{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:FLFF4XQLH7QPH3CKIGVN65MBNE","short_pith_number":"pith:FLFF4XQL","schema_version":"1.0","canonical_sha256":"2aca5e5e0b3fe0f3ec4a41aadf75816937389c5aa854f87589c92f9f6bc1cb82","source":{"kind":"arxiv","id":"1705.10119","version":3},"attestation_state":"computed","paper":{"title":"Kernel Implicit Variational Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.NE"],"primary_cat":"stat.ML","authors_text":"Jiaxin Shi, Jun Zhu, Shengyang Sun","submitted_at":"2017-05-29T11:11:35Z","abstract_excerpt":"Recent progress in variational inference has paid much attention to the flexibility of variational posteriors. One promising direction is to use implicit distributions, i.e., distributions without tractable densities as the variational posterior. However, existing methods on implicit posteriors still face challenges of noisy estimation and computational infeasibility when applied to models with high-dimensional latent variables. In this paper, we present a new approach named Kernel Implicit Variational Inference that addresses these challenges. As far as we know, for the first time implicit va"},"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":"1705.10119","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-05-29T11:11:35Z","cross_cats_sorted":["cs.AI","cs.LG","cs.NE"],"title_canon_sha256":"dabc642ea258c1b9100af29499ca6dcb1e4a5c263497f35e1d8091eee94f04b3","abstract_canon_sha256":"dc57e77c379d58faa2b37f121b983e3ad0cd9d8f89a1087612911185bb4dcd1b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:22:44.720292Z","signature_b64":"Bin83zA9UTk7I2pKRdzYPeI+8WGHPfomT45nuFXIdLmL3WfLqsctCstkK0Ugd6bua1LDvx5p4tmpJw2XDFmlAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2aca5e5e0b3fe0f3ec4a41aadf75816937389c5aa854f87589c92f9f6bc1cb82","last_reissued_at":"2026-05-18T00:22:44.719820Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:22:44.719820Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Kernel Implicit Variational Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.NE"],"primary_cat":"stat.ML","authors_text":"Jiaxin Shi, Jun Zhu, Shengyang Sun","submitted_at":"2017-05-29T11:11:35Z","abstract_excerpt":"Recent progress in variational inference has paid much attention to the flexibility of variational posteriors. One promising direction is to use implicit distributions, i.e., distributions without tractable densities as the variational posterior. However, existing methods on implicit posteriors still face challenges of noisy estimation and computational infeasibility when applied to models with high-dimensional latent variables. In this paper, we present a new approach named Kernel Implicit Variational Inference that addresses these challenges. As far as we know, for the first time implicit va"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.10119","kind":"arxiv","version":3},"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":"1705.10119","created_at":"2026-05-18T00:22:44.719895+00:00"},{"alias_kind":"arxiv_version","alias_value":"1705.10119v3","created_at":"2026-05-18T00:22:44.719895+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.10119","created_at":"2026-05-18T00:22:44.719895+00:00"},{"alias_kind":"pith_short_12","alias_value":"FLFF4XQLH7QP","created_at":"2026-05-18T12:31:15.632608+00:00"},{"alias_kind":"pith_short_16","alias_value":"FLFF4XQLH7QPH3CK","created_at":"2026-05-18T12:31:15.632608+00:00"},{"alias_kind":"pith_short_8","alias_value":"FLFF4XQL","created_at":"2026-05-18T12:31:15.632608+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":212,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FLFF4XQLH7QPH3CKIGVN65MBNE","json":"https://pith.science/pith/FLFF4XQLH7QPH3CKIGVN65MBNE.json","graph_json":"https://pith.science/api/pith-number/FLFF4XQLH7QPH3CKIGVN65MBNE/graph.json","events_json":"https://pith.science/api/pith-number/FLFF4XQLH7QPH3CKIGVN65MBNE/events.json","paper":"https://pith.science/paper/FLFF4XQL"},"agent_actions":{"view_html":"https://pith.science/pith/FLFF4XQLH7QPH3CKIGVN65MBNE","download_json":"https://pith.science/pith/FLFF4XQLH7QPH3CKIGVN65MBNE.json","view_paper":"https://pith.science/paper/FLFF4XQL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1705.10119&json=true","fetch_graph":"https://pith.science/api/pith-number/FLFF4XQLH7QPH3CKIGVN65MBNE/graph.json","fetch_events":"https://pith.science/api/pith-number/FLFF4XQLH7QPH3CKIGVN65MBNE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FLFF4XQLH7QPH3CKIGVN65MBNE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FLFF4XQLH7QPH3CKIGVN65MBNE/action/storage_attestation","attest_author":"https://pith.science/pith/FLFF4XQLH7QPH3CKIGVN65MBNE/action/author_attestation","sign_citation":"https://pith.science/pith/FLFF4XQLH7QPH3CKIGVN65MBNE/action/citation_signature","submit_replication":"https://pith.science/pith/FLFF4XQLH7QPH3CKIGVN65MBNE/action/replication_record"}},"created_at":"2026-05-18T00:22:44.719895+00:00","updated_at":"2026-05-18T00:22:44.719895+00:00"}