{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:MESEFEZNEFI75N3T22BPFD6AKX","short_pith_number":"pith:MESEFEZN","canonical_record":{"source":{"id":"2007.10527","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-07-20T23:26:16Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"bb33be820c9c0f23f81b378280578bf6089b897462085ff01630bb5c2c59a7c2","abstract_canon_sha256":"637f809c57cb3f25bb37f081b6be4342ed9a06770b33df3bcd9ff10212c29231"},"schema_version":"1.0"},"canonical_sha256":"612442932d2151feb773d682f28fc055d0577b340d033c15bb8302856c330d02","source":{"kind":"arxiv","id":"2007.10527","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2007.10527","created_at":"2026-07-05T02:04:42Z"},{"alias_kind":"arxiv_version","alias_value":"2007.10527v2","created_at":"2026-07-05T02:04:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.10527","created_at":"2026-07-05T02:04:42Z"},{"alias_kind":"pith_short_12","alias_value":"MESEFEZNEFI7","created_at":"2026-07-05T02:04:42Z"},{"alias_kind":"pith_short_16","alias_value":"MESEFEZNEFI75N3T","created_at":"2026-07-05T02:04:42Z"},{"alias_kind":"pith_short_8","alias_value":"MESEFEZN","created_at":"2026-07-05T02:04:42Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:MESEFEZNEFI75N3T22BPFD6AKX","target":"record","payload":{"canonical_record":{"source":{"id":"2007.10527","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-07-20T23:26:16Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"bb33be820c9c0f23f81b378280578bf6089b897462085ff01630bb5c2c59a7c2","abstract_canon_sha256":"637f809c57cb3f25bb37f081b6be4342ed9a06770b33df3bcd9ff10212c29231"},"schema_version":"1.0"},"canonical_sha256":"612442932d2151feb773d682f28fc055d0577b340d033c15bb8302856c330d02","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:04:42.190340Z","signature_b64":"UlcYEm6kScAzEvmowQMSXvsuLQ00fUQ82Ns68+zvqmEl06EUl3Q1rdLIJn4+jM8MBSi7FigyFRPiFNZprT/qAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"612442932d2151feb773d682f28fc055d0577b340d033c15bb8302856c330d02","last_reissued_at":"2026-07-05T02:04:42.189899Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:04:42.189899Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2007.10527","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:04:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1yZgN07MoNlkdjhXDJKU4PohVPMnv5MasIG1rdm7pPr5FQQwI0RaMweofPikM1kW1MlRgfTrw8k9D68EzrX8BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T18:45:00.414757Z"},"content_sha256":"953a944cfc2cfe312b80a073ef6396d971e9b7e1afd3ac3f436d46b7e2e6bd87","schema_version":"1.0","event_id":"sha256:953a944cfc2cfe312b80a073ef6396d971e9b7e1afd3ac3f436d46b7e2e6bd87"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:MESEFEZNEFI75N3T22BPFD6AKX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Navigating the Trade-Off between Multi-Task Learning and Learning to Multitask in Deep Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Jonathan D. Cohen, Maia Hamin, Sachin Ravi, Sebastian Musslick, Theodore L. Willke","submitted_at":"2020-07-20T23:26:16Z","abstract_excerpt":"The terms multi-task learning and multitasking are easily confused. Multi-task learning refers to a paradigm in machine learning in which a network is trained on various related tasks to facilitate the acquisition of tasks. In contrast, multitasking is used to indicate, especially in the cognitive science literature, the ability to execute multiple tasks simultaneously. While multi-task learning exploits the discovery of common structure between tasks in the form of shared representations, multitasking is promoted by separating representations between tasks to avoid processing interference. He"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.10527","kind":"arxiv","version":2},"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/2007.10527/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:04:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QjUNgpidK+f5JoCexXnBqshkP3qZum2X2NVyWzeZNcSPKq+RU5K5+MzIWwXadxUHbxAvMsuAu/lHiOLf8iREBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T18:45:00.415135Z"},"content_sha256":"c51dd6ed808d12077752a9ffd14b39a58ceb182752caa6c974ae75e04c24c0f1","schema_version":"1.0","event_id":"sha256:c51dd6ed808d12077752a9ffd14b39a58ceb182752caa6c974ae75e04c24c0f1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MESEFEZNEFI75N3T22BPFD6AKX/bundle.json","state_url":"https://pith.science