{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:HF46WSVJKTUVAR7M3XPQ7LQ2DX","short_pith_number":"pith:HF46WSVJ","canonical_record":{"source":{"id":"1608.04236","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-08-15T11:14:35Z","cross_cats_sorted":["cs.HC","cs.LG","stat.ML"],"title_canon_sha256":"95e6295c22d603743413b8d1484368ab6129bf7a7fafcc70e307c2e32d13178b","abstract_canon_sha256":"4f266013bc89780f5560707644206eef188e72a527158ed8d222a8baa3ec5088"},"schema_version":"1.0"},"canonical_sha256":"3979eb4aa954e95047ecdddf0fae1a1dc829571094b2e978063b841e1aa73e09","source":{"kind":"arxiv","id":"1608.04236","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1608.04236","created_at":"2026-05-18T01:08:37Z"},{"alias_kind":"arxiv_version","alias_value":"1608.04236v2","created_at":"2026-05-18T01:08:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.04236","created_at":"2026-05-18T01:08:37Z"},{"alias_kind":"pith_short_12","alias_value":"HF46WSVJKTUV","created_at":"2026-05-18T12:30:19Z"},{"alias_kind":"pith_short_16","alias_value":"HF46WSVJKTUVAR7M","created_at":"2026-05-18T12:30:19Z"},{"alias_kind":"pith_short_8","alias_value":"HF46WSVJ","created_at":"2026-05-18T12:30:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:HF46WSVJKTUVAR7M3XPQ7LQ2DX","target":"record","payload":{"canonical_record":{"source":{"id":"1608.04236","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-08-15T11:14:35Z","cross_cats_sorted":["cs.HC","cs.LG","stat.ML"],"title_canon_sha256":"95e6295c22d603743413b8d1484368ab6129bf7a7fafcc70e307c2e32d13178b","abstract_canon_sha256":"4f266013bc89780f5560707644206eef188e72a527158ed8d222a8baa3ec5088"},"schema_version":"1.0"},"canonical_sha256":"3979eb4aa954e95047ecdddf0fae1a1dc829571094b2e978063b841e1aa73e09","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:08:37.325011Z","signature_b64":"GX7xn30rasLPaUP7hKEAtH1dNv2TRh3O3VepkzuEoiUY1MtLItBpeInKci+SM0JLlXzoxxZnxACgHAARyfLQCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3979eb4aa954e95047ecdddf0fae1a1dc829571094b2e978063b841e1aa73e09","last_reissued_at":"2026-05-18T01:08:37.324576Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:08:37.324576Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1608.04236","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:08:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SlHxPAfCK0i+RfQ9yWPTv8N+uE4noiusBUVxx5aNhqLLz05tHNRR/ZvXqKRTmuztHYjCtSvOWiykjLN88o2jBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T09:20:21.333337Z"},"content_sha256":"99de89ab0ca6ee6a89a6d408fd13ebcb525365d9808cf52b8bd4c376533f0bfe","schema_version":"1.0","event_id":"sha256:99de89ab0ca6ee6a89a6d408fd13ebcb525365d9808cf52b8bd4c376533f0bfe"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:HF46WSVJKTUVAR7M3XPQ7LQ2DX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Generative and Discriminative Voxel Modeling with Convolutional Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.HC","cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Andrew Brock, J.M. Ritchie, Nick Weston, Theodore Lim","submitted_at":"2016-08-15T11:14:35Z","abstract_excerpt":"When working with three-dimensional data, choice of representation is key. We explore voxel-based models, and present evidence for the viability of voxellated representations in applications including shape modeling and object classification. Our key contributions are methods for training voxel-based variational autoencoders, a user interface for exploring the latent space learned by the autoencoder, and a deep convolutional neural network architecture for object classification. We address challenges unique to voxel-based representations, and empirically evaluate our models on the ModelNet ben"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.04236","kind":"arxiv","version":2},"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":"2026-05-18T01:08:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jiPH+02KlY4r1JeeKAyDdptYerDEm1BodQiH6DWnqQFwSh3qVVfqiFRLesgU/6iFfbQ70grXtpTvAnHRwJJDCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T09:20:21.333867Z"},"content_sha256":"124dca8c6e981ba1c29580714bebd5b91a6c63ea4d34a6960e82fdbd10ff6abf","schema_version":"1.0","event_id":"sha256:124dca8c6e981ba1c29580714bebd5b91a6c63ea4d34a6960e82fdbd10ff6abf"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HF46WSVJKTUVAR7M3XPQ7LQ2DX/bundle.json","state_url":"https://pith.science