{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:H4ZHEKR2RAUG55G3CEG7ZRX3LH","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"db69593ce81acb1782231596bb58c1010483d0b3361c66cc3ed1806cc338e288","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-05-19T17:45:19Z","title_canon_sha256":"64980f00fd5392cb62c3787cfb2719a870e94c86bcb3a256bb578468496b4071"},"schema_version":"1.0","source":{"id":"1705.07111","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.07111","created_at":"2026-05-18T00:44:10Z"},{"alias_kind":"arxiv_version","alias_value":"1705.07111v1","created_at":"2026-05-18T00:44:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.07111","created_at":"2026-05-18T00:44:10Z"},{"alias_kind":"pith_short_12","alias_value":"H4ZHEKR2RAUG","created_at":"2026-05-18T12:31:18Z"},{"alias_kind":"pith_short_16","alias_value":"H4ZHEKR2RAUG55G3","created_at":"2026-05-18T12:31:18Z"},{"alias_kind":"pith_short_8","alias_value":"H4ZHEKR2","created_at":"2026-05-18T12:31:18Z"}],"graph_snapshots":[{"event_id":"sha256:9fdf60fc8065355eea07022af8b30c24e7a9aaf8d981c3554b56b5598bb97b3e","target":"graph","created_at":"2026-05-18T00:44:10Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"This paper introduces the kernel mixture network, a new method for nonparametric estimation of conditional probability densities using neural networks. We model arbitrarily complex conditional densities as linear combinations of a family of kernel functions centered at a subset of training points. The weights are determined by the outer layer of a deep neural network, trained by minimizing the negative log likelihood. This generalizes the popular quantized softmax approach, which can be seen as a kernel mixture network with square and non-overlapping kernels. We test the performance of our met","authors_text":"Eric Maris, Luca Ambrogioni, Marcel A. J. van Gerven, Umut G\\\"u\\c{c}l\\\"u","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-05-19T17:45:19Z","title":"The Kernel Mixture Network: A Nonparametric Method for Conditional Density Estimation of Continuous Random Variables"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.07111","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:76f7e985ddd53edbe988ed10f1be90cf542d5ea4745b247e24336f82fa68a071","target":"record","created_at":"2026-05-18T00:44:10Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"db69593ce81acb1782231596bb58c1010483d0b3361c66cc3ed1806cc338e288","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-05-19T17:45:19Z","title_canon_sha256":"64980f00fd5392cb62c3787cfb2719a870e94c86bcb3a256bb578468496b4071"},"schema_version":"1.0","source":{"id":"1705.07111","kind":"arxiv","version":1}},"canonical_sha256":"3f32722a3a88286ef4db110dfcc6fb59c019629a458fb3339e4c738c3b9f2b72","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3f32722a3a88286ef4db110dfcc6fb59c019629a458fb3339e4c738c3b9f2b72","first_computed_at":"2026-05-18T00:44:10.599549Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:44:10.599549Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"d7Pq30XBvr37Syn45wID9ALrCpW5/cJelW9B7kKewtk39PyUIwNZUowHqnVMtVMaen5JT3jSCFM+/DgvhprxAQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:44:10.599985Z","signed_message":"canonical_sha256_bytes"},"source_id":"1705.07111","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:76f7e985ddd53edbe988ed10f1be90cf542d5ea4745b247e24336f82fa68a071","sha256:9fdf60fc8065355eea07022af8b30c24e7a9aaf8d981c3554b56b5598bb97b3e"],"state_sha256":"7af2a576d0ad4ab583a19cf95c536ebd712ac10666e063415190c9126ea5bf31"}