{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:FDWMVI2JGENNJCXVQWN2FB5THQ","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":"78bb6336435ad5b9f1663b93a3f7d6ae9c0715a6b2485670683642c2ef8cd3f0","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-06-05T13:25:24Z","title_canon_sha256":"267e9c6e62a24cd483a0c3d116ff3af44ce8b7d5b5720bfd73f15a39c061ee65"},"schema_version":"1.0","source":{"id":"1706.01307","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.01307","created_at":"2026-05-18T00:43:03Z"},{"alias_kind":"arxiv_version","alias_value":"1706.01307v1","created_at":"2026-05-18T00:43:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.01307","created_at":"2026-05-18T00:43:03Z"},{"alias_kind":"pith_short_12","alias_value":"FDWMVI2JGENN","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_16","alias_value":"FDWMVI2JGENNJCXV","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_8","alias_value":"FDWMVI2J","created_at":"2026-05-18T12:31:15Z"}],"graph_snapshots":[{"event_id":"sha256:4098daaf518f2c2ea3fe2e2827626c9e22893539a799d71c7eecd907a98c5427","target":"graph","created_at":"2026-05-18T00:43:03Z","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"},"paper":{"abstract_excerpt":"Convolutional network are the de-facto standard for analysing spatio-temporal data such as images, videos, 3D shapes, etc. Whilst some of this data is naturally dense (for instance, photos), many other data sources are inherently sparse. Examples include pen-strokes forming on a piece of paper, or (colored) 3D point clouds that were obtained using a LiDAR scanner or RGB-D camera. Standard \"dense\" implementations of convolutional networks are very inefficient when applied on such sparse data. We introduce a sparse convolutional operation tailored to processing sparse data that differs from prio","authors_text":"Benjamin Graham, Laurens van der Maaten","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-06-05T13:25:24Z","title":"Submanifold Sparse Convolutional Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.01307","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":"sha256:23443ab4210797b03eb2fd015a5e966ea7ff848c517b86f6a75b6a50f1fdc139","target":"record","created_at":"2026-05-18T00:43:03Z","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":"78bb6336435ad5b9f1663b93a3f7d6ae9c0715a6b2485670683642c2ef8cd3f0","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-06-05T13:25:24Z","title_canon_sha256":"267e9c6e62a24cd483a0c3d116ff3af44ce8b7d5b5720bfd73f15a39c061ee65"},"schema_version":"1.0","source":{"id":"1706.01307","kind":"arxiv","version":1}},"canonical_sha256":"28eccaa349311ad48af5859ba287b33c24cc945369beca82f2cee3677b8d8126","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"28eccaa349311ad48af5859ba287b33c24cc945369beca82f2cee3677b8d8126","first_computed_at":"2026-05-18T00:43:03.928650Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:43:03.928650Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"E2hpGqxs9Mnopaa3L/QgJBXOx/pEv7W+lUVWUINmm1GRCXCpHBrFzQi/O2OJvfTPqGRgyd33zzC/GgCs0hLnBA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:43:03.929160Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.01307","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:23443ab4210797b03eb2fd015a5e966ea7ff848c517b86f6a75b6a50f1fdc139","sha256:4098daaf518f2c2ea3fe2e2827626c9e22893539a799d71c7eecd907a98c5427"],"state_sha256":"fb5442aab963d750d4602e26b53ee77151174c90eb643b9e6e09b0c2f3afa3b1"}