{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:QSJP7WBVZACTE6AY7RLVC2DFDY","short_pith_number":"pith:QSJP7WBV","schema_version":"1.0","canonical_sha256":"8492ffd835c805327818fc575168651e2ca18ac761f8206c612ec1b72d21e317","source":{"kind":"arxiv","id":"2312.14977","version":1},"attestation_state":"computed","paper":{"title":"Diffusion Models for Generative Artificial Intelligence: An Introduction for Applied Mathematicians","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Catherine F. Higham, Desmond J. Higham, Peter Grindrod","submitted_at":"2023-12-21T20:20:52Z","abstract_excerpt":"Generative artificial intelligence (AI) refers to algorithms that create synthetic but realistic output. Diffusion models currently offer state of the art performance in generative AI for images. They also form a key component in more general tools, including text-to-image generators and large language models. Diffusion models work by adding noise to the available training data and then learning how to reverse the process. The reverse operation may then be applied to new random data in order to produce new outputs. We provide a brief introduction to diffusion models for applied mathematicians "},"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":"2312.14977","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-12-21T20:20:52Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"abd3160ce5ce239d3efbdefe679b7c7d036d4c1452da1e41a1b1d9a0fb28b13c","abstract_canon_sha256":"2ef4f54ac2e0ae9b8bd48440fa6dc664d059d0af057312132712ab1f52616af9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:27:29.109846Z","signature_b64":"rvp5qOTGT2Yk3TBYv0rlRfetR3ceXgxyZeCiVc2oFX3PryTi/CBALlMGEiYAgPjyDHOqQ9jcKuB7rWuNHStCCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8492ffd835c805327818fc575168651e2ca18ac761f8206c612ec1b72d21e317","last_reissued_at":"2026-07-05T07:27:29.109369Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:27:29.109369Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Diffusion Models for Generative Artificial Intelligence: An Introduction for Applied Mathematicians","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Catherine F. Higham, Desmond J. Higham, Peter Grindrod","submitted_at":"2023-12-21T20:20:52Z","abstract_excerpt":"Generative artificial intelligence (AI) refers to algorithms that create synthetic but realistic output. Diffusion models currently offer state of the art performance in generative AI for images. They also form a key component in more general tools, including text-to-image generators and large language models. Diffusion models work by adding noise to the available training data and then learning how to reverse the process. The reverse operation may then be applied to new random data in order to produce new outputs. We provide a brief introduction to diffusion models for applied mathematicians "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.14977","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/2312.14977/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":"2312.14977","created_at":"2026-07-05T07:27:29.109427+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.14977v1","created_at":"2026-07-05T07:27:29.109427+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.14977","created_at":"2026-07-05T07:27:29.109427+00:00"},{"alias_kind":"pith_short_12","alias_value":"QSJP7WBVZACT","created_at":"2026-07-05T07:27:29.109427+00:00"},{"alias_kind":"pith_short_16","alias_value":"QSJP7WBVZACTE6AY","created_at":"2026-07-05T07:27:29.109427+00:00"},{"alias_kind":"pith_short_8","alias_value":"QSJP7WBV","created_at":"2026-07-05T07:27:29.109427+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.08226","citing_title":"SPECTRA-Net: Scalable Pipeline for Explainable Cross-domain Tensor Representations for AI-generated Images Detection","ref_index":14,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QSJP7WBVZACTE6AY7RLVC2DFDY","json":"https://pith.science/pith/QSJP7WBVZACTE6AY7RLVC2DFDY.json","graph_json":"https://pith.science/api/pith-number/QSJP7WBVZACTE6AY7RLVC2DFDY/graph.json","events_json":"https://pith.science/api/pith-number/QSJP7WBVZACTE6AY7RLVC2DFDY/events.json","paper":"https://pith.science/paper/QSJP7WBV"},"agent_actions":{"view_html":"https://pith.science/pith/QSJP7WBVZACTE6AY7RLVC2DFDY","download_json":"https://pith.science/pith/QSJP7WBVZACTE6AY7RLVC2DFDY.json","view_paper":"https://pith.science/paper/QSJP7WBV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.14977&json=true","fetch_graph":"https://pith.science/api/pith-number/QSJP7WBVZACTE6AY7RLVC2DFDY/graph.json","fetch_events":"https://pith.science/api/pith-number/QSJP7WBVZACTE6AY7RLVC2DFDY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QSJP7WBVZACTE6AY7RLVC2DFDY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QSJP7WBVZACTE6AY7RLVC2DFDY/action/storage_attestation","attest_author":"https://pith.science/pith/QSJP7WBVZACTE6AY7RLVC2DFDY/action/author_attestation","sign_citation":"https://pith.science/pith/QSJP7WBVZACTE6AY7RLVC2DFDY/action/citation_signature","submit_replication":"https://pith.science/pith/QSJP7WBVZACTE6AY7RLVC2DFDY/action/replication_record"}},"created_at":"2026-07-05T07:27:29.109427+00:00","updated_at":"2026-07-05T07:27:29.109427+00:00"}