{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:PS7QLAK4ULDMOF7Z5TDHTXCHV4","short_pith_number":"pith:PS7QLAK4","canonical_record":{"source":{"id":"2008.05808","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-08-13T10:45:31Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"e3c477dee48ca87c79fc533f86dbde33dd8888f2fc01ae53d279cac1fe4f21c7","abstract_canon_sha256":"f48f967266f8aed199d0077441163a3ea054ead0d9f3da5b5c3214227a08d18a"},"schema_version":"1.0"},"canonical_sha256":"7cbf05815ca2c6c717f9ecc679dc47af360ea4dcc74914edd888827609a927ba","source":{"kind":"arxiv","id":"2008.05808","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2008.05808","created_at":"2026-07-05T01:26:57Z"},{"alias_kind":"arxiv_version","alias_value":"2008.05808v1","created_at":"2026-07-05T01:26:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2008.05808","created_at":"2026-07-05T01:26:57Z"},{"alias_kind":"pith_short_12","alias_value":"PS7QLAK4ULDM","created_at":"2026-07-05T01:26:57Z"},{"alias_kind":"pith_short_16","alias_value":"PS7QLAK4ULDMOF7Z","created_at":"2026-07-05T01:26:57Z"},{"alias_kind":"pith_short_8","alias_value":"PS7QLAK4","created_at":"2026-07-05T01:26:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:PS7QLAK4ULDMOF7Z5TDHTXCHV4","target":"record","payload":{"canonical_record":{"source":{"id":"2008.05808","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-08-13T10:45:31Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"e3c477dee48ca87c79fc533f86dbde33dd8888f2fc01ae53d279cac1fe4f21c7","abstract_canon_sha256":"f48f967266f8aed199d0077441163a3ea054ead0d9f3da5b5c3214227a08d18a"},"schema_version":"1.0"},"canonical_sha256":"7cbf05815ca2c6c717f9ecc679dc47af360ea4dcc74914edd888827609a927ba","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:26:57.667264Z","signature_b64":"tT3KGon/lcfVWzOfOsHCl2dcR/8xnXKnd4OfUCciKwSGd6QiCIbJO3Ba257SH/YFmuIBsT2bTLLlq3YrEeu4DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7cbf05815ca2c6c717f9ecc679dc47af360ea4dcc74914edd888827609a927ba","last_reissued_at":"2026-07-05T01:26:57.666845Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:26:57.666845Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2008.05808","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":"2026-07-05T01:26:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7bzOVNw3yUtP9dpf21r9AH8JBcNCqhTmZy56Jb42fWtr2lNb4gopPit5FfSGrTSnFJNyQkgadpXuFSek2WqQAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-18T15:19:28.443548Z"},"content_sha256":"11e6aa8228443f3053a841ecc31c805e468c5bbef0f9b368cfa5c388d46c8ee6","schema_version":"1.0","event_id":"sha256:11e6aa8228443f3053a841ecc31c805e468c5bbef0f9b368cfa5c388d46c8ee6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:PS7QLAK4ULDMOF7Z5TDHTXCHV4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Small Towers Make Big Differences","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Bo Dai, Christopher Fifty, Dong Lin, Ed H. Chi, Lichan Hong, Yuyan Wang, Zhe Zhao","submitted_at":"2020-08-13T10:45:31Z","abstract_excerpt":"Multi-task learning aims at solving multiple machine learning tasks at the same time. A good solution to a multi-task learning problem should be generalizable in addition to being Pareto optimal. In this paper, we provide some insights on understanding the trade-off between Pareto efficiency and generalization as a result of parameterization in multi-task deep learning models. As a multi-objective optimization problem, enough parameterization is needed for handling task conflicts in a constrained solution space; however, from a multi-task generalization perspective, over-parameterization under"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2008.05808","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/2008.05808/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":"2026-07-05T01:26:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9bLk8q7GHhCzyGwZqnhtRA4XhQep4Jxgzm0KsnaSIMuxGRSijIJfrOo+smwc5qqYZg50jBA+90UBG7titQ+4Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-18T15:19:28.443911Z"},"content_sha256":"b2801430f9c63d7423f61437ec5019b18778951f252247cc35bd010ddbfcfe4a","schema_version":"1.0","event_id":"sha256:b2801430f9c63d7423f61437ec5019b18778951f252247cc35bd010ddbfcfe4a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PS7QLAK4ULDMOF7Z5TDHTXCHV4/bundle.json","state_url":"https://pith.science