{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:N53M6QZ7DIIZ43DBWNFDJ7GWW6","short_pith_number":"pith:N53M6QZ7","canonical_record":{"source":{"id":"2402.16848","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-26T18:59:52Z","cross_cats_sorted":[],"title_canon_sha256":"b31953af40e1fced568eb5b56dd98f2daea69c3094355c8c9c18a2d0e8c0ea95","abstract_canon_sha256":"65b239f70099e3b6cb2611ac281cf07bcfcd9be151c4ae5a5c366768dc596a5e"},"schema_version":"1.0"},"canonical_sha256":"6f76cf433f1a119e6c61b34a34fcd6b7b10b3f6fefc00a3102ae7a8f3ab6f916","source":{"kind":"arxiv","id":"2402.16848","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.16848","created_at":"2026-07-05T07:49:26Z"},{"alias_kind":"arxiv_version","alias_value":"2402.16848v1","created_at":"2026-07-05T07:49:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.16848","created_at":"2026-07-05T07:49:26Z"},{"alias_kind":"pith_short_12","alias_value":"N53M6QZ7DIIZ","created_at":"2026-07-05T07:49:26Z"},{"alias_kind":"pith_short_16","alias_value":"N53M6QZ7DIIZ43DB","created_at":"2026-07-05T07:49:26Z"},{"alias_kind":"pith_short_8","alias_value":"N53M6QZ7","created_at":"2026-07-05T07:49:26Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:N53M6QZ7DIIZ43DBWNFDJ7GWW6","target":"record","payload":{"canonical_record":{"source":{"id":"2402.16848","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-26T18:59:52Z","cross_cats_sorted":[],"title_canon_sha256":"b31953af40e1fced568eb5b56dd98f2daea69c3094355c8c9c18a2d0e8c0ea95","abstract_canon_sha256":"65b239f70099e3b6cb2611ac281cf07bcfcd9be151c4ae5a5c366768dc596a5e"},"schema_version":"1.0"},"canonical_sha256":"6f76cf433f1a119e6c61b34a34fcd6b7b10b3f6fefc00a3102ae7a8f3ab6f916","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:49:26.738494Z","signature_b64":"SJ0Ou1FG1Gmrnt5CIwc6g/o1cH7HxBdbWz33JlXA6PV4ky6YvUquX0elx8AvLJ9EEfzeuJnUX1SOErDTkASACw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6f76cf433f1a119e6c61b34a34fcd6b7b10b3f6fefc00a3102ae7a8f3ab6f916","last_reissued_at":"2026-07-05T07:49:26.737994Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:49:26.737994Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2402.16848","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:49:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AVL+sFAbuuBZ1eINT/qTGH7qZFMNx6TEenKDC4VWhKclWJbGRoEv0wX35JceKpsmdy4fLM+rTTYEDJxro5nBBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T01:31:03.411621Z"},"content_sha256":"9564a01509f7aface00785a0fa47471b0dd6956fca8defa175a2332875b49190","schema_version":"1.0","event_id":"sha256:9564a01509f7aface00785a0fa47471b0dd6956fca8defa175a2332875b49190"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:N53M6QZ7DIIZ43DBWNFDJ7GWW6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"InterroGate: Learning to Share, Specialize, and Prune Representations for Multi-task Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Amelie Royer, Babak Ehteshami Bejnordi, Christos Louizos, Gaurav Kumar, Mohsen Ghafoorian, Tijmen Blankevoort","submitted_at":"2024-02-26T18:59:52Z","abstract_excerpt":"Jointly learning multiple tasks with a unified model can improve accuracy and data efficiency, but it faces the challenge of task interference, where optimizing one task objective may inadvertently compromise the performance of another. A solution to mitigate this issue is to allocate task-specific parameters, free from interference, on top of shared features. However, manually designing such architectures is cumbersome, as practitioners need to balance between the overall performance across all tasks and the higher computational cost induced by the newly added parameters. In this work, we pro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.16848","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/2402.16848/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":"2026-07-05T07:49:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1pn6GGEQDq6V0QK7JyzVcmKtwnPy++vHEN4axSuSGRjukXJdG2zSau/xURiJYrPR32iONhYaFyzzLT221zP8Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T01:31:03.412537Z"},"content_sha256":"7829fc614f1042c4b11fc3298353d4bd2ebc6738ffbb1d057ec2750605abf4a5","schema_version":"1.0","event_id":"sha256:7829fc614f1042c4b11fc3298353d4bd2ebc6738ffbb1d057ec2750605abf4a5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/N53M6QZ7DIIZ43DBWNFDJ7GWW6/bundle.json","state_url":"https://pith.science