{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:5P6PQMMFJUX7SNYV2RMKZOITJ2","short_pith_number":"pith:5P6PQMMF","schema_version":"1.0","canonical_sha256":"ebfcf831854d2ff93715d458acb9134eb5939bf2d546309f464082e1c6ff2eb3","source":{"kind":"arxiv","id":"2311.11891","version":1},"attestation_state":"computed","paper":{"title":"AMES: A Differentiable Embedding Space Selection Framework for Latent Graph Inference","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Haitz S\\'aez de Oc\\'ariz Borde, Pietro Li\\`o, Yuan Lu","submitted_at":"2023-11-20T16:24:23Z","abstract_excerpt":"In real-world scenarios, although data entities may possess inherent relationships, the specific graph illustrating their connections might not be directly accessible. Latent graph inference addresses this issue by enabling Graph Neural Networks (GNNs) to operate on point cloud data, dynamically learning the necessary graph structure. These graphs are often derived from a latent embedding space, which can be modeled using Euclidean, hyperbolic, spherical, or product spaces. However, currently, there is no principled differentiable method for determining the optimal embedding space. In this wor"},"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":"2311.11891","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-11-20T16:24:23Z","cross_cats_sorted":["cs.SI","stat.ML"],"title_canon_sha256":"5d4e1d64c3f24ec3e15e5bfca4bd235b50811b32f6c62764f89a44b669ecd61c","abstract_canon_sha256":"3da3b476f2e746e6a34e42e6dbdafcd930c6aee900622d4bcc93a97ec6322aa1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:14:40.566922Z","signature_b64":"Q+WG+01J9mrJEMIen4T7MdtWFFcupbXInt3WORtbZpMO89mxpY3rfpoAGQ7OQgk6B/HTBo1+++8vLTztubtRCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ebfcf831854d2ff93715d458acb9134eb5939bf2d546309f464082e1c6ff2eb3","last_reissued_at":"2026-07-05T07:14:40.566433Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:14:40.566433Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AMES: A Differentiable Embedding Space Selection Framework for Latent Graph Inference","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Haitz S\\'aez de Oc\\'ariz Borde, Pietro Li\\`o, Yuan Lu","submitted_at":"2023-11-20T16:24:23Z","abstract_excerpt":"In real-world scenarios, although data entities may possess inherent relationships, the specific graph illustrating their connections might not be directly accessible. Latent graph inference addresses this issue by enabling Graph Neural Networks (GNNs) to operate on point cloud data, dynamically learning the necessary graph structure. These graphs are often derived from a latent embedding space, which can be modeled using Euclidean, hyperbolic, spherical, or product spaces. However, currently, there is no principled differentiable method for determining the optimal embedding space. In this wor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.11891","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2311.11891/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2311.11891","created_at":"2026-07-05T07:14:40.566492+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.11891v1","created_at":"2026-07-05T07:14:40.566492+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.11891","created_at":"2026-07-05T07:14:40.566492+00:00"},{"alias_kind":"pith_short_12","alias_value":"5P6PQMMFJUX7","created_at":"2026-07-05T07:14:40.566492+00:00"},{"alias_kind":"pith_short_16","alias_value":"5P6PQMMFJUX7SNYV","created_at":"2026-07-05T07:14:40.566492+00:00"},{"alias_kind":"pith_short_8","alias_value":"5P6PQMMF","created_at":"2026-07-05T07:14:40.566492+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/5P6PQMMFJUX7SNYV2RMKZOITJ2","json":"https://pith.science/pith/5P6PQMMFJUX7SNYV2RMKZOITJ2.json","graph_json":"https://pith.science/api/pith-number/5P6PQMMFJUX7SNYV2RMKZOITJ2/graph.json","events_json":"https://pith.science/api/pith-number/5P6PQMMFJUX7SNYV2RMKZOITJ2/events.json","paper":"https://pith.science/paper/5P6PQMMF"},"agent_actions":{"view_html":"https://pith.science/pith/5P6PQMMFJUX7SNYV2RMKZOITJ2","download_json":"https://pith.science/pith/5P6PQMMFJUX7SNYV2RMKZOITJ2.json","view_paper":"https://pith.science/paper/5P6PQMMF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.11891&json=true","fetch_graph":"https://pith.science/api/pith-number/5P6PQMMFJUX7SNYV2RMKZOITJ2/graph.json","fetch_events":"https://pith.science/api/pith-number/5P6PQMMFJUX7SNYV2RMKZOITJ2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5P6PQMMFJUX7SNYV2RMKZOITJ2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5P6PQMMFJUX7SNYV2RMKZOITJ2/action/storage_attestation","attest_author":"https://pith.science/pith/5P6PQMMFJUX7SNYV2RMKZOITJ2/action/author_attestation","sign_citation":"https://pith.science/pith/5P6PQMMFJUX7SNYV2RMKZOITJ2/action/citation_signature","submit_replication":"https://pith.science/pith/5P6PQMMFJUX7SNYV2RMKZOITJ2/action/replication_record"}},"created_at":"2026-07-05T07:14:40.566492+00:00","updated_at":"2026-07-05T07:14:40.566492+00:00"}