{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:LR24AQT32X67IMOXP2WBPIBHZE","short_pith_number":"pith:LR24AQT3","schema_version":"1.0","canonical_sha256":"5c75c0427bd5fdf431d77eac17a027c901138b7e6fb17d022f49c5e631d3933b","source":{"kind":"arxiv","id":"1802.06153","version":2},"attestation_state":"computed","paper":{"title":"A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-bio.PE","stat.ML"],"primary_cat":"cs.LG","authors_text":"Jeffrey Chan, Jeffrey P. Spence, Paul A. Jenkins, Sara Mathieson, Valerio Perrone, Yun S. Song","submitted_at":"2018-02-16T22:36:16Z","abstract_excerpt":"An explosion of high-throughput DNA sequencing in the past decade has led to a surge of interest in population-scale inference with whole-genome data. Recent work in population genetics has centered on designing inference methods for relatively simple model classes, and few scalable general-purpose inference techniques exist for more realistic, complex models. To achieve this, two inferential challenges need to be addressed: (1) population data are exchangeable, calling for methods that efficiently exploit the symmetries of the data, and (2) computing likelihoods is intractable as it requires "},"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":"1802.06153","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-02-16T22:36:16Z","cross_cats_sorted":["q-bio.PE","stat.ML"],"title_canon_sha256":"fd5739ab6f1d486d85ce1b7deab7edaf43e00a66de4b12c7b98ac97877781dcb","abstract_canon_sha256":"1294212102e51a84ca168cfec8986b8a5b7637174b66fb31b186104a05c1e642"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:01:30.094891Z","signature_b64":"ASP8yHmCYL7vOQVbJUNgbFCXckuhm0b+vqF+WPyNZOUN+sjBLYCGhQCCMdmecJ2GTNDoCGsfSDCaEwlBolYlBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5c75c0427bd5fdf431d77eac17a027c901138b7e6fb17d022f49c5e631d3933b","last_reissued_at":"2026-05-18T00:01:30.094466Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:01:30.094466Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-bio.PE","stat.ML"],"primary_cat":"cs.LG","authors_text":"Jeffrey Chan, Jeffrey P. Spence, Paul A. Jenkins, Sara Mathieson, Valerio Perrone, Yun S. Song","submitted_at":"2018-02-16T22:36:16Z","abstract_excerpt":"An explosion of high-throughput DNA sequencing in the past decade has led to a surge of interest in population-scale inference with whole-genome data. Recent work in population genetics has centered on designing inference methods for relatively simple model classes, and few scalable general-purpose inference techniques exist for more realistic, complex models. To achieve this, two inferential challenges need to be addressed: (1) population data are exchangeable, calling for methods that efficiently exploit the symmetries of the data, and (2) computing likelihoods is intractable as it requires "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.06153","kind":"arxiv","version":2},"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":"1802.06153","created_at":"2026-05-18T00:01:30.094538+00:00"},{"alias_kind":"arxiv_version","alias_value":"1802.06153v2","created_at":"2026-05-18T00:01:30.094538+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.06153","created_at":"2026-05-18T00:01:30.094538+00:00"},{"alias_kind":"pith_short_12","alias_value":"LR24AQT32X67","created_at":"2026-05-18T12:32:37.024351+00:00"},{"alias_kind":"pith_short_16","alias_value":"LR24AQT32X67IMOX","created_at":"2026-05-18T12:32:37.024351+00:00"},{"alias_kind":"pith_short_8","alias_value":"LR24AQT3","created_at":"2026-05-18T12:32:37.024351+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/LR24AQT32X67IMOXP2WBPIBHZE","json":"https://pith.science/pith/LR24AQT32X67IMOXP2WBPIBHZE.json","graph_json":"https://pith.science/api/pith-number/LR24AQT32X67IMOXP2WBPIBHZE/graph.json","events_json":"https://pith.science/api/pith-number/LR24AQT32X67IMOXP2WBPIBHZE/events.json","paper":"https://pith.science/paper/LR24AQT3"},"agent_actions":{"view_html":"https://pith.science/pith/LR24AQT32X67IMOXP2WBPIBHZE","download_json":"https://pith.science/pith/LR24AQT32X67IMOXP2WBPIBHZE.json","view_paper":"https://pith.science/paper/LR24AQT3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1802.06153&json=true","fetch_graph":"https://pith.science/api/pith-number/LR24AQT32X67IMOXP2WBPIBHZE/graph.json","fetch_events":"https://pith.science/api/pith-number/LR24AQT32X67IMOXP2WBPIBHZE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LR24AQT32X67IMOXP2WBPIBHZE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LR24AQT32X67IMOXP2WBPIBHZE/action/storage_attestation","attest_author":"https://pith.science/pith/LR24AQT32X67IMOXP2WBPIBHZE/action/author_attestation","sign_citation":"https://pith.science/pith/LR24AQT32X67IMOXP2WBPIBHZE/action/citation_signature","submit_replication":"https://pith.science/pith/LR24AQT32X67IMOXP2WBPIBHZE/action/replication_record"}},"created_at":"2026-05-18T00:01:30.094538+00:00","updated_at":"2026-05-18T00:01:30.094538+00:00"}