{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:246I46SJPMXJ4P34LJ73V4X2GY","short_pith_number":"pith:246I46SJ","canonical_record":{"source":{"id":"2011.01507","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-11-03T06:53:53Z","cross_cats_sorted":[],"title_canon_sha256":"b67cda88999f50ca415e941ef2ff506569dbc9dea0b476adb81ccc6504efcc38","abstract_canon_sha256":"775c7e9b12749d397a51d1106b9a691f3defb7638de16fcda4b4d208428d16b0"},"schema_version":"1.0"},"canonical_sha256":"d73c8e7a497b2e9e3f7c5a7fbaf2fa36216c02cca1bb1620fc3ea4800e6bee61","source":{"kind":"arxiv","id":"2011.01507","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2011.01507","created_at":"2026-07-05T01:54:48Z"},{"alias_kind":"arxiv_version","alias_value":"2011.01507v4","created_at":"2026-07-05T01:54:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.01507","created_at":"2026-07-05T01:54:48Z"},{"alias_kind":"pith_short_12","alias_value":"246I46SJPMXJ","created_at":"2026-07-05T01:54:48Z"},{"alias_kind":"pith_short_16","alias_value":"246I46SJPMXJ4P34","created_at":"2026-07-05T01:54:48Z"},{"alias_kind":"pith_short_8","alias_value":"246I46SJ","created_at":"2026-07-05T01:54:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:246I46SJPMXJ4P34LJ73V4X2GY","target":"record","payload":{"canonical_record":{"source":{"id":"2011.01507","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-11-03T06:53:53Z","cross_cats_sorted":[],"title_canon_sha256":"b67cda88999f50ca415e941ef2ff506569dbc9dea0b476adb81ccc6504efcc38","abstract_canon_sha256":"775c7e9b12749d397a51d1106b9a691f3defb7638de16fcda4b4d208428d16b0"},"schema_version":"1.0"},"canonical_sha256":"d73c8e7a497b2e9e3f7c5a7fbaf2fa36216c02cca1bb1620fc3ea4800e6bee61","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:54:48.354695Z","signature_b64":"eP7Txl5P7dG8XfwX37ZDKz+Wt4lrrESaulAtaC2xlGLUI7YRBp+jA90lIeTJgiU9WzJitD05185dh8lJtWGjAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d73c8e7a497b2e9e3f7c5a7fbaf2fa36216c02cca1bb1620fc3ea4800e6bee61","last_reissued_at":"2026-07-05T01:54:48.354189Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:54:48.354189Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2011.01507","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:54:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"M0YpP66aR91hda+Ku2d+esfrS9QEq6clQKCd9in8BplmZ9IMBDV3SYMHLowKOo8KhsdRP262VEPkkI+/NFZiBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T12:55:37.678852Z"},"content_sha256":"e9cee82655cd2de19282b11ac20a69bdcf237a32a96f2ec22090cbffb41d175b","schema_version":"1.0","event_id":"sha256:e9cee82655cd2de19282b11ac20a69bdcf237a32a96f2ec22090cbffb41d175b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:246I46SJPMXJ4P34LJ73V4X2GY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"VEGA: Towards an End-to-End Configurable AutoML Pipeline","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bochao Wang, Chen Chen, Chenhan Jiang, Chunjing Xu, Dehua Song, Fengwei Zhou, Hang Xu, Han Shu, Jiajin Zhang, Jiawei Li, Kai Han, Lanqing Hong, Ning Kang, Tong Zhang, Wei Zhang, Wenzhi Liu, Xiaozhi Fang, Xinghao Chen, Xinyue Cai, Yixing Xu, Yong Li, Yunhe Wang, Zhenguo Li, Zhicheng Liu","submitted_at":"2020-11-03T06:53:53Z","abstract_excerpt":"Automated Machine Learning (AutoML) is an important industrial solution for automatic discovery and deployment of the machine learning models. However, designing an integrated AutoML system faces four great challenges of configurability, scalability, integrability, and platform diversity. In this work, we present VEGA, an efficient and comprehensive AutoML framework that is compatible and optimized for multiple hardware platforms. a) The VEGA pipeline integrates various modules of AutoML, including Neural Architecture Search (NAS), Hyperparameter Optimization (HPO), Auto Data Augmentation, Mod"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.01507","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2011.01507/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T01:54:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2ytPYt+Qw3TEHylSv1hYo15kQ7dX4Z4wX1jT+TmOEp451xvQl5ARDzA2Czng72ypB/sg917///XqSbkpx2zcDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T12:55:37.679509Z"},"content_sha256":"131dc48dc335d68cbbba649713d289cbe33218f44017cddbec9082e3a5206d7a","schema_version":"1.0","event_id":"sha256:131dc48dc335d68cbbba649713d289cbe33218f44017cddbec9082e3a5206d7a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/246I46SJPMXJ4P34LJ73V4X2GY/bundle.json","state_url":"https://pith.science/pith/246I46SJPMXJ4P34