{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:UTGDSGH3WU2AEW3GEOFIJJUV2Z","short_pith_number":"pith:UTGDSGH3","canonical_record":{"source":{"id":"1409.3358","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2014-09-11T08:44:28Z","cross_cats_sorted":["cs.LG","cs.NE"],"title_canon_sha256":"f137b203f8ad23e0a9ada02ea5cbd3ce9e84b7eaf226564cbac026ad6bdee696","abstract_canon_sha256":"21188880caf228f65c16f2c71ed695ad49218cd585d2a9a205114272912793ae"},"schema_version":"1.0"},"canonical_sha256":"a4cc3918fbb534025b66238a84a695d670e5c4ff88c2f5d35bcb740145780b12","source":{"kind":"arxiv","id":"1409.3358","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1409.3358","created_at":"2026-05-18T02:43:01Z"},{"alias_kind":"arxiv_version","alias_value":"1409.3358v1","created_at":"2026-05-18T02:43:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1409.3358","created_at":"2026-05-18T02:43:01Z"},{"alias_kind":"pith_short_12","alias_value":"UTGDSGH3WU2A","created_at":"2026-05-18T12:28:52Z"},{"alias_kind":"pith_short_16","alias_value":"UTGDSGH3WU2AEW3G","created_at":"2026-05-18T12:28:52Z"},{"alias_kind":"pith_short_8","alias_value":"UTGDSGH3","created_at":"2026-05-18T12:28:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:UTGDSGH3WU2AEW3GEOFIJJUV2Z","target":"record","payload":{"canonical_record":{"source":{"id":"1409.3358","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2014-09-11T08:44:28Z","cross_cats_sorted":["cs.LG","cs.NE"],"title_canon_sha256":"f137b203f8ad23e0a9ada02ea5cbd3ce9e84b7eaf226564cbac026ad6bdee696","abstract_canon_sha256":"21188880caf228f65c16f2c71ed695ad49218cd585d2a9a205114272912793ae"},"schema_version":"1.0"},"canonical_sha256":"a4cc3918fbb534025b66238a84a695d670e5c4ff88c2f5d35bcb740145780b12","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:43:01.568983Z","signature_b64":"Tr3P/qvd+lPaQPqCjjrv9VckC5JhMuwxWhLTLDluLNyK/2mD7ivA8kug42v5pu/J7EdheBRxuwrMgqOBrqEWDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a4cc3918fbb534025b66238a84a695d670e5c4ff88c2f5d35bcb740145780b12","last_reissued_at":"2026-05-18T02:43:01.568502Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:43:01.568502Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1409.3358","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":"2026-05-18T02:43:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eLmcqnR1g7HcaR1/PKfyohbtBGZDHK4l+WjJG+CNcoPvU45UgfoPgXnmSTPz1LkZbDNYFCTj86xuXPh7p6fFCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T11:25:58.100447Z"},"content_sha256":"b9ac09049df921c9669acd9793bdcb7eab2c5ccf2aa6024290fd677bc0421f1f","schema_version":"1.0","event_id":"sha256:b9ac09049df921c9669acd9793bdcb7eab2c5ccf2aa6024290fd677bc0421f1f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:UTGDSGH3WU2AEW3GEOFIJJUV2Z","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Building Program Vector Representations for Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NE"],"primary_cat":"cs.SE","authors_text":"Ge Li, Hao Peng, Lili Mou, Lu Zhang, Yan Xu, Yuxuan Liu, Zhi Jin","submitted_at":"2014-09-11T08:44:28Z","abstract_excerpt":"Deep learning has made significant breakthroughs in various fields of artificial intelligence. Advantages of deep learning include the ability to capture highly complicated features, weak involvement of human engineering, etc. However, it is still virtually impossible to use deep learning to analyze programs since deep architectures cannot be trained effectively with pure back propagation. In this pioneering paper, we propose the \"coding criterion\" to build program vector representations, which are the premise of deep learning for program analysis. Our representation learning approach directly"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1409.3358","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":"2026-05-18T02:43:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2ftIHcPuYwTshsFQv+/R0j+Ll46/T087z03/CrvmQIqM24NIvZ+4dbZMohnJUIXmecZHK9Y7oEstbFk2omLrAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T11:25:58.100854Z"},"content_sha256":"841c6ccc80b86e1e9e4457a15217fbb74c1b33430e24ffe4cfdd7582cdc2a1be","schema_version":"1.0","event_id":"sha256:841c6ccc80b86e1e9e4457a15217fbb74c1b33430e24ffe4cfdd7582cdc2a1be"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UTGDSGH3WU2AEW3GEOFIJJUV2Z/bundle.json","state_url":"https://pith.science/pith