{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:TSNXPX2C3HLQK7OL7XWCCBG7Z7","short_pith_number":"pith:TSNXPX2C","schema_version":"1.0","canonical_sha256":"9c9b77df42d9d7057dcbfdec2104dfcfc6e1de783678bfc3861825563a05928d","source":{"kind":"arxiv","id":"1511.07275","version":2},"attestation_state":"computed","paper":{"title":"Learning Simple Algorithms from Examples","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Armand Joulin, Rob Fergus, Tomas Mikolov, Wojciech Zaremba","submitted_at":"2015-11-23T15:31:54Z","abstract_excerpt":"We present an approach for learning simple algorithms such as copying, multi-digit addition and single digit multiplication directly from examples. Our framework consists of a set of interfaces, accessed by a controller. Typical interfaces are 1-D tapes or 2-D grids that hold the input and output data. For the controller, we explore a range of neural network-based models which vary in their ability to abstract the underlying algorithm from training instances and generalize to test examples with many thousands of digits. The controller is trained using $Q$-learning with several enhancements and"},"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":"1511.07275","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2015-11-23T15:31:54Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"df62295edb00e0a6ef8851d35abcf632714397578beeea80a5298e5477a7499f","abstract_canon_sha256":"5180daf99b98e993acdeb9814d3d3d150cfb7595095a30255f65a60e3ca3e599"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:26:09.045526Z","signature_b64":"RQXyDAoIeU/+iwsogytizOuhXr2EyZFIoqbDFa28+Hc8Hi33c0Vr0M6NiMhApw9d/0613oQaBo5ivnS9nTz1BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9c9b77df42d9d7057dcbfdec2104dfcfc6e1de783678bfc3861825563a05928d","last_reissued_at":"2026-05-18T01:26:09.044969Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:26:09.044969Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Simple Algorithms from Examples","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Armand Joulin, Rob Fergus, Tomas Mikolov, Wojciech Zaremba","submitted_at":"2015-11-23T15:31:54Z","abstract_excerpt":"We present an approach for learning simple algorithms such as copying, multi-digit addition and single digit multiplication directly from examples. Our framework consists of a set of interfaces, accessed by a controller. Typical interfaces are 1-D tapes or 2-D grids that hold the input and output data. For the controller, we explore a range of neural network-based models which vary in their ability to abstract the underlying algorithm from training instances and generalize to test examples with many thousands of digits. The controller is trained using $Q$-learning with several enhancements and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.07275","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":"1511.07275","created_at":"2026-05-18T01:26:09.045041+00:00"},{"alias_kind":"arxiv_version","alias_value":"1511.07275v2","created_at":"2026-05-18T01:26:09.045041+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.07275","created_at":"2026-05-18T01:26:09.045041+00:00"},{"alias_kind":"pith_short_12","alias_value":"TSNXPX2C3HLQ","created_at":"2026-05-18T12:29:42.218222+00:00"},{"alias_kind":"pith_short_16","alias_value":"TSNXPX2C3HLQK7OL","created_at":"2026-05-18T12:29:42.218222+00:00"},{"alias_kind":"pith_short_8","alias_value":"TSNXPX2C","created_at":"2026-05-18T12:29:42.218222+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/TSNXPX2C3HLQK7OL7XWCCBG7Z7","json":"https://pith.science/pith/TSNXPX2C3HLQK7OL7XWCCBG7Z7.json","graph_json":"https://pith.science/api/pith-number/TSNXPX2C3HLQK7OL7XWCCBG7Z7/graph.json","events_json":"https://pith.science/api/pith-number/TSNXPX2C3HLQK7OL7XWCCBG7Z7/events.json","paper":"https://pith.science/paper/TSNXPX2C"},"agent_actions":{"view_html":"https://pith.science/pith/TSNXPX2C3HLQK7OL7XWCCBG7Z7","download_json":"https://pith.science/pith/TSNXPX2C3HLQK7OL7XWCCBG7Z7.json","view_paper":"https://pith.science/paper/TSNXPX2C","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1511.07275&json=true","fetch_graph":"https://pith.science/api/pith-number/TSNXPX2C3HLQK7OL7XWCCBG7Z7/graph.json","fetch_events":"https://pith.science/api/pith-number/TSNXPX2C3HLQK7OL7XWCCBG7Z7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TSNXPX2C3HLQK7OL7XWCCBG7Z7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TSNXPX2C3HLQK7OL7XWCCBG7Z7/action/storage_attestation","attest_author":"https://pith.science/pith/TSNXPX2C3HLQK7OL7XWCCBG7Z7/action/author_attestation","sign_citation":"https://pith.science/pith/TSNXPX2C3HLQK7OL7XWCCBG7Z7/action/citation_signature","submit_replication":"https://pith.science/pith/TSNXPX2C3HLQK7OL7XWCCBG7Z7/action/replication_record"}},"created_at":"2026-05-18T01:26:09.045041+00:00","updated_at":"2026-05-18T01:26:09.045041+00:00"}