{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:2AUNYV3MJS62UT4V4MB34WEZ2O","short_pith_number":"pith:2AUNYV3M","schema_version":"1.0","canonical_sha256":"d028dc576c4cbdaa4f95e303be5899d3a1ac023e214619bea7b788ad70592fa6","source":{"kind":"arxiv","id":"1810.11098","version":1},"attestation_state":"computed","paper":{"title":"Provable Gaussian Embedding with One Observation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Ming Yu, Mladen Kolar, Tuo Zhao, Zhaoran Wang, Zhuoran Yang","submitted_at":"2018-10-25T20:34:37Z","abstract_excerpt":"The success of machine learning methods heavily relies on having an appropriate representation for data at hand. Traditionally, machine learning approaches relied on user-defined heuristics to extract features encoding structural information about data. However, recently there has been a surge in approaches that learn how to encode the data automatically in a low dimensional space. Exponential family embedding provides a probabilistic framework for learning low-dimensional representation for various types of high-dimensional data. Though successful in practice, theoretical underpinnings for ex"},"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":"1810.11098","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-10-25T20:34:37Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"79bb63280e15a49ee57690795e02f13a48c6bdd75b77d16bd7859bdd50b75dca","abstract_canon_sha256":"f035ce724894ef83aea13ce687e30c16d751087ac2398caa1999c07a75d3bef0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:02:15.205847Z","signature_b64":"+i79CrQQhayFo5+76qpEw+qpp9F9GylBaTy1oaLAk3fyz5d+NVH2JGaInhomjQ4S+OzfUd7OTHmjJqeIFPWbAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d028dc576c4cbdaa4f95e303be5899d3a1ac023e214619bea7b788ad70592fa6","last_reissued_at":"2026-05-18T00:02:15.205364Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:02:15.205364Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Provable Gaussian Embedding with One Observation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Ming Yu, Mladen Kolar, Tuo Zhao, Zhaoran Wang, Zhuoran Yang","submitted_at":"2018-10-25T20:34:37Z","abstract_excerpt":"The success of machine learning methods heavily relies on having an appropriate representation for data at hand. Traditionally, machine learning approaches relied on user-defined heuristics to extract features encoding structural information about data. However, recently there has been a surge in approaches that learn how to encode the data automatically in a low dimensional space. Exponential family embedding provides a probabilistic framework for learning low-dimensional representation for various types of high-dimensional data. Though successful in practice, theoretical underpinnings for ex"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.11098","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":"1810.11098","created_at":"2026-05-18T00:02:15.205444+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.11098v1","created_at":"2026-05-18T00:02:15.205444+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.11098","created_at":"2026-05-18T00:02:15.205444+00:00"},{"alias_kind":"pith_short_12","alias_value":"2AUNYV3MJS62","created_at":"2026-05-18T12:31:59.375834+00:00"},{"alias_kind":"pith_short_16","alias_value":"2AUNYV3MJS62UT4V","created_at":"2026-05-18T12:31:59.375834+00:00"},{"alias_kind":"pith_short_8","alias_value":"2AUNYV3M","created_at":"2026-05-18T12:31:59.375834+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/2AUNYV3MJS62UT4V4MB34WEZ2O","json":"https://pith.science/pith/2AUNYV3MJS62UT4V4MB34WEZ2O.json","graph_json":"https://pith.science/api/pith-number/2AUNYV3MJS62UT4V4MB34WEZ2O/graph.json","events_json":"https://pith.science/api/pith-number/2AUNYV3MJS62UT4V4MB34WEZ2O/events.json","paper":"https://pith.science/paper/2AUNYV3M"},"agent_actions":{"view_html":"https://pith.science/pith/2AUNYV3MJS62UT4V4MB34WEZ2O","download_json":"https://pith.science/pith/2AUNYV3MJS62UT4V4MB34WEZ2O.json","view_paper":"https://pith.science/paper/2AUNYV3M","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.11098&json=true","fetch_graph":"https://pith.science/api/pith-number/2AUNYV3MJS62UT4V4MB34WEZ2O/graph.json","fetch_events":"https://pith.science/api/pith-number/2AUNYV3MJS62UT4V4MB34WEZ2O/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2AUNYV3MJS62UT4V4MB34WEZ2O/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2AUNYV3MJS62UT4V4MB34WEZ2O/action/storage_attestation","attest_author":"https://pith.science/pith/2AUNYV3MJS62UT4V4MB34WEZ2O/action/author_attestation","sign_citation":"https://pith.science/pith/2AUNYV3MJS62UT4V4MB34WEZ2O/action/citation_signature","submit_replication":"https://pith.science/pith/2AUNYV3MJS62UT4V4MB34WEZ2O/action/replication_record"}},"created_at":"2026-05-18T00:02:15.205444+00:00","updated_at":"2026-05-18T00:02:15.205444+00:00"}