{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:RJBUDTORR3T6J7W3E7ABJROGAR","short_pith_number":"pith:RJBUDTOR","schema_version":"1.0","canonical_sha256":"8a4341cdd18ee7e4fedb27c014c5c6047d87b6ea662675de6b14d005cba9a6f3","source":{"kind":"arxiv","id":"1605.06065","version":1},"attestation_state":"computed","paper":{"title":"One-shot Learning with Memory-Augmented Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Adam Santoro, Daan Wierstra, Matthew Botvinick, Sergey Bartunov, Timothy Lillicrap","submitted_at":"2016-05-19T17:44:51Z","abstract_excerpt":"Despite recent breakthroughs in the applications of deep neural networks, one setting that presents a persistent challenge is that of \"one-shot learning.\" Traditional gradient-based networks require a lot of data to learn, often through extensive iterative training. When new data is encountered, the models must inefficiently relearn their parameters to adequately incorporate the new information without catastrophic interference. Architectures with augmented memory capacities, such as Neural Turing Machines (NTMs), offer the ability to quickly encode and retrieve new information, and hence can "},"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":"1605.06065","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-05-19T17:44:51Z","cross_cats_sorted":[],"title_canon_sha256":"aa7816a14e608423ef4aa2a4dd80370ae5a99561c213699aa24b38bff0ff6b2a","abstract_canon_sha256":"649a4f60eaa79b68e622574c539359bc13be0bab8effdbf8512dfe72cc6e0bb2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:14:26.618218Z","signature_b64":"wrtUJgL+TSgMLR4MiUzfqljGEHoi8auceVSASCSgBaVYvnEn2bKh4hgSohC43Nb3etvOlNqhquj5OJL6WgQACg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8a4341cdd18ee7e4fedb27c014c5c6047d87b6ea662675de6b14d005cba9a6f3","last_reissued_at":"2026-05-18T01:14:26.617585Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:14:26.617585Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"One-shot Learning with Memory-Augmented Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Adam Santoro, Daan Wierstra, Matthew Botvinick, Sergey Bartunov, Timothy Lillicrap","submitted_at":"2016-05-19T17:44:51Z","abstract_excerpt":"Despite recent breakthroughs in the applications of deep neural networks, one setting that presents a persistent challenge is that of \"one-shot learning.\" Traditional gradient-based networks require a lot of data to learn, often through extensive iterative training. When new data is encountered, the models must inefficiently relearn their parameters to adequately incorporate the new information without catastrophic interference. Architectures with augmented memory capacities, such as Neural Turing Machines (NTMs), offer the ability to quickly encode and retrieve new information, and hence can "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1605.06065","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":""},"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":"1605.06065","created_at":"2026-05-18T01:14:26.617678+00:00"},{"alias_kind":"arxiv_version","alias_value":"1605.06065v1","created_at":"2026-05-18T01:14:26.617678+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1605.06065","created_at":"2026-05-18T01:14:26.617678+00:00"},{"alias_kind":"pith_short_12","alias_value":"RJBUDTORR3T6","created_at":"2026-05-18T12:30:41.710351+00:00"},{"alias_kind":"pith_short_16","alias_value":"RJBUDTORR3T6J7W3","created_at":"2026-05-18T12:30:41.710351+00:00"},{"alias_kind":"pith_short_8","alias_value":"RJBUDTOR","created_at":"2026-05-18T12:30:41.710351+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":3,"sample":[{"citing_arxiv_id":"1907.06837","citing_title":"A Self-Attentive model for Knowledge Tracing","ref_index":12,"is_internal_anchor":true},{"citing_arxiv_id":"1907.07319","citing_title":"Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning","ref_index":21,"is_internal_anchor":true},{"citing_arxiv_id":"1911.05507","citing_title":"Compressive Transformers for Long-Range Sequence Modelling","ref_index":62,"is_internal_anchor":true},{"citing_arxiv_id":"2001.04451","citing_title":"Reformer: The Efficient Transformer","ref_index":16,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RJBUDTORR3T6J7W3E7ABJROGAR","json":"https://pith.science/pith/RJBUDTORR3T6J7W3E7ABJROGAR.json","graph_json":"https://pith.science/api/pith-number/RJBUDTORR3T6J7W3E7ABJROGAR/graph.json","events_json":"https://pith.science/api/pith-number/RJBUDTORR3T6J7W3E7ABJROGAR/events.json","paper":"https://pith.science/paper/RJBUDTOR"},"agent_actions":{"view_html":"https://pith.science/pith/RJBUDTORR3T6J7W3E7ABJROGAR","download_json":"https://pith.science/pith/RJBUDTORR3T6J7W3E7ABJROGAR.json","view_paper":"https://pith.science/paper/RJBUDTOR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1605.06065&json=true","fetch_graph":"https://pith.science/api/pith-number/RJBUDTORR3T6J7W3E7ABJROGAR/graph.json","fetch_events":"https://pith.science/api/pith-number/RJBUDTORR3T6J7W3E7ABJROGAR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RJBUDTORR3T6J7W3E7ABJROGAR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RJBUDTORR3T6J7W3E7ABJROGAR/action/storage_attestation","attest_author":"https://pith.science/pith/RJBUDTORR3T6J7W3E7ABJROGAR/action/author_attestation","sign_citation":"https://pith.science/pith/RJBUDTORR3T6J7W3E7ABJROGAR/action/citation_signature","submit_replication":"https://pith.science/pith/RJBUDTORR3T6J7W3E7ABJROGAR/action/replication_record"}},"created_at":"2026-05-18T01:14:26.617678+00:00","updated_at":"2026-05-18T01:14:26.617678+00:00"}