{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:G5ZLDCBCTYETYEWXXPOSNLXMX3","short_pith_number":"pith:G5ZLDCBC","canonical_record":{"source":{"id":"1612.04774","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-12-14T19:06:05Z","cross_cats_sorted":["cs.CG"],"title_canon_sha256":"cac336eb9c75a604b1978cac900ec54df0a4c0224c259f896d8146fbbe22c821","abstract_canon_sha256":"edd73983966c6ae509ea7df054d828cb79a420115f488f28d223351584bd775f"},"schema_version":"1.0"},"canonical_sha256":"3772b188229e093c12d7bbdd26aeecbef880d2e5db5adbd8c572645ea61afa2f","source":{"kind":"arxiv","id":"1612.04774","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1612.04774","created_at":"2026-05-18T00:54:58Z"},{"alias_kind":"arxiv_version","alias_value":"1612.04774v1","created_at":"2026-05-18T00:54:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.04774","created_at":"2026-05-18T00:54:58Z"},{"alias_kind":"pith_short_12","alias_value":"G5ZLDCBCTYET","created_at":"2026-05-18T12:30:15Z"},{"alias_kind":"pith_short_16","alias_value":"G5ZLDCBCTYETYEWX","created_at":"2026-05-18T12:30:15Z"},{"alias_kind":"pith_short_8","alias_value":"G5ZLDCBC","created_at":"2026-05-18T12:30:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:G5ZLDCBCTYETYEWXXPOSNLXMX3","target":"record","payload":{"canonical_record":{"source":{"id":"1612.04774","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-12-14T19:06:05Z","cross_cats_sorted":["cs.CG"],"title_canon_sha256":"cac336eb9c75a604b1978cac900ec54df0a4c0224c259f896d8146fbbe22c821","abstract_canon_sha256":"edd73983966c6ae509ea7df054d828cb79a420115f488f28d223351584bd775f"},"schema_version":"1.0"},"canonical_sha256":"3772b188229e093c12d7bbdd26aeecbef880d2e5db5adbd8c572645ea61afa2f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:54:58.647916Z","signature_b64":"/rR61zhHq7W6dr3VGIXTzdvqbfn8QWp9m6IaNPI0uxw8C5zbjD48uvtTFoHIDIdHh/EhmMQm01Nngchpo30ZBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3772b188229e093c12d7bbdd26aeecbef880d2e5db5adbd8c572645ea61afa2f","last_reissued_at":"2026-05-18T00:54:58.647396Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:54:58.647396Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1612.04774","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:54:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/AIIGVzDVkTTwONDp73c4qRVlWyeYR4FWQ8AfZCHblLY2Y3j80NISvlgQ/z6SpCJ3WQzRoHgxGtWf3rbkBcyDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T06:54:41.120440Z"},"content_sha256":"eb3c11e1ca2d01e22c5cd3ed0379ad405671278f2e6789a702e583154bea9949","schema_version":"1.0","event_id":"sha256:eb3c11e1ca2d01e22c5cd3ed0379ad405671278f2e6789a702e583154bea9949"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:G5ZLDCBCTYETYEWXXPOSNLXMX3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Beam Search for Learning a Deep Convolutional Neural Network of 3D Shapes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CG"],"primary_cat":"cs.CV","authors_text":"Sinisa Todorovic, Xu Xu","submitted_at":"2016-12-14T19:06:05Z","abstract_excerpt":"This paper addresses 3D shape recognition. Recent work typically represents a 3D shape as a set of binary variables corresponding to 3D voxels of a uniform 3D grid centered on the shape, and resorts to deep convolutional neural networks(CNNs) for modeling these binary variables. Robust learning of such CNNs is currently limited by the small datasets of 3D shapes available, an order of magnitude smaller than other common datasets in computer vision. Related work typically deals with the small training datasets using a number of ad hoc, hand-tuning strategies. To address this issue, we formulate"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.04774","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:54:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2LTCIHFnbdn5yz1ObR0+jeRjWckczMUMW1vCVsJPqvX/pZxGJZdZDylwmmnoCaXrPDcov8oUiwyTh9xAFUYlDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T06:54:41.120793Z"},"content_sha256":"5dbbea10630ce15b4bb2447a7e9a1ecc5835a20b95557fe1a139ebc17cf213bb","schema_version":"1.0","event_id":"sha256:5dbbea10630ce15b4bb2447a7e9a1ecc5835a20b95557fe1a139ebc17cf213bb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/G5ZLDCBCTYETYEWXXPOSNLXMX3/bundle.json","state_url":"https://pith.science/pith/G5ZLDCBCTYETYEWXXPOSNLXMX3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/G5ZLDCBCTYETYEWXXPOSNLXMX3/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-30T06:54:41Z","links":{"resolver":"https://pith.science/pith/G5ZLDCBCTYETYEWXXPOSNLXMX3","bundle":"https://pith.science/pith/G5ZLDCBCTYETYEWXXPOSNLXMX3/bundle.json","state":"https://pith.science/pith/G5ZLDCBCTYETYEWXXPOSNLXMX3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/G5ZLDCBCTYETYEWXXPOSNLXMX3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:G5ZLDCBCTYETYEWXXPOSNLXMX3","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":"edd73983966c6ae509ea7df054d828cb79a420115f488f28d223351584bd775f","cross_cats_sorted":["cs.CG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-12-14T19:06:05Z","title_canon_sha256":"cac336eb9c75a604b1978cac900ec54df0a4c0224c259f896d8146fbbe22c821"},"schema_version":"1.0","source":{"id":"1612.04774","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1612.04774","created_at":"2026-05-18T00:54:58Z"},{"alias_kind":"arxiv_version","alias_value":"1612.04774v1","created_at":"2026-05-18T00:54:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.04774","created_at":"2026-05-18T00:54:58Z"},{"alias_kind":"pith_short_12","alias_value":"G5ZLDCBCTYET","created_at":"2026-05-18T12:30:15Z"},{"alias_kind":"pith_short_16","alias_value":"G5ZLDCBCTYETYEWX","created_at":"2026-05-18T12:30:15Z"},{"alias_kind":"pith_short_8","alias_value":"G5ZLDCBC","created_at":"2026-05-18T12:30:15Z"}],"graph_snapshots":[{"event_id":"sha256:5dbbea10630ce15b4bb2447a7e9a1ecc5835a20b95557fe1a139ebc17cf213bb","target":"graph","created_at":"2026-05-18T00:54:58Z","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":"This paper addresses 3D shape recognition. Recent work typically represents a 3D shape as a set of binary variables corresponding to 3D voxels of a uniform 3D grid centered on the shape, and resorts to deep convolutional neural networks(CNNs) for modeling these binary variables. Robust learning of such CNNs is currently limited by the small datasets of 3D shapes available, an order of magnitude smaller than other common datasets in computer vision. Related work typically deals with the small training datasets using a number of ad hoc, hand-tuning strategies. To address this issue, we formulate","authors_text":"Sinisa Todorovic, Xu Xu","cross_cats":["cs.CG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-12-14T19:06:05Z","title":"Beam Search for Learning a Deep Convolutional Neural Network of 3D Shapes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.04774","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:eb3c11e1ca2d01e22c5cd3ed0379ad405671278f2e6789a702e583154bea9949","target":"record","created_at":"2026-05-18T00:54:58Z","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":"edd73983966c6ae509ea7df054d828cb79a420115f488f28d223351584bd775f","cross_cats_sorted":["cs.CG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-12-14T19:06:05Z","title_canon_sha256":"cac336eb9c75a604b1978cac900ec54df0a4c0224c259f896d8146fbbe22c821"},"schema_version":"1.0","source":{"id":"1612.04774","kind":"arxiv","version":1}},"canonical_sha256":"3772b188229e093c12d7bbdd26aeecbef880d2e5db5adbd8c572645ea61afa2f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3772b188229e093c12d7bbdd26aeecbef880d2e5db5adbd8c572645ea61afa2f","first_computed_at":"2026-05-18T00:54:58.647396Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:54:58.647396Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/rR61zhHq7W6dr3VGIXTzdvqbfn8QWp9m6IaNPI0uxw8C5zbjD48uvtTFoHIDIdHh/EhmMQm01Nngchpo30ZBA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:54:58.647916Z","signed_message":"canonical_sha256_bytes"},"source_id":"1612.04774","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:eb3c11e1ca2d01e22c5cd3ed0379ad405671278f2e6789a702e583154bea9949","sha256:5dbbea10630ce15b4bb2447a7e9a1ecc5835a20b95557fe1a139ebc17cf213bb"],"state_sha256":"949a5958133e20f4c09c2e446238c241cc807971a89ffd17deeb05f8dbe65d68"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3cmHnvLldU/3np3Ev05kXZ9rhvzawxShavARYseW/05+ML0BV3OktaQflheCgK2vhcd34bR4gYSTcw4tKuvlCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T06:54:41.122783Z","bundle_sha256":"36f45b953d1e1623b0227e9875d5f33e44d0fdff3efd034faf0beb2af6a9259b"}}