/pith/PS7QLAK4ULDMOF7Z5TDHTXCHV4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PS7QLAK4ULDMOF7Z5TDHTXCHV4/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":"2026-07-18T15:19:28Z","links":{"resolver":"https://pith.science/pith/PS7QLAK4ULDMOF7Z5TDHTXCHV4","bundle":"https://pith.science/pith/PS7QLAK4ULDMOF7Z5TDHTXCHV4/bundle.json","state":"https://pith.science/pith/PS7QLAK4ULDMOF7Z5TDHTXCHV4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PS7QLAK4ULDMOF7Z5TDHTXCHV4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:PS7QLAK4ULDMOF7Z5TDHTXCHV4","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":"f48f967266f8aed199d0077441163a3ea054ead0d9f3da5b5c3214227a08d18a","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-08-13T10:45:31Z","title_canon_sha256":"e3c477dee48ca87c79fc533f86dbde33dd8888f2fc01ae53d279cac1fe4f21c7"},"schema_version":"1.0","source":{"id":"2008.05808","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2008.05808","created_at":"2026-07-05T01:26:57Z"},{"alias_kind":"arxiv_version","alias_value":"2008.05808v1","created_at":"2026-07-05T01:26:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2008.05808","created_at":"2026-07-05T01:26:57Z"},{"alias_kind":"pith_short_12","alias_value":"PS7QLAK4ULDM","created_at":"2026-07-05T01:26:57Z"},{"alias_kind":"pith_short_16","alias_value":"PS7QLAK4ULDMOF7Z","created_at":"2026-07-05T01:26:57Z"},{"alias_kind":"pith_short_8","alias_value":"PS7QLAK4","created_at":"2026-07-05T01:26:57Z"}],"graph_snapshots":[{"event_id":"sha256:b2801430f9c63d7423f61437ec5019b18778951f252247cc35bd010ddbfcfe4a","target":"graph","created_at":"2026-07-05T01:26:57Z","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/2008.05808/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multi-task learning aims at solving multiple machine learning tasks at the same time. A good solution to a multi-task learning problem should be generalizable in addition to being Pareto optimal. In this paper, we provide some insights on understanding the trade-off between Pareto efficiency and generalization as a result of parameterization in multi-task deep learning models. As a multi-objective optimization problem, enough parameterization is needed for handling task conflicts in a constrained solution space; however, from a multi-task generalization perspective, over-parameterization under","authors_text":"Bo Dai, Christopher Fifty, Dong Lin, Ed H. Chi, Lichan Hong, Yuyan Wang, Zhe Zhao","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-08-13T10:45:31Z","title":"Small Towers Make Big Differences"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2008.05808","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":"sha256:11e6aa8228443f3053a841ecc31c805e468c5bbef0f9b368cfa5c388d46c8ee6","target":"record","created_at":"2026-07-05T01:26:57Z","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":"f48f967266f8aed199d0077441163a3ea054ead0d9f3da5b5c3214227a08d18a","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-08-13T10:45:31Z","title_canon_sha256":"e3c477dee48ca87c79fc533f86dbde33dd8888f2fc01ae53d279cac1fe4f21c7"},"schema_version":"1.0","source":{"id":"2008.05808","kind":"arxiv","version":1}},"canonical_sha256":"7cbf05815ca2c6c717f9ecc679dc47af360ea4dcc74914edd888827609a927ba","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7cbf05815ca2c6c717f9ecc679dc47af360ea4dcc74914edd888827609a927ba","first_computed_at":"2026-07-05T01:26:57.666845Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:26:57.666845Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tT3KGon/lcfVWzOfOsHCl2dcR/8xnXKnd4OfUCciKwSGd6QiCIbJO3Ba257SH/YFmuIBsT2bTLLlq3YrEeu4DQ==","signature_status":"signed_v1","signed_at":"2026-07-05T01:26:57.667264Z","signed_message":"canonical_sha256_bytes"},"source_id":"2008.05808","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:11e6aa8228443f3053a841ecc31c805e468c5bbef0f9b368cfa5c388d46c8ee6","sha256:b2801430f9c63d7423f61437ec5019b18778951f252247cc35bd010ddbfcfe4a"],"state_sha256":"fabf15c37531022c63d3224814aa7354a02eebcb2bfc8e15907aa71f264a054c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"n/YKqfoIY5s0h4hCrPcp2KZu7TW51ZTrpDYUXYyzJxpFMqfDEHahyngwhNUHL3URK6hDW7JTNukwodIIEcQ3AA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-18T15:19:28.446007Z","bundle_sha256":"54bbac3a7aed732916a3fa32a61c7d52b64efbc61345b9cf89729b8aa08bd034"}}