/pith/HF46WSVJKTUVAR7M3XPQ7LQ2DX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HF46WSVJKTUVAR7M3XPQ7LQ2DX/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":"2026-05-30T09:20:21Z","links":{"resolver":"https://pith.science/pith/HF46WSVJKTUVAR7M3XPQ7LQ2DX","bundle":"https://pith.science/pith/HF46WSVJKTUVAR7M3XPQ7LQ2DX/bundle.json","state":"https://pith.science/pith/HF46WSVJKTUVAR7M3XPQ7LQ2DX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HF46WSVJKTUVAR7M3XPQ7LQ2DX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:HF46WSVJKTUVAR7M3XPQ7LQ2DX","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":"4f266013bc89780f5560707644206eef188e72a527158ed8d222a8baa3ec5088","cross_cats_sorted":["cs.HC","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-08-15T11:14:35Z","title_canon_sha256":"95e6295c22d603743413b8d1484368ab6129bf7a7fafcc70e307c2e32d13178b"},"schema_version":"1.0","source":{"id":"1608.04236","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1608.04236","created_at":"2026-05-18T01:08:37Z"},{"alias_kind":"arxiv_version","alias_value":"1608.04236v2","created_at":"2026-05-18T01:08:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.04236","created_at":"2026-05-18T01:08:37Z"},{"alias_kind":"pith_short_12","alias_value":"HF46WSVJKTUV","created_at":"2026-05-18T12:30:19Z"},{"alias_kind":"pith_short_16","alias_value":"HF46WSVJKTUVAR7M","created_at":"2026-05-18T12:30:19Z"},{"alias_kind":"pith_short_8","alias_value":"HF46WSVJ","created_at":"2026-05-18T12:30:19Z"}],"graph_snapshots":[{"event_id":"sha256:124dca8c6e981ba1c29580714bebd5b91a6c63ea4d34a6960e82fdbd10ff6abf","target":"graph","created_at":"2026-05-18T01:08:37Z","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":"When working with three-dimensional data, choice of representation is key. We explore voxel-based models, and present evidence for the viability of voxellated representations in applications including shape modeling and object classification. Our key contributions are methods for training voxel-based variational autoencoders, a user interface for exploring the latent space learned by the autoencoder, and a deep convolutional neural network architecture for object classification. We address challenges unique to voxel-based representations, and empirically evaluate our models on the ModelNet ben","authors_text":"Andrew Brock, J.M. Ritchie, Nick Weston, Theodore Lim","cross_cats":["cs.HC","cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-08-15T11:14:35Z","title":"Generative and Discriminative Voxel Modeling with Convolutional Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.04236","kind":"arxiv","version":2},"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":"sha256:99de89ab0ca6ee6a89a6d408fd13ebcb525365d9808cf52b8bd4c376533f0bfe","target":"record","created_at":"2026-05-18T01:08:37Z","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":"4f266013bc89780f5560707644206eef188e72a527158ed8d222a8baa3ec5088","cross_cats_sorted":["cs.HC","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-08-15T11:14:35Z","title_canon_sha256":"95e6295c22d603743413b8d1484368ab6129bf7a7fafcc70e307c2e32d13178b"},"schema_version":"1.0","source":{"id":"1608.04236","kind":"arxiv","version":2}},"canonical_sha256":"3979eb4aa954e95047ecdddf0fae1a1dc829571094b2e978063b841e1aa73e09","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3979eb4aa954e95047ecdddf0fae1a1dc829571094b2e978063b841e1aa73e09","first_computed_at":"2026-05-18T01:08:37.324576Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:08:37.324576Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GX7xn30rasLPaUP7hKEAtH1dNv2TRh3O3VepkzuEoiUY1MtLItBpeInKci+SM0JLlXzoxxZnxACgHAARyfLQCw==","signature_status":"signed_v1","signed_at":"2026-05-18T01:08:37.325011Z","signed_message":"canonical_sha256_bytes"},"source_id":"1608.04236","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:99de89ab0ca6ee6a89a6d408fd13ebcb525365d9808cf52b8bd4c376533f0bfe","sha256:124dca8c6e981ba1c29580714bebd5b91a6c63ea4d34a6960e82fdbd10ff6abf"],"state_sha256":"f09e51dc7518a85c0be881b5dd672a0826d8b9bfd294b19b98c9c3d540a78983"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vn8+je4zFOHJvB4SVJSxKmOw6YqnYD13pjciEvueHqzCxKPZctfR3s+CiZK3Yx96F3YqIYe6fPUVAgbLjDWCDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T09:20:21.336426Z","bundle_sha256":"bf9edb2d3a52304d62513d7b0174b77650fbe15d032f3ce55229de8f7c736de6"}}