{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:N4IXR4ZYS2GOYZJFMQSRAD43VJ","short_pith_number":"pith:N4IXR4ZY","schema_version":"1.0","canonical_sha256":"6f1178f338968cec65256425100f9baa403e8847dfb8e43c349654fb9882c95c","source":{"kind":"arxiv","id":"1308.4214","version":1},"attestation_state":"computed","paper":{"title":"Pylearn2: a machine learning research library","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.MS"],"primary_cat":"stat.ML","authors_text":"David Warde-Farley, Fr\\'ed\\'eric Bastien, Ian J. Goodfellow, James Bergstra, Mehdi Mirza, Pascal Lamblin, Razvan Pascanu, Vincent Dumoulin, Yoshua Bengio","submitted_at":"2013-08-20T02:50:43Z","abstract_excerpt":"Pylearn2 is a machine learning research library. This does not just mean that it is a collection of machine learning algorithms that share a common API; it means that it has been designed for flexibility and extensibility in order to facilitate research projects that involve new or unusual use cases. In this paper we give a brief history of the library, an overview of its basic philosophy, a summary of the library's architecture, and a description of how the Pylearn2 community functions socially."},"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":"1308.4214","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2013-08-20T02:50:43Z","cross_cats_sorted":["cs.LG","cs.MS"],"title_canon_sha256":"c0ee16ba18d71c1ba9b1665b160c48150e3ebacfa2cfdad465e8c11e2f9e3c48","abstract_canon_sha256":"6358f081e6d172fd4c6d6b34d5e2fad7ed9d7a5a4861981bde2f39abc7ef81b3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:15:20.299286Z","signature_b64":"tsffoyzTDgoHEAA/AxGedF42lQf5mnVfqMsoqEPCm8s21Ar9UXp0XjI/T0uEdJK/cHHbXPjk6cI34yqrf1piAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6f1178f338968cec65256425100f9baa403e8847dfb8e43c349654fb9882c95c","last_reissued_at":"2026-05-18T03:15:20.298455Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:15:20.298455Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Pylearn2: a machine learning research library","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.MS"],"primary_cat":"stat.ML","authors_text":"David Warde-Farley, Fr\\'ed\\'eric Bastien, Ian J. Goodfellow, James Bergstra, Mehdi Mirza, Pascal Lamblin, Razvan Pascanu, Vincent Dumoulin, Yoshua Bengio","submitted_at":"2013-08-20T02:50:43Z","abstract_excerpt":"Pylearn2 is a machine learning research library. This does not just mean that it is a collection of machine learning algorithms that share a common API; it means that it has been designed for flexibility and extensibility in order to facilitate research projects that involve new or unusual use cases. In this paper we give a brief history of the library, an overview of its basic philosophy, a summary of the library's architecture, and a description of how the Pylearn2 community functions socially."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1308.4214","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":"1308.4214","created_at":"2026-05-18T03:15:20.298594+00:00"},{"alias_kind":"arxiv_version","alias_value":"1308.4214v1","created_at":"2026-05-18T03:15:20.298594+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1308.4214","created_at":"2026-05-18T03:15:20.298594+00:00"},{"alias_kind":"pith_short_12","alias_value":"N4IXR4ZYS2GO","created_at":"2026-05-18T12:27:52.871228+00:00"},{"alias_kind":"pith_short_16","alias_value":"N4IXR4ZYS2GOYZJF","created_at":"2026-05-18T12:27:52.871228+00:00"},{"alias_kind":"pith_short_8","alias_value":"N4IXR4ZY","created_at":"2026-05-18T12:27:52.871228+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1410.8516","citing_title":"NICE: Non-linear Independent Components Estimation","ref_index":12,"is_internal_anchor":true},{"citing_arxiv_id":"1406.2661","citing_title":"Generative Adversarial Networks","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"1411.1784","citing_title":"Conditional Generative Adversarial Nets","ref_index":7,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/N4IXR4ZYS2GOYZJFMQSRAD43VJ","json":"https://pith.science/pith/N4IXR4ZYS2GOYZJFMQSRAD43VJ.json","graph_json":"https://pith.science/api/pith-number/N4IXR4ZYS2GOYZJFMQSRAD43VJ/graph.json","events_json":"https://pith.science/api/pith-number/N4IXR4ZYS2GOYZJFMQSRAD43VJ/events.json","paper":"https://pith.science/paper/N4IXR4ZY"},"agent_actions":{"view_html":"https://pith.science/pith/N4IXR4ZYS2GOYZJFMQSRAD43VJ","download_json":"https://pith.science/pith/N4IXR4ZYS2GOYZJFMQSRAD43VJ.json","view_paper":"https://pith.science/paper/N4IXR4ZY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1308.4214&json=true","fetch_graph":"https://pith.science/api/pith-number/N4IXR4ZYS2GOYZJFMQSRAD43VJ/graph.json","fetch_events":"https://pith.science/api/pith-number/N4IXR4ZYS2GOYZJFMQSRAD43VJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/N4IXR4ZYS2GOYZJFMQSRAD43VJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/N4IXR4ZYS2GOYZJFMQSRAD43VJ/action/storage_attestation","attest_author":"https://pith.science/pith/N4IXR4ZYS2GOYZJFMQSRAD43VJ/action/author_attestation","sign_citation":"https://pith.science/pith/N4IXR4ZYS2GOYZJFMQSRAD43VJ/action/citation_signature","submit_replication":"https://pith.science/pith/N4IXR4ZYS2GOYZJFMQSRAD43VJ/action/replication_record"}},"created_at":"2026-05-18T03:15:20.298594+00:00","updated_at":"2026-05-18T03:15:20.298594+00:00"}