{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:EIHR3PXZO4ZI6BUNHSHICROUIE","short_pith_number":"pith:EIHR3PXZ","schema_version":"1.0","canonical_sha256":"220f1dbef977328f068d3c8e8145d441311a9f4ecc2f3b7386b652c655a4f94a","source":{"kind":"arxiv","id":"1804.01712","version":1},"attestation_state":"computed","paper":{"title":"Variational Rejection Sampling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.NE"],"primary_cat":"stat.ML","authors_text":"Aditya Grover, Dale Schuurmans, Miguel Lazaro-Gredilla, Ramki Gummadi, Stefano Ermon","submitted_at":"2018-04-05T07:53:41Z","abstract_excerpt":"Learning latent variable models with stochastic variational inference is challenging when the approximate posterior is far from the true posterior, due to high variance in the gradient estimates. We propose a novel rejection sampling step that discards samples from the variational posterior which are assigned low likelihoods by the model. Our approach provides an arbitrarily accurate approximation of the true posterior at the expense of extra computation. Using a new gradient estimator for the resulting unnormalized proposal distribution, we achieve average improvements of 3.71 nats and 0.21 n"},"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":"1804.01712","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-04-05T07:53:41Z","cross_cats_sorted":["cs.AI","cs.LG","cs.NE"],"title_canon_sha256":"d4086d7c8f8479e464671453ef24afafbc0e51b59fafdf6d4b8bde65b6e0aecd","abstract_canon_sha256":"39ebdce4a766752ed57d74934906c0dc8dbc5c0de3a6273285a6cee91ac31493"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:19:10.224174Z","signature_b64":"fgt3NPn+Nh7GB8ovoiEDt8cV+O7d9OWnZnUq1oHN7hdpxTDNQLE/rmmOq5njHhUaEHQ/PkinEKuL29yEoGePAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"220f1dbef977328f068d3c8e8145d441311a9f4ecc2f3b7386b652c655a4f94a","last_reissued_at":"2026-05-18T00:19:10.223677Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:19:10.223677Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Variational Rejection Sampling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.NE"],"primary_cat":"stat.ML","authors_text":"Aditya Grover, Dale Schuurmans, Miguel Lazaro-Gredilla, Ramki Gummadi, Stefano Ermon","submitted_at":"2018-04-05T07:53:41Z","abstract_excerpt":"Learning latent variable models with stochastic variational inference is challenging when the approximate posterior is far from the true posterior, due to high variance in the gradient estimates. We propose a novel rejection sampling step that discards samples from the variational posterior which are assigned low likelihoods by the model. Our approach provides an arbitrarily accurate approximation of the true posterior at the expense of extra computation. Using a new gradient estimator for the resulting unnormalized proposal distribution, we achieve average improvements of 3.71 nats and 0.21 n"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.01712","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":"1804.01712","created_at":"2026-05-18T00:19:10.223763+00:00"},{"alias_kind":"arxiv_version","alias_value":"1804.01712v1","created_at":"2026-05-18T00:19:10.223763+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.01712","created_at":"2026-05-18T00:19:10.223763+00:00"},{"alias_kind":"pith_short_12","alias_value":"EIHR3PXZO4ZI","created_at":"2026-05-18T12:32:22.470017+00:00"},{"alias_kind":"pith_short_16","alias_value":"EIHR3PXZO4ZI6BUN","created_at":"2026-05-18T12:32:22.470017+00:00"},{"alias_kind":"pith_short_8","alias_value":"EIHR3PXZ","created_at":"2026-05-18T12:32:22.470017+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/EIHR3PXZO4ZI6BUNHSHICROUIE","json":"https://pith.science/pith/EIHR3PXZO4ZI6BUNHSHICROUIE.json","graph_json":"https://pith.science/api/pith-number/EIHR3PXZO4ZI6BUNHSHICROUIE/graph.json","events_json":"https://pith.science/api/pith-number/EIHR3PXZO4ZI6BUNHSHICROUIE/events.json","paper":"https://pith.science/paper/EIHR3PXZ"},"agent_actions":{"view_html":"https://pith.science/pith/EIHR3PXZO4ZI6BUNHSHICROUIE","download_json":"https://pith.science/pith/EIHR3PXZO4ZI6BUNHSHICROUIE.json","view_paper":"https://pith.science/paper/EIHR3PXZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1804.01712&json=true","fetch_graph":"https://pith.science/api/pith-number/EIHR3PXZO4ZI6BUNHSHICROUIE/graph.json","fetch_events":"https://pith.science/api/pith-number/EIHR3PXZO4ZI6BUNHSHICROUIE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EIHR3PXZO4ZI6BUNHSHICROUIE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EIHR3PXZO4ZI6BUNHSHICROUIE/action/storage_attestation","attest_author":"https://pith.science/pith/EIHR3PXZO4ZI6BUNHSHICROUIE/action/author_attestation","sign_citation":"https://pith.science/pith/EIHR3PXZO4ZI6BUNHSHICROUIE/action/citation_signature","submit_replication":"https://pith.science/pith/EIHR3PXZO4ZI6BUNHSHICROUIE/action/replication_record"}},"created_at":"2026-05-18T00:19:10.223763+00:00","updated_at":"2026-05-18T00:19:10.223763+00:00"}