/pith/N53M6QZ7DIIZ43DBWNFDJ7GWW6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/N53M6QZ7DIIZ43DBWNFDJ7GWW6/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":"2026-08-07T01:31:03Z","links":{"resolver":"https://pith.science/pith/N53M6QZ7DIIZ43DBWNFDJ7GWW6","bundle":"https://pith.science/pith/N53M6QZ7DIIZ43DBWNFDJ7GWW6/bundle.json","state":"https://pith.science/pith/N53M6QZ7DIIZ43DBWNFDJ7GWW6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/N53M6QZ7DIIZ43DBWNFDJ7GWW6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:N53M6QZ7DIIZ43DBWNFDJ7GWW6","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":"65b239f70099e3b6cb2611ac281cf07bcfcd9be151c4ae5a5c366768dc596a5e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-26T18:59:52Z","title_canon_sha256":"b31953af40e1fced568eb5b56dd98f2daea69c3094355c8c9c18a2d0e8c0ea95"},"schema_version":"1.0","source":{"id":"2402.16848","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.16848","created_at":"2026-07-05T07:49:26Z"},{"alias_kind":"arxiv_version","alias_value":"2402.16848v1","created_at":"2026-07-05T07:49:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.16848","created_at":"2026-07-05T07:49:26Z"},{"alias_kind":"pith_short_12","alias_value":"N53M6QZ7DIIZ","created_at":"2026-07-05T07:49:26Z"},{"alias_kind":"pith_short_16","alias_value":"N53M6QZ7DIIZ43DB","created_at":"2026-07-05T07:49:26Z"},{"alias_kind":"pith_short_8","alias_value":"N53M6QZ7","created_at":"2026-07-05T07:49:26Z"}],"graph_snapshots":[{"event_id":"sha256:7829fc614f1042c4b11fc3298353d4bd2ebc6738ffbb1d057ec2750605abf4a5","target":"graph","created_at":"2026-07-05T07:49:26Z","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/2402.16848/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Jointly learning multiple tasks with a unified model can improve accuracy and data efficiency, but it faces the challenge of task interference, where optimizing one task objective may inadvertently compromise the performance of another. A solution to mitigate this issue is to allocate task-specific parameters, free from interference, on top of shared features. However, manually designing such architectures is cumbersome, as practitioners need to balance between the overall performance across all tasks and the higher computational cost induced by the newly added parameters. In this work, we pro","authors_text":"Amelie Royer, Babak Ehteshami Bejnordi, Christos Louizos, Gaurav Kumar, Mohsen Ghafoorian, Tijmen Blankevoort","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-26T18:59:52Z","title":"InterroGate: Learning to Share, Specialize, and Prune Representations for Multi-task Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.16848","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":"sha256:9564a01509f7aface00785a0fa47471b0dd6956fca8defa175a2332875b49190","target":"record","created_at":"2026-07-05T07:49:26Z","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":"65b239f70099e3b6cb2611ac281cf07bcfcd9be151c4ae5a5c366768dc596a5e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-26T18:59:52Z","title_canon_sha256":"b31953af40e1fced568eb5b56dd98f2daea69c3094355c8c9c18a2d0e8c0ea95"},"schema_version":"1.0","source":{"id":"2402.16848","kind":"arxiv","version":1}},"canonical_sha256":"6f76cf433f1a119e6c61b34a34fcd6b7b10b3f6fefc00a3102ae7a8f3ab6f916","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6f76cf433f1a119e6c61b34a34fcd6b7b10b3f6fefc00a3102ae7a8f3ab6f916","first_computed_at":"2026-07-05T07:49:26.737994Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:49:26.737994Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"SJ0Ou1FG1Gmrnt5CIwc6g/o1cH7HxBdbWz33JlXA6PV4ky6YvUquX0elx8AvLJ9EEfzeuJnUX1SOErDTkASACw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:49:26.738494Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.16848","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9564a01509f7aface00785a0fa47471b0dd6956fca8defa175a2332875b49190","sha256:7829fc614f1042c4b11fc3298353d4bd2ebc6738ffbb1d057ec2750605abf4a5"],"state_sha256":"63797949fb773d2cfa10ee37956f89fa8aac92dc717bce89e171046a3e81f21c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4975h9DE2bXcskGsO+O+t0L/lkoz0OgWdKMWPOhhTJ0Ynyr0dpotgv5ETz7vX3WhFmyDKKOdLjqN+TxkDGIJDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T01:31:03.419069Z","bundle_sha256":"0b457a0ce2b61a9e191dd91c57c8816a3e9be796083db2605ac13e465f8344d2"}}