"RMOKYMY5ZWIGZCLG","created_at":"2026-07-05T08:02:58.018681+00:00"},{"alias_kind":"pith_short_8","alias_value":"RMOKYMY5","created_at":"2026-07-05T08:02:58.018681+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RMOKYMY5ZWIGZCLG6WYSVGAOCF","json":"https://pith.science/pith/RMOKYMY5ZWIGZCLG6WYSVGAOCF.json","graph_json":"https://pith.science/api/pith-number/RMOKYMY5ZWIGZCLG6WYSVGAOCF/graph.json","events_json":"https://pith.science/api/pith-number/RMOKYMY5ZWIGZCLG6WYSVGAOCF/events.json","paper":"https://pith.science/paper/RMOKYMY5"},"agent_actions":{"view_html":"https://pith.science/pith/RMOKYMY5ZWIGZCLG6WYSVGAOCF","download_json":"https://pith.science/pith/RMOKYMY5ZWIGZCLG6WYSVGAOCF.json","view_paper":"https://pith.science/paper/RMOKYMY5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.00885&json=true","fetch_graph":"https://pith.science/api/pith-number/RMOKYMY5ZWIGZCLG6WYSVGAOCF/graph.json","fetch_events":"https://pith.science/api/pith-number/RMOKYMY5ZWIGZCLG6WYSVGAOCF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RMOKYMY5ZWIGZCLG6WYSVGAOCF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RMOKYMY5ZWIGZCLG6WYSVGAOCF/action/storage_attestation","attest_author":"https://pith.science/pith/RMOKYMY5ZWIGZCLG6WYSVGAOCF/action/author_attestation","sign_citation":"https://pith.science/pith/RMOKYMY5ZWIGZCLG6WYSVGAOCF/action/citation_signature","submit_replication":"https://pith.science/pith/RMOKYMY5ZWIGZCLG6WYSVGAOCF/action/replication_record"}},"created_at":"2026-07-05T08:02:58.018681+00:00","updated_at":"2026-07-05T08:02:58.018681+00:00"}