{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:UKYOZXEX2ZM7RLGHG53UMIEVWR","short_pith_number":"pith:UKYOZXEX","schema_version":"1.0","canonical_sha256":"a2b0ecdc97d659f8acc73777462095b44acee26b5ad29c2a63703323cc4fda4c","source":{"kind":"arxiv","id":"1812.00971","version":2},"attestation_state":"computed","paper":{"title":"Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Ali Farhadi, Kiana Ehsani, Mitchell Wortsman, Mohammad Rastegari, Roozbeh Mottaghi","submitted_at":"2018-12-03T18:46:02Z","abstract_excerpt":"Learning is an inherently continuous phenomenon. When humans learn a new task there is no explicit distinction between training and inference. As we learn a task, we keep learning about it while performing the task. What we learn and how we learn it varies during different stages of learning. Learning how to learn and adapt is a key property that enables us to generalize effortlessly to new settings. This is in contrast with conventional settings in machine learning where a trained model is frozen during inference. In this paper we study the problem of learning to learn at both training and te"},"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":"1812.00971","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-03T18:46:02Z","cross_cats_sorted":["cs.AI","cs.LG","cs.RO"],"title_canon_sha256":"3250cf19207de4082cd402b98acf1147a5389ff2d49f14742c77f1a2f79fce91","abstract_canon_sha256":"70eb581222406c7648a5cf4056f277f74fccc149016f5783d4440010aa0dd224"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:50:06.711081Z","signature_b64":"d/+7PvHLVeWd2CPJbSpKNvk4u8rls4M1GEXmJvhSOdmA4mZsH33rJwiosFT6X5hZdGQWSTx74rEBrk9Pvfk7CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a2b0ecdc97d659f8acc73777462095b44acee26b5ad29c2a63703323cc4fda4c","last_reissued_at":"2026-05-17T23:50:06.710591Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:50:06.710591Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Ali Farhadi, Kiana Ehsani, Mitchell Wortsman, Mohammad Rastegari, Roozbeh Mottaghi","submitted_at":"2018-12-03T18:46:02Z","abstract_excerpt":"Learning is an inherently continuous phenomenon. When humans learn a new task there is no explicit distinction between training and inference. As we learn a task, we keep learning about it while performing the task. What we learn and how we learn it varies during different stages of learning. Learning how to learn and adapt is a key property that enables us to generalize effortlessly to new settings. This is in contrast with conventional settings in machine learning where a trained model is frozen during inference. In this paper we study the problem of learning to learn at both training and te"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.00971","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":""},"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":"1812.00971","created_at":"2026-05-17T23:50:06.710667+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.00971v2","created_at":"2026-05-17T23:50:06.710667+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.00971","created_at":"2026-05-17T23:50:06.710667+00:00"},{"alias_kind":"pith_short_12","alias_value":"UKYOZXEX2ZM7","created_at":"2026-05-18T12:32:56.356000+00:00"},{"alias_kind":"pith_short_16","alias_value":"UKYOZXEX2ZM7RLGH","created_at":"2026-05-18T12:32:56.356000+00:00"},{"alias_kind":"pith_short_8","alias_value":"UKYOZXEX","created_at":"2026-05-18T12:32:56.356000+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UKYOZXEX2ZM7RLGHG53UMIEVWR","json":"https://pith.science/pith/UKYOZXEX2ZM7RLGHG53UMIEVWR.json","graph_json":"https://pith.science/api/pith-number/UKYOZXEX2ZM7RLGHG53UMIEVWR/graph.json","events_json":"https://pith.science/api/pith-number/UKYOZXEX2ZM7RLGHG53UMIEVWR/events.json","paper":"https://pith.science/paper/UKYOZXEX"},"agent_actions":{"view_html":"https://pith.science/pith/UKYOZXEX2ZM7RLGHG53UMIEVWR","download_json":"https://pith.science/pith/UKYOZXEX2ZM7RLGHG53UMIEVWR.json","view_paper":"https://pith.science/paper/UKYOZXEX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.00971&json=true","fetch_graph":"https://pith.science/api/pith-number/UKYOZXEX2ZM7RLGHG53UMIEVWR/graph.json","fetch_events":"https://pith.science/api/pith-number/UKYOZXEX2ZM7RLGHG53UMIEVWR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UKYOZXEX2ZM7RLGHG53UMIEVWR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UKYOZXEX2ZM7RLGHG53UMIEVWR/action/storage_attestation","attest_author":"https://pith.science/pith/UKYOZXEX2ZM7RLGHG53UMIEVWR/action/author_attestation","sign_citation":"https://pith.science/pith/UKYOZXEX2ZM7RLGHG53UMIEVWR/action/citation_signature","submit_replication":"https://pith.science/pith/UKYOZXEX2ZM7RLGHG53UMIEVWR/action/replication_record"}},"created_at":"2026-05-17T23:50:06.710667+00:00","updated_at":"2026-05-17T23:50:06.710667+00:00"}