{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:7KYHUWFJLJK6WIBUMCTN42EPFF","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":"aa124f450dad99d31d0b191ba42e89345487c8364c864984669bb5c9f389b981","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-11-25T10:05:03Z","title_canon_sha256":"9ef13bceb25e1a812cded5cd16fe16c5c83c538c55c626ee1e462c3e68abd4bf"},"schema_version":"1.0","source":{"id":"1611.08402","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.08402","created_at":"2026-05-18T00:55:35Z"},{"alias_kind":"arxiv_version","alias_value":"1611.08402v3","created_at":"2026-05-18T00:55:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.08402","created_at":"2026-05-18T00:55:35Z"},{"alias_kind":"pith_short_12","alias_value":"7KYHUWFJLJK6","created_at":"2026-05-18T12:30:04Z"},{"alias_kind":"pith_short_16","alias_value":"7KYHUWFJLJK6WIBU","created_at":"2026-05-18T12:30:04Z"},{"alias_kind":"pith_short_8","alias_value":"7KYHUWFJ","created_at":"2026-05-18T12:30:04Z"}],"graph_snapshots":[{"event_id":"sha256:ddb276142c3559f29cf82cc27441c3dcbb06580ac6a2430df97baff5881e6e23","target":"graph","created_at":"2026-05-18T00:55:35Z","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":"Deep learning has achieved a remarkable performance breakthrough in several fields, most notably in speech recognition, natural language processing, and computer vision. In particular, convolutional neural network (CNN) architectures currently produce state-of-the-art performance on a variety of image analysis tasks such as object detection and recognition. Most of deep learning research has so far focused on dealing with 1D, 2D, or 3D Euclidean-structured data such as acoustic signals, images, or videos. Recently, there has been an increasing interest in geometric deep learning, attempting to","authors_text":"Davide Boscaini, Emanuele Rodol\\`a, Federico Monti, Jan Svoboda, Jonathan Masci, Michael M. Bronstein","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-11-25T10:05:03Z","title":"Geometric deep learning on graphs and manifolds using mixture model CNNs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.08402","kind":"arxiv","version":3},"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":"sha256:543224165db82b4c1d887ea92b9865eebc5ccc643c3eb64675dc11b409c655eb","target":"record","created_at":"2026-05-18T00:55:35Z","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":"aa124f450dad99d31d0b191ba42e89345487c8364c864984669bb5c9f389b981","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-11-25T10:05:03Z","title_canon_sha256":"9ef13bceb25e1a812cded5cd16fe16c5c83c538c55c626ee1e462c3e68abd4bf"},"schema_version":"1.0","source":{"id":"1611.08402","kind":"arxiv","version":3}},"canonical_sha256":"fab07a58a95a55eb203460a6de688f2942542ed06ff735f1358c0f24afb22356","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fab07a58a95a55eb203460a6de688f2942542ed06ff735f1358c0f24afb22356","first_computed_at":"2026-05-18T00:55:35.532136Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:55:35.532136Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NUfVPUzBfynew9jJJxO0iCBteTige3vVaWJAQpBjHDU3nP2KxGAkd252V4/HCsGsdWr3wlOTwekdxU6SVmo2Cw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:55:35.532593Z","signed_message":"canonical_sha256_bytes"},"source_id":"1611.08402","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:543224165db82b4c1d887ea92b9865eebc5ccc643c3eb64675dc11b409c655eb","sha256:ddb276142c3559f29cf82cc27441c3dcbb06580ac6a2430df97baff5881e6e23"],"state_sha256":"8c6d32b52bc2bd17591b579df9cc511f625879cb35bffa3e02935f3a7232164a"}