{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:NRRN5APJ546XJ62AWMWF7H2ZAZ","short_pith_number":"pith:NRRN5APJ","schema_version":"1.0","canonical_sha256":"6c62de81e9ef3d74fb40b32c5f9f590668eadb027247363a85f973c459e4a30c","source":{"kind":"arxiv","id":"2212.06096","version":3},"attestation_state":"computed","paper":{"title":"Implicit Convolutional Kernels for Steerable CNNs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Gabriele Cesa, Maksim Zhdanov, Nico Hoffmann","submitted_at":"2022-12-12T18:10:33Z","abstract_excerpt":"Steerable convolutional neural networks (CNNs) provide a general framework for building neural networks equivariant to translations and transformations of an origin-preserving group $G$, such as reflections and rotations. They rely on standard convolutions with $G$-steerable kernels obtained by analytically solving the group-specific equivariance constraint imposed onto the kernel space. As the solution is tailored to a particular group $G$, implementing a kernel basis does not generalize to other symmetry transformations, complicating the development of general group equivariant models. We pr"},"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":"2212.06096","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-12-12T18:10:33Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"b099f3e25e01f9f47dbafe2e1e8422aa5d7815e7608cd757a16dd28df74cc785","abstract_canon_sha256":"9b173588eee0a21d4c6cc2fc0cde5c7e1d8ff153ab293a84035a3c49fbb0ec4e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:05:40.836791Z","signature_b64":"dS5txSEwP6ZR1DMUFXnea4Zd4DlsQYH0Dlj0ksCXf335qk3ZvTJtYDtcu/38K/81kEM0JoQa8cvMpE6GHYWoAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6c62de81e9ef3d74fb40b32c5f9f590668eadb027247363a85f973c459e4a30c","last_reissued_at":"2026-07-05T07:05:40.836274Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:05:40.836274Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Implicit Convolutional Kernels for Steerable CNNs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Gabriele Cesa, Maksim Zhdanov, Nico Hoffmann","submitted_at":"2022-12-12T18:10:33Z","abstract_excerpt":"Steerable convolutional neural networks (CNNs) provide a general framework for building neural networks equivariant to translations and transformations of an origin-preserving group $G$, such as reflections and rotations. They rely on standard convolutions with $G$-steerable kernels obtained by analytically solving the group-specific equivariance constraint imposed onto the kernel space. As the solution is tailored to a particular group $G$, implementing a kernel basis does not generalize to other symmetry transformations, complicating the development of general group equivariant models. We pr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.06096","kind":"arxiv","version":3},"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/2212.06096/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":"2212.06096","created_at":"2026-07-05T07:05:40.836342+00:00"},{"alias_kind":"arxiv_version","alias_value":"2212.06096v3","created_at":"2026-07-05T07:05:40.836342+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.06096","created_at":"2026-07-05T07:05:40.836342+00:00"},{"alias_kind":"pith_short_12","alias_value":"NRRN5APJ546X","created_at":"2026-07-05T07:05:40.836342+00:00"},{"alias_kind":"pith_short_16","alias_value":"NRRN5APJ546XJ62A","created_at":"2026-07-05T07:05:40.836342+00:00"},{"alias_kind":"pith_short_8","alias_value":"NRRN5APJ","created_at":"2026-07-05T07:05:40.836342+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.12493","citing_title":"Symmetry-preserving neural networks in lattice field theories","ref_index":76,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NRRN5APJ546XJ62AWMWF7H2ZAZ","json":"https://pith.science/pith/NRRN5APJ546XJ62AWMWF7H2ZAZ.json","graph_json":"https://pith.science/api/pith-number/NRRN5APJ546XJ62AWMWF7H2ZAZ/graph.json","events_json":"https://pith.science/api/pith-number/NRRN5APJ546XJ62AWMWF7H2ZAZ/events.json","paper":"https://pith.science/paper/NRRN5APJ"},"agent_actions":{"view_html":"https://pith.science/pith/NRRN5APJ546XJ62AWMWF7H2ZAZ","download_json":"https://pith.science/pith/NRRN5APJ546XJ62AWMWF7H2ZAZ.json","view_paper":"https://pith.science/paper/NRRN5APJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2212.06096&json=true","fetch_graph":"https://pith.science/api/pith-number/NRRN5APJ546XJ62AWMWF7H2ZAZ/graph.json","fetch_events":"https://pith.science/api/pith-number/NRRN5APJ546XJ62AWMWF7H2ZAZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NRRN5APJ546XJ62AWMWF7H2ZAZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NRRN5APJ546XJ62AWMWF7H2ZAZ/action/storage_attestation","attest_author":"https://pith.science/pith/NRRN5APJ546XJ62AWMWF7H2ZAZ/action/author_attestation","sign_citation":"https://pith.science/pith/NRRN5APJ546XJ62AWMWF7H2ZAZ/action/citation_signature","submit_replication":"https://pith.science/pith/NRRN5APJ546XJ62AWMWF7H2ZAZ/action/replication_record"}},"created_at":"2026-07-05T07:05:40.836342+00:00","updated_at":"2026-07-05T07:05:40.836342+00:00"}