{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:AA4QBCKQZBYMYQVFT35I5TUVFJ","short_pith_number":"pith:AA4QBCKQ","schema_version":"1.0","canonical_sha256":"0039008950c870cc42a59efa8ece952a6f9d8fa5346fa64376410e5d0c1b156f","source":{"kind":"arxiv","id":"1703.06975","version":1},"attestation_state":"computed","paper":{"title":"Learning to Generate Samples from Noise through Infusion Training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Florian Bordes, Pascal Vincent, Sina Honari","submitted_at":"2017-03-20T21:29:18Z","abstract_excerpt":"In this work, we investigate a novel training procedure to learn a generative model as the transition operator of a Markov chain, such that, when applied repeatedly on an unstructured random noise sample, it will denoise it into a sample that matches the target distribution from the training set. The novel training procedure to learn this progressive denoising operation involves sampling from a slightly different chain than the model chain used for generation in the absence of a denoising target. In the training chain we infuse information from the training target example that we would like th"},"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":"1703.06975","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-03-20T21:29:18Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"6a6fddd10f658ed9c4d16ff5308185c5d18d367f3081af34081816eba8acb926","abstract_canon_sha256":"823062fb822a1bb8be90889f9c5763ec87958059b5bcb30d9fa3a146c2766823"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:48:11.727180Z","signature_b64":"PCXEdZhgsPJSohpvHv30dsrl3YI0kPi36JQ4oithfqqNs2z6qh4FP7HUZKdc+sMw7+NkBPLtmPTO5M3Hu3P1Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0039008950c870cc42a59efa8ece952a6f9d8fa5346fa64376410e5d0c1b156f","last_reissued_at":"2026-05-18T00:48:11.726679Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:48:11.726679Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning to Generate Samples from Noise through Infusion Training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Florian Bordes, Pascal Vincent, Sina Honari","submitted_at":"2017-03-20T21:29:18Z","abstract_excerpt":"In this work, we investigate a novel training procedure to learn a generative model as the transition operator of a Markov chain, such that, when applied repeatedly on an unstructured random noise sample, it will denoise it into a sample that matches the target distribution from the training set. The novel training procedure to learn this progressive denoising operation involves sampling from a slightly different chain than the model chain used for generation in the absence of a denoising target. In the training chain we infuse information from the training target example that we would like th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.06975","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":"1703.06975","created_at":"2026-05-18T00:48:11.726757+00:00"},{"alias_kind":"arxiv_version","alias_value":"1703.06975v1","created_at":"2026-05-18T00:48:11.726757+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.06975","created_at":"2026-05-18T00:48:11.726757+00:00"},{"alias_kind":"pith_short_12","alias_value":"AA4QBCKQZBYM","created_at":"2026-05-18T12:31:05.417338+00:00"},{"alias_kind":"pith_short_16","alias_value":"AA4QBCKQZBYMYQVF","created_at":"2026-05-18T12:31:05.417338+00:00"},{"alias_kind":"pith_short_8","alias_value":"AA4QBCKQ","created_at":"2026-05-18T12:31:05.417338+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"2011.13456","citing_title":"Score-Based Generative Modeling through Stochastic Differential Equations","ref_index":5,"is_internal_anchor":true},{"citing_arxiv_id":"1907.05600","citing_title":"Generative Modeling by Estimating Gradients of the Data Distribution","ref_index":5,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AA4QBCKQZBYMYQVFT35I5TUVFJ","json":"https://pith.science/pith/AA4QBCKQZBYMYQVFT35I5TUVFJ.json","graph_json":"https://pith.science/api/pith-number/AA4QBCKQZBYMYQVFT35I5TUVFJ/graph.json","events_json":"https://pith.science/api/pith-number/AA4QBCKQZBYMYQVFT35I5TUVFJ/events.json","paper":"https://pith.science/paper/AA4QBCKQ"},"agent_actions":{"view_html":"https://pith.science/pith/AA4QBCKQZBYMYQVFT35I5TUVFJ","download_json":"https://pith.science/pith/AA4QBCKQZBYMYQVFT35I5TUVFJ.json","view_paper":"https://pith.science/paper/AA4QBCKQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1703.06975&json=true","fetch_graph":"https://pith.science/api/pith-number/AA4QBCKQZBYMYQVFT35I5TUVFJ/graph.json","fetch_events":"https://pith.science/api/pith-number/AA4QBCKQZBYMYQVFT35I5TUVFJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AA4QBCKQZBYMYQVFT35I5TUVFJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AA4QBCKQZBYMYQVFT35I5TUVFJ/action/storage_attestation","attest_author":"https://pith.science/pith/AA4QBCKQZBYMYQVFT35I5TUVFJ/action/author_attestation","sign_citation":"https://pith.science/pith/AA4QBCKQZBYMYQVFT35I5TUVFJ/action/citation_signature","submit_replication":"https://pith.science/pith/AA4QBCKQZBYMYQVFT35I5TUVFJ/action/replication_record"}},"created_at":"2026-05-18T00:48:11.726757+00:00","updated_at":"2026-05-18T00:48:11.726757+00:00"}