{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:GPNEEX6PT4FY3SPQX5QGOATPXE","short_pith_number":"pith:GPNEEX6P","canonical_record":{"source":{"id":"2302.11529","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-22T18:11:25Z","cross_cats_sorted":[],"title_canon_sha256":"d30a7db11d05ecb6763a1f97a419f53ad5dff7032e328d7de22b784f2f2c5efd","abstract_canon_sha256":"e418f32442ed6d7559b56cc812cc11f0e56ba23db93e047baff757720eb79065"},"schema_version":"1.0"},"canonical_sha256":"33da425fcf9f0b8dc9f0bf6067026fb9303addee9a1315052752067302c460a7","source":{"kind":"arxiv","id":"2302.11529","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.11529","created_at":"2026-07-05T07:38:08Z"},{"alias_kind":"arxiv_version","alias_value":"2302.11529v2","created_at":"2026-07-05T07:38:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.11529","created_at":"2026-07-05T07:38:08Z"},{"alias_kind":"pith_short_12","alias_value":"GPNEEX6PT4FY","created_at":"2026-07-05T07:38:08Z"},{"alias_kind":"pith_short_16","alias_value":"GPNEEX6PT4FY3SPQ","created_at":"2026-07-05T07:38:08Z"},{"alias_kind":"pith_short_8","alias_value":"GPNEEX6P","created_at":"2026-07-05T07:38:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:GPNEEX6PT4FY3SPQX5QGOATPXE","target":"record","payload":{"canonical_record":{"source":{"id":"2302.11529","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-22T18:11:25Z","cross_cats_sorted":[],"title_canon_sha256":"d30a7db11d05ecb6763a1f97a419f53ad5dff7032e328d7de22b784f2f2c5efd","abstract_canon_sha256":"e418f32442ed6d7559b56cc812cc11f0e56ba23db93e047baff757720eb79065"},"schema_version":"1.0"},"canonical_sha256":"33da425fcf9f0b8dc9f0bf6067026fb9303addee9a1315052752067302c460a7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:38:08.182254Z","signature_b64":"vnqaGH+j6DN89rPc3ERWt4ozpLGSp2Hh+/Nd2j3fH8t+14M5+6o0zepEhPYG/kmiUvuE5RtwS1jRZsmD+UfFCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"33da425fcf9f0b8dc9f0bf6067026fb9303addee9a1315052752067302c460a7","last_reissued_at":"2026-07-05T07:38:08.181842Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:38:08.181842Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2302.11529","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":"2026-07-05T07:38:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0Vt+8Hr9ID+ReMy0v/qELbpewzFpoRrQT9Dxn57Dr7gun52cea131iGJTuOQonR2pbn4DfK0FKb1CHIuxVGiCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T03:51:00.557217Z"},"content_sha256":"1819b736fcd6225ad0bbb704197ea5de6aefe64f2218eb299510461e24f02af7","schema_version":"1.0","event_id":"sha256:1819b736fcd6225ad0bbb704197ea5de6aefe64f2218eb299510461e24f02af7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:GPNEEX6PT4FY3SPQX5QGOATPXE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Modular Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Edoardo Maria Ponti, Ivan Vuli\\'c, Jonas Pfeiffer, Sebastian Ruder","submitted_at":"2023-02-22T18:11:25Z","abstract_excerpt":"Transfer learning has recently become the dominant paradigm of machine learning. Pre-trained models fine-tuned for downstream tasks achieve better performance with fewer labelled examples. Nonetheless, it remains unclear how to develop models that specialise towards multiple tasks without incurring negative interference and that generalise systematically to non-identically distributed tasks. Modular deep learning has emerged as a promising solution to these challenges. In this framework, units of computation are often implemented as autonomous parameter-efficient modules. Information is condit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.11529","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/2302.11529/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":"2026-07-05T07:38:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VePs5fUGgfqpIISvFSGU1SxMFiepFKvVknQNmstS/jjlP6VX1hByrjmOMgzJfyMM3slOvAhmKSgt2/NK4hneCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T03:51:00.557599Z"},"content_sha256":"be7456cffb7bb25ed680802c358f1e24deaa78c3f55ebbc3a44b3fa014c084b5","schema_version":"1.0","event_id":"sha256:be7456cffb7bb25ed680802c358f1e24deaa78c3f55ebbc3a44b3fa014c084b5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GPNEEX6PT4FY3SPQX5QGOATPXE/bundle.json","state_url":"https://pith.science/pith/GPNEEX6PT4FY3SPQX5QGOATPXE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GPNEEX6PT4FY3SPQX5QGOATPXE/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":"2026-07-07T03:51:00Z","links":{"resolver":"https://pith.science/pith/GPNEEX6PT4FY3SPQX5QGOATPXE","bundle":"https://pith.science/pith/GPNEEX6PT4FY3SPQX5QGOATPXE/bundle.json","state":"https://pith.science/pith/GPNEEX6PT4FY3SPQX5QGOATPXE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GPNEEX6PT4FY3SPQX5QGOATPXE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:GPNEEX6PT4FY3SPQX5QGOATPXE","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":"e418f32442ed6d7559b56cc812cc11f0e56ba23db93e047baff757720eb79065","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-22T18:11:25Z","title_canon_sha256":"d30a7db11d05ecb6763a1f97a419f53ad5dff7032e328d7de22b784f2f2c5efd"},"schema_version":"1.0","source":{"id":"2302.11529","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.11529","created_at":"2026-07-05T07:38:08Z"},{"alias_kind":"arxiv_version","alias_value":"2302.11529v2","created_at":"2026-07-05T07:38:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.11529","created_at":"2026-07-05T07:38:08Z"},{"alias_kind":"pith_short_12","alias_value":"GPNEEX6PT4FY","created_at":"2026-07-05T07:38:08Z"},{"alias_kind":"pith_short_16","alias_value":"GPNEEX6PT4FY3SPQ","created_at":"2026-07-05T07:38:08Z"},{"alias_kind":"pith_short_8","alias_value":"GPNEEX6P","created_at":"2026-07-05T07:38:08Z"}],"graph_snapshots":[{"event_id":"sha256:be7456cffb7bb25ed680802c358f1e24deaa78c3f55ebbc3a44b3fa014c084b5","target":"graph","created_at":"2026-07-05T07:38:08Z","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/2302.11529/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Transfer learning has recently become the dominant paradigm of machine learning. Pre-trained models fine-tuned for downstream tasks achieve better performance with fewer labelled examples. Nonetheless, it remains unclear how to develop models that specialise towards multiple tasks without incurring negative interference and that generalise systematically to non-identically distributed tasks. Modular deep learning has emerged as a promising solution to these challenges. In this framework, units of computation are often implemented as autonomous parameter-efficient modules. Information is condit","authors_text":"Edoardo Maria Ponti, Ivan Vuli\\'c, Jonas Pfeiffer, Sebastian Ruder","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-22T18:11:25Z","title":"Modular Deep Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.11529","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":"sha256:1819b736fcd6225ad0bbb704197ea5de6aefe64f2218eb299510461e24f02af7","target":"record","created_at":"2026-07-05T07:38:08Z","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":"e418f32442ed6d7559b56cc812cc11f0e56ba23db93e047baff757720eb79065","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-22T18:11:25Z","title_canon_sha256":"d30a7db11d05ecb6763a1f97a419f53ad5dff7032e328d7de22b784f2f2c5efd"},"schema_version":"1.0","source":{"id":"2302.11529","kind":"arxiv","version":2}},"canonical_sha256":"33da425fcf9f0b8dc9f0bf6067026fb9303addee9a1315052752067302c460a7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"33da425fcf9f0b8dc9f0bf6067026fb9303addee9a1315052752067302c460a7","first_computed_at":"2026-07-05T07:38:08.181842Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:38:08.181842Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"vnqaGH+j6DN89rPc3ERWt4ozpLGSp2Hh+/Nd2j3fH8t+14M5+6o0zepEhPYG/kmiUvuE5RtwS1jRZsmD+UfFCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:38:08.182254Z","signed_message":"canonical_sha256_bytes"},"source_id":"2302.11529","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1819b736fcd6225ad0bbb704197ea5de6aefe64f2218eb299510461e24f02af7","sha256:be7456cffb7bb25ed680802c358f1e24deaa78c3f55ebbc3a44b3fa014c084b5"],"state_sha256":"74d859808bd820a4676022cac3e89416d36cc6db72309f1d81446cfd820d7de7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AH6U7na2ZgzKFSwWP+Kwp8iMUmRSTQI6up+HYBWBTt2K7iufz6TuwiJYpJt6cTexBoBPXNMZ5qpVgyWm9Mk2BA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T03:51:00.559590Z","bundle_sha256":"21ce90b0055e88549ec3e66dd68978172546863204b85baa1fa97e720ad2ba99"}}