{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:Q4IXINOBMOEW2BOAAUCIRYGDFE","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":"95ebaffe6ba57a786311836ea2baf5230200c0f53faeafdfe420ace05544946f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-10-24T23:29:46Z","title_canon_sha256":"582f27f02de6e66b640a49176060b8d1bec5d7b034043b624d124f43f9081157"},"schema_version":"1.0","source":{"id":"2310.16241","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.16241","created_at":"2026-07-05T07:04:54Z"},{"alias_kind":"arxiv_version","alias_value":"2310.16241v1","created_at":"2026-07-05T07:04:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.16241","created_at":"2026-07-05T07:04:54Z"},{"alias_kind":"pith_short_12","alias_value":"Q4IXINOBMOEW","created_at":"2026-07-05T07:04:54Z"},{"alias_kind":"pith_short_16","alias_value":"Q4IXINOBMOEW2BOA","created_at":"2026-07-05T07:04:54Z"},{"alias_kind":"pith_short_8","alias_value":"Q4IXINOB","created_at":"2026-07-05T07:04:54Z"}],"graph_snapshots":[{"event_id":"sha256:43c63d6d980728be90638e565be8b2eb15572f6679d5daf766f65640ae2be2d1","target":"graph","created_at":"2026-07-05T07:04:54Z","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/2310.16241/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"When a number of similar tasks have to be learned simultaneously, multi-task learning (MTL) models can attain significantly higher accuracy than single-task learning (STL) models. However, the advantage of MTL depends on various factors, such as the similarity of the tasks, the sizes of the datasets, and so on; in fact, some tasks might not benefit from MTL and may even incur a loss of accuracy compared to STL. Hence, the question arises: which tasks should be learned together? Domain experts can attempt to group tasks together following intuition, experience, and best practices, but manual gr","authors_text":"Afiya Ayman, Aron Laszka, Ayan Mukhopadhyay","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-10-24T23:29:46Z","title":"Task Grouping for Automated Multi-Task Machine Learning via Task Affinity Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.16241","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":"sha256:56b34d460c174e54e07b6e7164cbc0fa98d412842124345ab8da3068e14872de","target":"record","created_at":"2026-07-05T07:04:54Z","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":"95ebaffe6ba57a786311836ea2baf5230200c0f53faeafdfe420ace05544946f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-10-24T23:29:46Z","title_canon_sha256":"582f27f02de6e66b640a49176060b8d1bec5d7b034043b624d124f43f9081157"},"schema_version":"1.0","source":{"id":"2310.16241","kind":"arxiv","version":1}},"canonical_sha256":"87117435c163896d05c0050488e0c329156f462a27df913f3dd9555d170d9bb9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"87117435c163896d05c0050488e0c329156f462a27df913f3dd9555d170d9bb9","first_computed_at":"2026-07-05T07:04:54.367731Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:04:54.367731Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"cFM115qqUmBygzom5JJW8p29JMCbRfxhHiPThdcow5h4VbPRVbIsqvdKX37IXqwd/WNyTnwWwRfYxZ02v7xkCA==","signature_status":"signed_v1","signed_at":"2026-07-05T07:04:54.368092Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.16241","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:56b34d460c174e54e07b6e7164cbc0fa98d412842124345ab8da3068e14872de","sha256:43c63d6d980728be90638e565be8b2eb15572f6679d5daf766f65640ae2be2d1"],"state_sha256":"7cfe3fa5dbf7d317dfa5dc951191b25543e38d84b52a0ab651b92700602fc647"}