{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:ODZMXHSGEC63CGROBRRFBYBNFM","short_pith_number":"pith:ODZMXHSG","schema_version":"1.0","canonical_sha256":"70f2cb9e4620bdb11a2e0c6250e02d2b0ba9dc7bd880046248955cc121ccb34d","source":{"kind":"arxiv","id":"2210.17425","version":1},"attestation_state":"computed","paper":{"title":"Algebraic Convolutional Filters on Lie Group Algebras","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Alejandro Parada-Mayorga, Alejandro Ribeiro, Harshat Kumar","submitted_at":"2022-10-31T15:53:03Z","abstract_excerpt":"Group convolutional neural networks are a useful tool for utilizing symmetries known to be in a signal; however, they require that the signal is defined on the group itself. Existing approaches either work directly with group signals, or they impose a lifting step with heuristics to compute the convolution which can be computationally costly. Taking an algebraic signal processing perspective, we propose a novel convolutional filter from the Lie group algebra directly, thereby removing the need to lift altogether. Furthermore, we establish stability of the filter by drawing connections to multi"},"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":"2210.17425","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2022-10-31T15:53:03Z","cross_cats_sorted":[],"title_canon_sha256":"553e546626caf6028156c7b69bcbbd7b91edd4cdd77e8f368434e6aec7b7895d","abstract_canon_sha256":"ef899ff62b7fda4bb95772bbc409c7487800b0cada3b45c7716dcce30408e76f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:12:05.350134Z","signature_b64":"wbJwsR76N4RdOXg19h/XaGQpjsdQg+aWpEDVq5EkpQc+eJWiLP95zDX4UbsL+9Pe1fIxjErUwzq2q9bceoznDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"70f2cb9e4620bdb11a2e0c6250e02d2b0ba9dc7bd880046248955cc121ccb34d","last_reissued_at":"2026-07-05T05:12:05.349692Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:12:05.349692Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Algebraic Convolutional Filters on Lie Group Algebras","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Alejandro Parada-Mayorga, Alejandro Ribeiro, Harshat Kumar","submitted_at":"2022-10-31T15:53:03Z","abstract_excerpt":"Group convolutional neural networks are a useful tool for utilizing symmetries known to be in a signal; however, they require that the signal is defined on the group itself. Existing approaches either work directly with group signals, or they impose a lifting step with heuristics to compute the convolution which can be computationally costly. Taking an algebraic signal processing perspective, we propose a novel convolutional filter from the Lie group algebra directly, thereby removing the need to lift altogether. Furthermore, we establish stability of the filter by drawing connections to multi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.17425","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/2210.17425/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":"2210.17425","created_at":"2026-07-05T05:12:05.349754+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.17425v1","created_at":"2026-07-05T05:12:05.349754+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.17425","created_at":"2026-07-05T05:12:05.349754+00:00"},{"alias_kind":"pith_short_12","alias_value":"ODZMXHSGEC63","created_at":"2026-07-05T05:12:05.349754+00:00"},{"alias_kind":"pith_short_16","alias_value":"ODZMXHSGEC63CGRO","created_at":"2026-07-05T05:12:05.349754+00:00"},{"alias_kind":"pith_short_8","alias_value":"ODZMXHSG","created_at":"2026-07-05T05:12:05.349754+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/ODZMXHSGEC63CGROBRRFBYBNFM","json":"https://pith.science/pith/ODZMXHSGEC63CGROBRRFBYBNFM.json","graph_json":"https://pith.science/api/pith-number/ODZMXHSGEC63CGROBRRFBYBNFM/graph.json","events_json":"https://pith.science/api/pith-number/ODZMXHSGEC63CGROBRRFBYBNFM/events.json","paper":"https://pith.science/paper/ODZMXHSG"},"agent_actions":{"view_html":"https://pith.science/pith/ODZMXHSGEC63CGROBRRFBYBNFM","download_json":"https://pith.science/pith/ODZMXHSGEC63CGROBRRFBYBNFM.json","view_paper":"https://pith.science/paper/ODZMXHSG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.17425&json=true","fetch_graph":"https://pith.science/api/pith-number/ODZMXHSGEC63CGROBRRFBYBNFM/graph.json","fetch_events":"https://pith.science/api/pith-number/ODZMXHSGEC63CGROBRRFBYBNFM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ODZMXHSGEC63CGROBRRFBYBNFM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ODZMXHSGEC63CGROBRRFBYBNFM/action/storage_attestation","attest_author":"https://pith.science/pith/ODZMXHSGEC63CGROBRRFBYBNFM/action/author_attestation","sign_citation":"https://pith.science/pith/ODZMXHSGEC63CGROBRRFBYBNFM/action/citation_signature","submit_replication":"https://pith.science/pith/ODZMXHSGEC63CGROBRRFBYBNFM/action/replication_record"}},"created_at":"2026-07-05T05:12:05.349754+00:00","updated_at":"2026-07-05T05:12:05.349754+00:00"}