{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:CSBSPOMGAYA3BNID4NBH4XT5NH","short_pith_number":"pith:CSBSPOMG","schema_version":"1.0","canonical_sha256":"148327b9860601b0b503e3427e5e7d69f3935757dc2fae9604cef3b297652412","source":{"kind":"arxiv","id":"1705.07107","version":5},"attestation_state":"computed","paper":{"title":"Gradient Estimators for Implicit Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Richard E. Turner, Yingzhen Li","submitted_at":"2017-05-19T17:35:04Z","abstract_excerpt":"Implicit models, which allow for the generation of samples but not for point-wise evaluation of probabilities, are omnipresent in real-world problems tackled by machine learning and a hot topic of current research. Some examples include data simulators that are widely used in engineering and scientific research, generative adversarial networks (GANs) for image synthesis, and hot-off-the-press approximate inference techniques relying on implicit distributions. The majority of existing approaches to learning implicit models rely on approximating the intractable distribution or optimisation objec"},"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":"1705.07107","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-05-19T17:35:04Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"853ae4a3058fe2038923364389d2f5cfed7bf71bb8283979db4372e2ca8b30d5","abstract_canon_sha256":"05d08825b4ac1da76cd689a2c43afe394dedd5279326cb05867de2193b2704fb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:17:27.254307Z","signature_b64":"hzMklvCEvVWDLvATz5GNv1OkfxMvmaiQ0TGB7PxPR8OwghlwC4mSKgxkLySTMM+reR2t1S7PpMXkSO0abkgMBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"148327b9860601b0b503e3427e5e7d69f3935757dc2fae9604cef3b297652412","last_reissued_at":"2026-05-18T00:17:27.253577Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:17:27.253577Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Gradient Estimators for Implicit Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Richard E. Turner, Yingzhen Li","submitted_at":"2017-05-19T17:35:04Z","abstract_excerpt":"Implicit models, which allow for the generation of samples but not for point-wise evaluation of probabilities, are omnipresent in real-world problems tackled by machine learning and a hot topic of current research. Some examples include data simulators that are widely used in engineering and scientific research, generative adversarial networks (GANs) for image synthesis, and hot-off-the-press approximate inference techniques relying on implicit distributions. The majority of existing approaches to learning implicit models rely on approximating the intractable distribution or optimisation objec"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.07107","kind":"arxiv","version":5},"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":"1705.07107","created_at":"2026-05-18T00:17:27.253712+00:00"},{"alias_kind":"arxiv_version","alias_value":"1705.07107v5","created_at":"2026-05-18T00:17:27.253712+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.07107","created_at":"2026-05-18T00:17:27.253712+00:00"},{"alias_kind":"pith_short_12","alias_value":"CSBSPOMGAYA3","created_at":"2026-05-18T12:31:10.602751+00:00"},{"alias_kind":"pith_short_16","alias_value":"CSBSPOMGAYA3BNID","created_at":"2026-05-18T12:31:10.602751+00:00"},{"alias_kind":"pith_short_8","alias_value":"CSBSPOMG","created_at":"2026-05-18T12:31:10.602751+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2605.16570","citing_title":"A Cubing Strategy for Identifying Stable Hyperparameter Regions for Uncertainty Quantification in Spatial Deep Learning","ref_index":210,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CSBSPOMGAYA3BNID4NBH4XT5NH","json":"https://pith.science/pith/CSBSPOMGAYA3BNID4NBH4XT5NH.json","graph_json":"https://pith.science/api/pith-number/CSBSPOMGAYA3BNID4NBH4XT5NH/graph.json","events_json":"https://pith.science/api/pith-number/CSBSPOMGAYA3BNID4NBH4XT5NH/events.json","paper":"https://pith.science/paper/CSBSPOMG"},"agent_actions":{"view_html":"https://pith.science/pith/CSBSPOMGAYA3BNID4NBH4XT5NH","download_json":"https://pith.science/pith/CSBSPOMGAYA3BNID4NBH4XT5NH.json","view_paper":"https://pith.science/paper/CSBSPOMG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1705.07107&json=true","fetch_graph":"https://pith.science/api/pith-number/CSBSPOMGAYA3BNID4NBH4XT5NH/graph.json","fetch_events":"https://pith.science/api/pith-number/CSBSPOMGAYA3BNID4NBH4XT5NH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CSBSPOMGAYA3BNID4NBH4XT5NH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CSBSPOMGAYA3BNID4NBH4XT5NH/action/storage_attestation","attest_author":"https://pith.science/pith/CSBSPOMGAYA3BNID4NBH4XT5NH/action/author_attestation","sign_citation":"https://pith.science/pith/CSBSPOMGAYA3BNID4NBH4XT5NH/action/citation_signature","submit_replication":"https://pith.science/pith/CSBSPOMGAYA3BNID4NBH4XT5NH/action/replication_record"}},"created_at":"2026-05-18T00:17:27.253712+00:00","updated_at":"2026-05-18T00:17:27.253712+00:00"}