{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:CU3AL7EFPTDR25LF5XJID4RHHL","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":"8d22b0ae0beeac884766b40c288abc103978366dbaa0beee32534cb062844774","cross_cats_sorted":["cs.IT","math.IT","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2024-09-03T16:36:42Z","title_canon_sha256":"83f7d0f8f32d24449d0b5517199d62da1f21b0f1407b4a3dbede08ea6e61fdf7"},"schema_version":"1.0","source":{"id":"2409.02732","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.02732","created_at":"2026-07-05T10:24:24Z"},{"alias_kind":"arxiv_version","alias_value":"2409.02732v1","created_at":"2026-07-05T10:24:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.02732","created_at":"2026-07-05T10:24:24Z"},{"alias_kind":"pith_short_12","alias_value":"CU3AL7EFPTDR","created_at":"2026-07-05T10:24:24Z"},{"alias_kind":"pith_short_16","alias_value":"CU3AL7EFPTDR25LF","created_at":"2026-07-05T10:24:24Z"},{"alias_kind":"pith_short_8","alias_value":"CU3AL7EF","created_at":"2026-07-05T10:24:24Z"}],"graph_snapshots":[{"event_id":"sha256:9c89236053c7a4f0bfb7bb1fbba884745b6da3bbabcdc0ac37974ddba5e8c28d","target":"graph","created_at":"2026-07-05T10:24:24Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2409.02732/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Mutual information (MI) is a general measure of statistical dependence with widespread application across the sciences. However, estimating MI between multi-dimensional variables is challenging because the number of samples necessary to converge to an accurate estimate scales unfavorably with dimensionality. In practice, existing techniques can reliably estimate MI in up to tens of dimensions, but fail in higher dimensions, where sufficient sample sizes are infeasible. Here, we explore the idea that underlying low-dimensional structure in high-dimensional data can be exploited to faithfully ap","authors_text":"Allon M. Klein, Gokul Gowri, Peng Yin, Xiao-Kang Lun","cross_cats":["cs.IT","math.IT","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2024-09-03T16:36:42Z","title":"Approximating mutual information of high-dimensional variables using learned representations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.02732","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":"sha256:059880267af460c1c06aad78347b2833ae533a6af47734213a245f391f931e53","target":"record","created_at":"2026-07-05T10:24:24Z","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":"8d22b0ae0beeac884766b40c288abc103978366dbaa0beee32534cb062844774","cross_cats_sorted":["cs.IT","math.IT","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2024-09-03T16:36:42Z","title_canon_sha256":"83f7d0f8f32d24449d0b5517199d62da1f21b0f1407b4a3dbede08ea6e61fdf7"},"schema_version":"1.0","source":{"id":"2409.02732","kind":"arxiv","version":1}},"canonical_sha256":"153605fc857cc71d7565edd281f2273af7d70ff46e81eb13e0633ba3a5b6c533","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"153605fc857cc71d7565edd281f2273af7d70ff46e81eb13e0633ba3a5b6c533","first_computed_at":"2026-07-05T10:24:24.531268Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:24:24.531268Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"yqoNgdkWPDl7kSU/aaWfZZPyhnrxAUJByduElnofknGk2GnBR3skr6e80tBqYiRNkwrErG4OKsuLAOefHGm5AA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:24:24.532286Z","signed_message":"canonical_sha256_bytes"},"source_id":"2409.02732","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:059880267af460c1c06aad78347b2833ae533a6af47734213a245f391f931e53","sha256:9c89236053c7a4f0bfb7bb1fbba884745b6da3bbabcdc0ac37974ddba5e8c28d"],"state_sha256":"4670a1cf5dbd0f6d65240b8e54228a74a4e0231c46e29afd7ea0c147e618fcc7"}