{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:KC7LLVZHYWXSNEIGHTTEM6DD3H","short_pith_number":"pith:KC7LLVZH","schema_version":"1.0","canonical_sha256":"50beb5d727c5af2691063ce6467863d9c1bbd64fc8e1fcb5bdef7a1f7ef78f47","source":{"kind":"arxiv","id":"2202.04238","version":1},"attestation_state":"computed","paper":{"title":"Parametric t-Stochastic Neighbor Embedding With Quantum Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"quant-ph","authors_text":"Keisuke Fujii, Kosuke Mitarai, Yoshiaki Kawase","submitted_at":"2022-02-09T02:49:54Z","abstract_excerpt":"t-Stochastic Neighbor Embedding (t-SNE) is a non-parametric data visualization method in classical machine learning. It maps the data from the high-dimensional space into a low-dimensional space, especially a two-dimensional plane, while maintaining the relationship, or similarities, between the surrounding points. In t-SNE, the initial position of the low-dimensional data is randomly determined, and the visualization is achieved by moving the low-dimensional data to minimize a cost function. Its variant called parametric t-SNE uses neural networks for this mapping. In this paper, we propose t"},"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":"2202.04238","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2022-02-09T02:49:54Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"38f14cb7e7b16eab76ef6f58e730479ca9c866eadea0298900a5bcd762612b76","abstract_canon_sha256":"169db33447911c818acde70601d7a1cc8cccdb6c6770349c3878be69523d0ac4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:55:34.971079Z","signature_b64":"YW1Hq/nIpgN3eq4lqTdd+8Khy26dgHpwmkpO6WG6JArTeqrq6dGJFE1kpemMFq5GcTwQOu0O/Y5LP1Amr90tBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"50beb5d727c5af2691063ce6467863d9c1bbd64fc8e1fcb5bdef7a1f7ef78f47","last_reissued_at":"2026-07-05T03:55:34.970572Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:55:34.970572Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Parametric t-Stochastic Neighbor Embedding With Quantum Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"quant-ph","authors_text":"Keisuke Fujii, Kosuke Mitarai, Yoshiaki Kawase","submitted_at":"2022-02-09T02:49:54Z","abstract_excerpt":"t-Stochastic Neighbor Embedding (t-SNE) is a non-parametric data visualization method in classical machine learning. It maps the data from the high-dimensional space into a low-dimensional space, especially a two-dimensional plane, while maintaining the relationship, or similarities, between the surrounding points. In t-SNE, the initial position of the low-dimensional data is randomly determined, and the visualization is achieved by moving the low-dimensional data to minimize a cost function. Its variant called parametric t-SNE uses neural networks for this mapping. In this paper, we propose t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.04238","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2202.04238/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2202.04238","created_at":"2026-07-05T03:55:34.970643+00:00"},{"alias_kind":"arxiv_version","alias_value":"2202.04238v1","created_at":"2026-07-05T03:55:34.970643+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.04238","created_at":"2026-07-05T03:55:34.970643+00:00"},{"alias_kind":"pith_short_12","alias_value":"KC7LLVZHYWXS","created_at":"2026-07-05T03:55:34.970643+00:00"},{"alias_kind":"pith_short_16","alias_value":"KC7LLVZHYWXSNEIG","created_at":"2026-07-05T03:55:34.970643+00:00"},{"alias_kind":"pith_short_8","alias_value":"KC7LLVZH","created_at":"2026-07-05T03:55:34.970643+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.11305","citing_title":"Generalizable Spectral Embedding with an Application to UMAP","ref_index":31,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KC7LLVZHYWXSNEIGHTTEM6DD3H","json":"https://pith.science/pith/KC7LLVZHYWXSNEIGHTTEM6DD3H.json","graph_json":"https://pith.science/api/pith-number/KC7LLVZHYWXSNEIGHTTEM6DD3H/graph.json","events_json":"https://pith.science/api/pith-number/KC7LLVZHYWXSNEIGHTTEM6DD3H/events.json","paper":"https://pith.science/paper/KC7LLVZH"},"agent_actions":{"view_html":"https://pith.science/pith/KC7LLVZHYWXSNEIGHTTEM6DD3H","download_json":"https://pith.science/pith/KC7LLVZHYWXSNEIGHTTEM6DD3H.json","view_paper":"https://pith.science/paper/KC7LLVZH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2202.04238&json=true","fetch_graph":"https://pith.science/api/pith-number/KC7LLVZHYWXSNEIGHTTEM6DD3H/graph.json","fetch_events":"https://pith.science/api/pith-number/KC7LLVZHYWXSNEIGHTTEM6DD3H/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KC7LLVZHYWXSNEIGHTTEM6DD3H/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KC7LLVZHYWXSNEIGHTTEM6DD3H/action/storage_attestation","attest_author":"https://pith.science/pith/KC7LLVZHYWXSNEIGHTTEM6DD3H/action/author_attestation","sign_citation":"https://pith.science/pith/KC7LLVZHYWXSNEIGHTTEM6DD3H/action/citation_signature","submit_replication":"https://pith.science/pith/KC7LLVZHYWXSNEIGHTTEM6DD3H/action/replication_record"}},"created_at":"2026-07-05T03:55:34.970643+00:00","updated_at":"2026-07-05T03:55:34.970643+00:00"}