{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:UTQYMZLBWZG4R7EZTHIB5JCL6X","short_pith_number":"pith:UTQYMZLB","schema_version":"1.0","canonical_sha256":"a4e1866561b64dc8fc9999d01ea44bf5dadb3343e19f870ba80b86d1099dc682","source":{"kind":"arxiv","id":"2310.10013","version":1},"attestation_state":"computed","paper":{"title":"Riemannian Residual Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Aaron Lou, Anna Asch, Christopher De Sa, Eric Ming Chen, Isay Katsman, Ser-Nam Lim, Sidhanth Holalkere","submitted_at":"2023-10-16T02:12:32Z","abstract_excerpt":"Recent methods in geometric deep learning have introduced various neural networks to operate over data that lie on Riemannian manifolds. Such networks are often necessary to learn well over graphs with a hierarchical structure or to learn over manifold-valued data encountered in the natural sciences. These networks are often inspired by and directly generalize standard Euclidean neural networks. However, extending Euclidean networks is difficult and has only been done for a select few manifolds. In this work, we examine the residual neural network (ResNet) and show how to extend this construct"},"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":"2310.10013","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2023-10-16T02:12:32Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c88a91bda8704943f5c4afd6453cf58e064621adbd53eda125803e58d48b2125","abstract_canon_sha256":"8b98a8d66513f27c9aa0c2452842f3e80118a200f1d7fb422b7c7d76d7c9536c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:01:13.883295Z","signature_b64":"XWDgo9KnkwQnulDQd4JSwIG52E54vJECvbrjA4DcJFnx775ihmdSgK/FAgyfba/77eaxumbMm4v7zF5cifRuBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a4e1866561b64dc8fc9999d01ea44bf5dadb3343e19f870ba80b86d1099dc682","last_reissued_at":"2026-07-05T07:01:13.882885Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:01:13.882885Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Riemannian Residual Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Aaron Lou, Anna Asch, Christopher De Sa, Eric Ming Chen, Isay Katsman, Ser-Nam Lim, Sidhanth Holalkere","submitted_at":"2023-10-16T02:12:32Z","abstract_excerpt":"Recent methods in geometric deep learning have introduced various neural networks to operate over data that lie on Riemannian manifolds. Such networks are often necessary to learn well over graphs with a hierarchical structure or to learn over manifold-valued data encountered in the natural sciences. These networks are often inspired by and directly generalize standard Euclidean neural networks. However, extending Euclidean networks is difficult and has only been done for a select few manifolds. In this work, we examine the residual neural network (ResNet) and show how to extend this construct"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.10013","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/2310.10013/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":"2310.10013","created_at":"2026-07-05T07:01:13.882944+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.10013v1","created_at":"2026-07-05T07:01:13.882944+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.10013","created_at":"2026-07-05T07:01:13.882944+00:00"},{"alias_kind":"pith_short_12","alias_value":"UTQYMZLBWZG4","created_at":"2026-07-05T07:01:13.882944+00:00"},{"alias_kind":"pith_short_16","alias_value":"UTQYMZLBWZG4R7EZ","created_at":"2026-07-05T07:01:13.882944+00:00"},{"alias_kind":"pith_short_8","alias_value":"UTQYMZLB","created_at":"2026-07-05T07:01:13.882944+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/UTQYMZLBWZG4R7EZTHIB5JCL6X","json":"https://pith.science/pith/UTQYMZLBWZG4R7EZTHIB5JCL6X.json","graph_json":"https://pith.science/api/pith-number/UTQYMZLBWZG4R7EZTHIB5JCL6X/graph.json","events_json":"https://pith.science/api/pith-number/UTQYMZLBWZG4R7EZTHIB5JCL6X/events.json","paper":"https://pith.science/paper/UTQYMZLB"},"agent_actions":{"view_html":"https://pith.science/pith/UTQYMZLBWZG4R7EZTHIB5JCL6X","download_json":"https://pith.science/pith/UTQYMZLBWZG4R7EZTHIB5JCL6X.json","view_paper":"https://pith.science/paper/UTQYMZLB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.10013&json=true","fetch_graph":"https://pith.science/api/pith-number/UTQYMZLBWZG4R7EZTHIB5JCL6X/graph.json","fetch_events":"https://pith.science/api/pith-number/UTQYMZLBWZG4R7EZTHIB5JCL6X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UTQYMZLBWZG4R7EZTHIB5JCL6X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UTQYMZLBWZG4R7EZTHIB5JCL6X/action/storage_attestation","attest_author":"https://pith.science/pith/UTQYMZLBWZG4R7EZTHIB5JCL6X/action/author_attestation","sign_citation":"https://pith.science/pith/UTQYMZLBWZG4R7EZTHIB5JCL6X/action/citation_signature","submit_replication":"https://pith.science/pith/UTQYMZLBWZG4R7EZTHIB5JCL6X/action/replication_record"}},"created_at":"2026-07-05T07:01:13.882944+00:00","updated_at":"2026-07-05T07:01:13.882944+00:00"}