/UTGDSGH3WU2AEW3GEOFIJJUV2Z/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UTGDSGH3WU2AEW3GEOFIJJUV2Z/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":"2026-06-01T11:25:58Z","links":{"resolver":"https://pith.science/pith/UTGDSGH3WU2AEW3GEOFIJJUV2Z","bundle":"https://pith.science/pith/UTGDSGH3WU2AEW3GEOFIJJUV2Z/bundle.json","state":"https://pith.science/pith/UTGDSGH3WU2AEW3GEOFIJJUV2Z/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UTGDSGH3WU2AEW3GEOFIJJUV2Z/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:UTGDSGH3WU2AEW3GEOFIJJUV2Z","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":"21188880caf228f65c16f2c71ed695ad49218cd585d2a9a205114272912793ae","cross_cats_sorted":["cs.LG","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2014-09-11T08:44:28Z","title_canon_sha256":"f137b203f8ad23e0a9ada02ea5cbd3ce9e84b7eaf226564cbac026ad6bdee696"},"schema_version":"1.0","source":{"id":"1409.3358","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1409.3358","created_at":"2026-05-18T02:43:01Z"},{"alias_kind":"arxiv_version","alias_value":"1409.3358v1","created_at":"2026-05-18T02:43:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1409.3358","created_at":"2026-05-18T02:43:01Z"},{"alias_kind":"pith_short_12","alias_value":"UTGDSGH3WU2A","created_at":"2026-05-18T12:28:52Z"},{"alias_kind":"pith_short_16","alias_value":"UTGDSGH3WU2AEW3G","created_at":"2026-05-18T12:28:52Z"},{"alias_kind":"pith_short_8","alias_value":"UTGDSGH3","created_at":"2026-05-18T12:28:52Z"}],"graph_snapshots":[{"event_id":"sha256:841c6ccc80b86e1e9e4457a15217fbb74c1b33430e24ffe4cfdd7582cdc2a1be","target":"graph","created_at":"2026-05-18T02:43:01Z","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 made significant breakthroughs in various fields of artificial intelligence. Advantages of deep learning include the ability to capture highly complicated features, weak involvement of human engineering, etc. However, it is still virtually impossible to use deep learning to analyze programs since deep architectures cannot be trained effectively with pure back propagation. In this pioneering paper, we propose the \"coding criterion\" to build program vector representations, which are the premise of deep learning for program analysis. Our representation learning approach directly","authors_text":"Ge Li, Hao Peng, Lili Mou, Lu Zhang, Yan Xu, Yuxuan Liu, Zhi Jin","cross_cats":["cs.LG","cs.NE"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2014-09-11T08:44:28Z","title":"Building Program Vector Representations for Deep Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1409.3358","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:b9ac09049df921c9669acd9793bdcb7eab2c5ccf2aa6024290fd677bc0421f1f","target":"record","created_at":"2026-05-18T02:43:01Z","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":"21188880caf228f65c16f2c71ed695ad49218cd585d2a9a205114272912793ae","cross_cats_sorted":["cs.LG","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2014-09-11T08:44:28Z","title_canon_sha256":"f137b203f8ad23e0a9ada02ea5cbd3ce9e84b7eaf226564cbac026ad6bdee696"},"schema_version":"1.0","source":{"id":"1409.3358","kind":"arxiv","version":1}},"canonical_sha256":"a4cc3918fbb534025b66238a84a695d670e5c4ff88c2f5d35bcb740145780b12","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a4cc3918fbb534025b66238a84a695d670e5c4ff88c2f5d35bcb740145780b12","first_computed_at":"2026-05-18T02:43:01.568502Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:43:01.568502Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Tr3P/qvd+lPaQPqCjjrv9VckC5JhMuwxWhLTLDluLNyK/2mD7ivA8kug42v5pu/J7EdheBRxuwrMgqOBrqEWDA==","signature_status":"signed_v1","signed_at":"2026-05-18T02:43:01.568983Z","signed_message":"canonical_sha256_bytes"},"source_id":"1409.3358","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b9ac09049df921c9669acd9793bdcb7eab2c5ccf2aa6024290fd677bc0421f1f","sha256:841c6ccc80b86e1e9e4457a15217fbb74c1b33430e24ffe4cfdd7582cdc2a1be"],"state_sha256":"39f746dbf60d0ef67068cbdedf0a437ffb8d42ada6d0bf4892c1a535e525dde2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JFcudM8GuFrfZfj+tP7nzx6ehXH/jckuWXyJme3uQT8015Gk/RKWg2+7Dne9FmKjUT/97qRqb0XA4NtJF8aoCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-01T11:25:58.102859Z","bundle_sha256":"afb5b5b1efefeab84fddda12068a145019e6a88bd62fddd356c68d7adbc5fc42"}}