{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:ZK2OLJUA6TWY7XEQYPJZ4FEVUJ","short_pith_number":"pith:ZK2OLJUA","schema_version":"1.0","canonical_sha256":"cab4e5a680f4ed8fdc90c3d39e1495a27e94650274f38e484dc01a0c170e5131","source":{"kind":"arxiv","id":"1904.04676","version":1},"attestation_state":"computed","paper":{"title":"Block Neural Autoregressive Flow","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Ivan Titov, Nicola De Cao, Wilker Aziz","submitted_at":"2019-04-09T13:54:55Z","abstract_excerpt":"Normalising flows (NFS) map two density functions via a differentiable bijection whose Jacobian determinant can be computed efficiently. Recently, as an alternative to hand-crafted bijections, Huang et al. (2018) proposed neural autoregressive flow (NAF) which is a universal approximator for density functions. Their flow is a neural network (NN) whose parameters are predicted by another NN. The latter grows quadratically with the size of the former and thus an efficient technique for parametrization is needed. We propose block neural autoregressive flow (B-NAF), a much more compact universal a"},"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":"1904.04676","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-04-09T13:54:55Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"88417991c067a0ae2c2552feae094bb8cf7b2c31925405f20180bf6491894ec8","abstract_canon_sha256":"c252f8a74774ea281f23fdbf6cad53cfb433dd14c59b4275a4d401d60567b092"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:58.443065Z","signature_b64":"NfdWbfb9aeysgqAxkUvT0hddx6quJbkLGo2dRwZ0eJRyOMu9gNQ5bkp9lyBynsuNdI5yHqtXgywX3I1inAj3Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cab4e5a680f4ed8fdc90c3d39e1495a27e94650274f38e484dc01a0c170e5131","last_reissued_at":"2026-05-17T23:48:58.442679Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:58.442679Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Block Neural Autoregressive Flow","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Ivan Titov, Nicola De Cao, Wilker Aziz","submitted_at":"2019-04-09T13:54:55Z","abstract_excerpt":"Normalising flows (NFS) map two density functions via a differentiable bijection whose Jacobian determinant can be computed efficiently. Recently, as an alternative to hand-crafted bijections, Huang et al. (2018) proposed neural autoregressive flow (NAF) which is a universal approximator for density functions. Their flow is a neural network (NN) whose parameters are predicted by another NN. The latter grows quadratically with the size of the former and thus an efficient technique for parametrization is needed. We propose block neural autoregressive flow (B-NAF), a much more compact universal a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.04676","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":"1904.04676","created_at":"2026-05-17T23:48:58.442743+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.04676v1","created_at":"2026-05-17T23:48:58.442743+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.04676","created_at":"2026-05-17T23:48:58.442743+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZK2OLJUA6TWY","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZK2OLJUA6TWY7XEQ","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZK2OLJUA","created_at":"2026-05-18T12:33:33.725879+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2605.02493","citing_title":"Morphological and Star Formation Properties of Cosmic Noon Massive Quiescent Galaxies","ref_index":20,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZK2OLJUA6TWY7XEQYPJZ4FEVUJ","json":"https://pith.science/pith/ZK2OLJUA6TWY7XEQYPJZ4FEVUJ.json","graph_json":"https://pith.science/api/pith-number/ZK2OLJUA6TWY7XEQYPJZ4FEVUJ/graph.json","events_json":"https://pith.science/api/pith-number/ZK2OLJUA6TWY7XEQYPJZ4FEVUJ/events.json","paper":"https://pith.science/paper/ZK2OLJUA"},"agent_actions":{"view_html":"https://pith.science/pith/ZK2OLJUA6TWY7XEQYPJZ4FEVUJ","download_json":"https://pith.science/pith/ZK2OLJUA6TWY7XEQYPJZ4FEVUJ.json","view_paper":"https://pith.science/paper/ZK2OLJUA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.04676&json=true","fetch_graph":"https://pith.science/api/pith-number/ZK2OLJUA6TWY7XEQYPJZ4FEVUJ/graph.json","fetch_events":"https://pith.science/api/pith-number/ZK2OLJUA6TWY7XEQYPJZ4FEVUJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZK2OLJUA6TWY7XEQYPJZ4FEVUJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZK2OLJUA6TWY7XEQYPJZ4FEVUJ/action/storage_attestation","attest_author":"https://pith.science/pith/ZK2OLJUA6TWY7XEQYPJZ4FEVUJ/action/author_attestation","sign_citation":"https://pith.science/pith/ZK2OLJUA6TWY7XEQYPJZ4FEVUJ/action/citation_signature","submit_replication":"https://pith.science/pith/ZK2OLJUA6TWY7XEQYPJZ4FEVUJ/action/replication_record"}},"created_at":"2026-05-17T23:48:58.442743+00:00","updated_at":"2026-05-17T23:48:58.442743+00:00"}