{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:K6VU4ECAZU52C6ZSOI4CWWAQ5J","short_pith_number":"pith:K6VU4ECA","schema_version":"1.0","canonical_sha256":"57ab4e1040cd3ba17b3272382b5810ea594bb12f2ff410d27652d29ba865a5d5","source":{"kind":"arxiv","id":"2106.03027","version":3},"attestation_state":"computed","paper":{"title":"Model Zoo: A Growing \"Brain\" That Learns Continually","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Pratik Chaudhari, Rahul Ramesh","submitted_at":"2021-06-06T04:25:09Z","abstract_excerpt":"This paper argues that continual learning methods can benefit by splitting the capacity of the learner across multiple models. We use statistical learning theory and experimental analysis to show how multiple tasks can interact with each other in a non-trivial fashion when a single model is trained on them. The generalization error on a particular task can improve when it is trained with synergistic tasks, but can also deteriorate when trained with competing tasks. This theory motivates our method named Model Zoo which, inspired from the boosting literature, grows an ensemble of small models, "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2106.03027","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-06-06T04:25:09Z","cross_cats_sorted":[],"title_canon_sha256":"c1cd9c6d449dbc1a3e096626b85b681494b1d690a371164788f290eac9d43cad","abstract_canon_sha256":"efccbd0d97f58527d97d22fccaf96b3541b1358230c358432fc1b4b2ec5a0b0b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:15:32.673605Z","signature_b64":"ilocgWyB/ZXFKX3d5A2PVnz7glaj/+vfWZwA0GQ0FWctFMnmonQ3S/N8aAkdVhqsj8GwfgbgTkHAXwO+hOCFAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"57ab4e1040cd3ba17b3272382b5810ea594bb12f2ff410d27652d29ba865a5d5","last_reissued_at":"2026-07-05T08:15:32.673098Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:15:32.673098Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Model Zoo: A Growing \"Brain\" That Learns Continually","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Pratik Chaudhari, Rahul Ramesh","submitted_at":"2021-06-06T04:25:09Z","abstract_excerpt":"This paper argues that continual learning methods can benefit by splitting the capacity of the learner across multiple models. We use statistical learning theory and experimental analysis to show how multiple tasks can interact with each other in a non-trivial fashion when a single model is trained on them. The generalization error on a particular task can improve when it is trained with synergistic tasks, but can also deteriorate when trained with competing tasks. This theory motivates our method named Model Zoo which, inspired from the boosting literature, grows an ensemble of small models, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.03027","kind":"arxiv","version":3},"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/2106.03027/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2106.03027","created_at":"2026-07-05T08:15:32.673154+00:00"},{"alias_kind":"arxiv_version","alias_value":"2106.03027v3","created_at":"2026-07-05T08:15:32.673154+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.03027","created_at":"2026-07-05T08:15:32.673154+00:00"},{"alias_kind":"pith_short_12","alias_value":"K6VU4ECAZU52","created_at":"2026-07-05T08:15:32.673154+00:00"},{"alias_kind":"pith_short_16","alias_value":"K6VU4ECAZU52C6ZS","created_at":"2026-07-05T08:15:32.673154+00:00"},{"alias_kind":"pith_short_8","alias_value":"K6VU4ECA","created_at":"2026-07-05T08:15:32.673154+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.01858","citing_title":"Polaris: Scaling Up Instruction-Guided Image Generation Towards Millions of Personalized Style Needs","ref_index":92,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/K6VU4ECAZU52C6ZSOI4CWWAQ5J","json":"https://pith.science/pith/K6VU4ECAZU52C6ZSOI4CWWAQ5J.json","graph_json":"https://pith.science/api/pith-number/K6VU4ECAZU52C6ZSOI4CWWAQ5J/graph.json","events_json":"https://pith.science/api/pith-number/K6VU4ECAZU52C6ZSOI4CWWAQ5J/events.json","paper":"https://pith.science/paper/K6VU4ECA"},"agent_actions":{"view_html":"https://pith.science/pith/K6VU4ECAZU52C6ZSOI4CWWAQ5J","download_json":"https://pith.science/pith/K6VU4ECAZU52C6ZSOI4CWWAQ5J.json","view_paper":"https://pith.science/paper/K6VU4ECA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2106.03027&json=true","fetch_graph":"https://pith.science/api/pith-number/K6VU4ECAZU52C6ZSOI4CWWAQ5J/graph.json","fetch_events":"https://pith.science/api/pith-number/K6VU4ECAZU52C6ZSOI4CWWAQ5J/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K6VU4ECAZU52C6ZSOI4CWWAQ5J/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K6VU4ECAZU52C6ZSOI4CWWAQ5J/action/storage_attestation","attest_author":"https://pith.science/pith/K6VU4ECAZU52C6ZSOI4CWWAQ5J/action/author_attestation","sign_citation":"https://pith.science/pith/K6VU4ECAZU52C6ZSOI4CWWAQ5J/action/citation_signature","submit_replication":"https://pith.science/pith/K6VU4ECAZU52C6ZSOI4CWWAQ5J/action/replication_record"}},"created_at":"2026-07-05T08:15:32.673154+00:00","updated_at":"2026-07-05T08:15:32.673154+00:00"}