/pith/MESEFEZNEFI75N3T22BPFD6AKX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MESEFEZNEFI75N3T22BPFD6AKX/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-06T18:45:00Z","links":{"resolver":"https://pith.science/pith/MESEFEZNEFI75N3T22BPFD6AKX","bundle":"https://pith.science/pith/MESEFEZNEFI75N3T22BPFD6AKX/bundle.json","state":"https://pith.science/pith/MESEFEZNEFI75N3T22BPFD6AKX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MESEFEZNEFI75N3T22BPFD6AKX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:MESEFEZNEFI75N3T22BPFD6AKX","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":"637f809c57cb3f25bb37f081b6be4342ed9a06770b33df3bcd9ff10212c29231","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-07-20T23:26:16Z","title_canon_sha256":"bb33be820c9c0f23f81b378280578bf6089b897462085ff01630bb5c2c59a7c2"},"schema_version":"1.0","source":{"id":"2007.10527","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2007.10527","created_at":"2026-07-05T02:04:42Z"},{"alias_kind":"arxiv_version","alias_value":"2007.10527v2","created_at":"2026-07-05T02:04:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.10527","created_at":"2026-07-05T02:04:42Z"},{"alias_kind":"pith_short_12","alias_value":"MESEFEZNEFI7","created_at":"2026-07-05T02:04:42Z"},{"alias_kind":"pith_short_16","alias_value":"MESEFEZNEFI75N3T","created_at":"2026-07-05T02:04:42Z"},{"alias_kind":"pith_short_8","alias_value":"MESEFEZN","created_at":"2026-07-05T02:04:42Z"}],"graph_snapshots":[{"event_id":"sha256:c51dd6ed808d12077752a9ffd14b39a58ceb182752caa6c974ae75e04c24c0f1","target":"graph","created_at":"2026-07-05T02:04:42Z","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/2007.10527/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The terms multi-task learning and multitasking are easily confused. Multi-task learning refers to a paradigm in machine learning in which a network is trained on various related tasks to facilitate the acquisition of tasks. In contrast, multitasking is used to indicate, especially in the cognitive science literature, the ability to execute multiple tasks simultaneously. While multi-task learning exploits the discovery of common structure between tasks in the form of shared representations, multitasking is promoted by separating representations between tasks to avoid processing interference. He","authors_text":"Jonathan D. Cohen, Maia Hamin, Sachin Ravi, Sebastian Musslick, Theodore L. Willke","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-07-20T23:26:16Z","title":"Navigating the Trade-Off between Multi-Task Learning and Learning to Multitask in Deep Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.10527","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":"sha256:953a944cfc2cfe312b80a073ef6396d971e9b7e1afd3ac3f436d46b7e2e6bd87","target":"record","created_at":"2026-07-05T02:04:42Z","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":"637f809c57cb3f25bb37f081b6be4342ed9a06770b33df3bcd9ff10212c29231","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-07-20T23:26:16Z","title_canon_sha256":"bb33be820c9c0f23f81b378280578bf6089b897462085ff01630bb5c2c59a7c2"},"schema_version":"1.0","source":{"id":"2007.10527","kind":"arxiv","version":2}},"canonical_sha256":"612442932d2151feb773d682f28fc055d0577b340d033c15bb8302856c330d02","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"612442932d2151feb773d682f28fc055d0577b340d033c15bb8302856c330d02","first_computed_at":"2026-07-05T02:04:42.189899Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:04:42.189899Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UlcYEm6kScAzEvmowQMSXvsuLQ00fUQ82Ns68+zvqmEl06EUl3Q1rdLIJn4+jM8MBSi7FigyFRPiFNZprT/qAg==","signature_status":"signed_v1","signed_at":"2026-07-05T02:04:42.190340Z","signed_message":"canonical_sha256_bytes"},"source_id":"2007.10527","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:953a944cfc2cfe312b80a073ef6396d971e9b7e1afd3ac3f436d46b7e2e6bd87","sha256:c51dd6ed808d12077752a9ffd14b39a58ceb182752caa6c974ae75e04c24c0f1"],"state_sha256":"12ba9ae69714043d8dee9d883ca448275b4bde1106d939509f18a050db83c1ed"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kgsQK0YqIKgtZaX5RDvyMedXeNZ+avgOerfvew3Dwwo+H3GGC/3hXlisqhrcvEwih/5MPDxIwaMLWvLP+nskAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T18:45:00.417171Z","bundle_sha256":"fa5c26696d6a2766acee68849369d8e98e8d490b8a872ef75f4a524c17005192"}}