LJ73V4X2GY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/246I46SJPMXJ4P34LJ73V4X2GY/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":"2026-08-05T12:55:37Z","links":{"resolver":"https://pith.science/pith/246I46SJPMXJ4P34LJ73V4X2GY","bundle":"https://pith.science/pith/246I46SJPMXJ4P34LJ73V4X2GY/bundle.json","state":"https://pith.science/pith/246I46SJPMXJ4P34LJ73V4X2GY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/246I46SJPMXJ4P34LJ73V4X2GY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:246I46SJPMXJ4P34LJ73V4X2GY","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":"775c7e9b12749d397a51d1106b9a691f3defb7638de16fcda4b4d208428d16b0","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-11-03T06:53:53Z","title_canon_sha256":"b67cda88999f50ca415e941ef2ff506569dbc9dea0b476adb81ccc6504efcc38"},"schema_version":"1.0","source":{"id":"2011.01507","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2011.01507","created_at":"2026-07-05T01:54:48Z"},{"alias_kind":"arxiv_version","alias_value":"2011.01507v4","created_at":"2026-07-05T01:54:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.01507","created_at":"2026-07-05T01:54:48Z"},{"alias_kind":"pith_short_12","alias_value":"246I46SJPMXJ","created_at":"2026-07-05T01:54:48Z"},{"alias_kind":"pith_short_16","alias_value":"246I46SJPMXJ4P34","created_at":"2026-07-05T01:54:48Z"},{"alias_kind":"pith_short_8","alias_value":"246I46SJ","created_at":"2026-07-05T01:54:48Z"}],"graph_snapshots":[{"event_id":"sha256:131dc48dc335d68cbbba649713d289cbe33218f44017cddbec9082e3a5206d7a","target":"graph","created_at":"2026-07-05T01:54:48Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2011.01507/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Automated Machine Learning (AutoML) is an important industrial solution for automatic discovery and deployment of the machine learning models. However, designing an integrated AutoML system faces four great challenges of configurability, scalability, integrability, and platform diversity. In this work, we present VEGA, an efficient and comprehensive AutoML framework that is compatible and optimized for multiple hardware platforms. a) The VEGA pipeline integrates various modules of AutoML, including Neural Architecture Search (NAS), Hyperparameter Optimization (HPO), Auto Data Augmentation, Mod","authors_text":"Bochao Wang, Chen Chen, Chenhan Jiang, Chunjing Xu, Dehua Song, Fengwei Zhou, Hang Xu, Han Shu, Jiajin Zhang, Jiawei Li, Kai Han, Lanqing Hong, Ning Kang, Tong Zhang, Wei Zhang, Wenzhi Liu, Xiaozhi Fang, Xinghao Chen, Xinyue Cai, Yixing Xu, Yong Li, Yunhe Wang, Zhenguo Li, Zhicheng Liu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-11-03T06:53:53Z","title":"VEGA: Towards an End-to-End Configurable AutoML Pipeline"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.01507","kind":"arxiv","version":4},"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:e9cee82655cd2de19282b11ac20a69bdcf237a32a96f2ec22090cbffb41d175b","target":"record","created_at":"2026-07-05T01:54:48Z","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":"775c7e9b12749d397a51d1106b9a691f3defb7638de16fcda4b4d208428d16b0","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-11-03T06:53:53Z","title_canon_sha256":"b67cda88999f50ca415e941ef2ff506569dbc9dea0b476adb81ccc6504efcc38"},"schema_version":"1.0","source":{"id":"2011.01507","kind":"arxiv","version":4}},"canonical_sha256":"d73c8e7a497b2e9e3f7c5a7fbaf2fa36216c02cca1bb1620fc3ea4800e6bee61","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d73c8e7a497b2e9e3f7c5a7fbaf2fa36216c02cca1bb1620fc3ea4800e6bee61","first_computed_at":"2026-07-05T01:54:48.354189Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:54:48.354189Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"eP7Txl5P7dG8XfwX37ZDKz+Wt4lrrESaulAtaC2xlGLUI7YRBp+jA90lIeTJgiU9WzJitD05185dh8lJtWGjAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T01:54:48.354695Z","signed_message":"canonical_sha256_bytes"},"source_id":"2011.01507","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e9cee82655cd2de19282b11ac20a69bdcf237a32a96f2ec22090cbffb41d175b","sha256:131dc48dc335d68cbbba649713d289cbe33218f44017cddbec9082e3a5206d7a"],"state_sha256":"0577f6ee736c2f8f30f16402e2185abf39affbc6df8a2322f53a539ce8476b44"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"85OqjuF+xxeB+3t8S+Fe0moDPo3OQPCmAouBk0Z41B0zavY+7IHsTaBOL58LDTlh6dLnzU5uZRB4vLwNMowSCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T12:55:37.684611Z","bundle_sha256":"e5afaea58fa4d8dc29a3fd7439d74341794d3368919fc921546e5f53b8dacee0"}}