{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:MYHIW2HUR5ALPOXA4DL7XBWYBX","short_pith_number":"pith:MYHIW2HU","schema_version":"1.0","canonical_sha256":"660e8b68f48f40b7bae0e0d7fb86d80dfc9119ca2363f51435dcb2c779625c7b","source":{"kind":"arxiv","id":"1605.07826","version":4},"attestation_state":"computed","paper":{"title":"Asymptotically exact inference in differentiable generative models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"stat.CO","authors_text":"Amos J. Storkey, Matthew M. Graham","submitted_at":"2016-05-25T11:10:36Z","abstract_excerpt":"Many generative models can be expressed as a differentiable function of random inputs drawn from some simple probability density. This framework includes both deep generative architectures such as Variational Autoencoders and a large class of procedurally defined simulator models. We present a method for performing efficient MCMC inference in such models when conditioning on observations of the model output. For some models this offers an asymptotically exact inference method where Approximate Bayesian Computation might otherwise be employed. We use the intuition that inference corresponds to "},"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":"1605.07826","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2016-05-25T11:10:36Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"0acc406471092f7eb991f2212b56d1586ef7633f78fcbb34266f7f603b5a9215","abstract_canon_sha256":"7fb0d514b34bbec6eabdedfa0631cd1d228e8db27d028749aa9730331ab8c305"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:49:37.942846Z","signature_b64":"bENbbDYzidXrPfLPvNcVmfe9abcZzDW0sm+3BFkl3uqJzi2SD6FkVTgcTNWAlvjsMAfFZk9njCvOehm2guH0CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"660e8b68f48f40b7bae0e0d7fb86d80dfc9119ca2363f51435dcb2c779625c7b","last_reissued_at":"2026-05-18T00:49:37.942033Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:49:37.942033Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Asymptotically exact inference in differentiable generative models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"stat.CO","authors_text":"Amos J. Storkey, Matthew M. Graham","submitted_at":"2016-05-25T11:10:36Z","abstract_excerpt":"Many generative models can be expressed as a differentiable function of random inputs drawn from some simple probability density. This framework includes both deep generative architectures such as Variational Autoencoders and a large class of procedurally defined simulator models. We present a method for performing efficient MCMC inference in such models when conditioning on observations of the model output. For some models this offers an asymptotically exact inference method where Approximate Bayesian Computation might otherwise be employed. We use the intuition that inference corresponds to "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1605.07826","kind":"arxiv","version":4},"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":"1605.07826","created_at":"2026-05-18T00:49:37.942178+00:00"},{"alias_kind":"arxiv_version","alias_value":"1605.07826v4","created_at":"2026-05-18T00:49:37.942178+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1605.07826","created_at":"2026-05-18T00:49:37.942178+00:00"},{"alias_kind":"pith_short_12","alias_value":"MYHIW2HUR5AL","created_at":"2026-05-18T12:30:32.724797+00:00"},{"alias_kind":"pith_short_16","alias_value":"MYHIW2HUR5ALPOXA","created_at":"2026-05-18T12:30:32.724797+00:00"},{"alias_kind":"pith_short_8","alias_value":"MYHIW2HU","created_at":"2026-05-18T12:30:32.724797+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/MYHIW2HUR5ALPOXA4DL7XBWYBX","json":"https://pith.science/pith/MYHIW2HUR5ALPOXA4DL7XBWYBX.json","graph_json":"https://pith.science/api/pith-number/MYHIW2HUR5ALPOXA4DL7XBWYBX/graph.json","events_json":"https://pith.science/api/pith-number/MYHIW2HUR5ALPOXA4DL7XBWYBX/events.json","paper":"https://pith.science/paper/MYHIW2HU"},"agent_actions":{"view_html":"https://pith.science/pith/MYHIW2HUR5ALPOXA4DL7XBWYBX","download_json":"https://pith.science/pith/MYHIW2HUR5ALPOXA4DL7XBWYBX.json","view_paper":"https://pith.science/paper/MYHIW2HU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1605.07826&json=true","fetch_graph":"https://pith.science/api/pith-number/MYHIW2HUR5ALPOXA4DL7XBWYBX/graph.json","fetch_events":"https://pith.science/api/pith-number/MYHIW2HUR5ALPOXA4DL7XBWYBX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MYHIW2HUR5ALPOXA4DL7XBWYBX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MYHIW2HUR5ALPOXA4DL7XBWYBX/action/storage_attestation","attest_author":"https://pith.science/pith/MYHIW2HUR5ALPOXA4DL7XBWYBX/action/author_attestation","sign_citation":"https://pith.science/pith/MYHIW2HUR5ALPOXA4DL7XBWYBX/action/citation_signature","submit_replication":"https://pith.science/pith/MYHIW2HUR5ALPOXA4DL7XBWYBX/action/replication_record"}},"created_at":"2026-05-18T00:49:37.942178+00:00","updated_at":"2026-05-18T00:49:37.942178+00:00"}