{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:BJKRLCUICBJ3ZHNAFRGFE7L2D7","short_pith_number":"pith:BJKRLCUI","canonical_record":{"source":{"id":"1911.03080","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-11-08T06:42:07Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"cd74d8b40f5a52dfe3162692e98f6706ebbfe82a4a19f4d627feee2f69eb44f7","abstract_canon_sha256":"1a5aae140a87bfa3c76b93cb6f73ed7879f96967bc720bc4ab1522d0e2cc5661"},"schema_version":"1.0"},"canonical_sha256":"0a55158a881053bc9da02c4c527d7a1fe5bbb8e0dbd89a9c7849b298ca353c7b","source":{"kind":"arxiv","id":"1911.03080","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1911.03080","created_at":"2026-07-05T00:17:59Z"},{"alias_kind":"arxiv_version","alias_value":"1911.03080v1","created_at":"2026-07-05T00:17:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.03080","created_at":"2026-07-05T00:17:59Z"},{"alias_kind":"pith_short_12","alias_value":"BJKRLCUICBJ3","created_at":"2026-07-05T00:17:59Z"},{"alias_kind":"pith_short_16","alias_value":"BJKRLCUICBJ3ZHNA","created_at":"2026-07-05T00:17:59Z"},{"alias_kind":"pith_short_8","alias_value":"BJKRLCUI","created_at":"2026-07-05T00:17:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:BJKRLCUICBJ3ZHNAFRGFE7L2D7","target":"record","payload":{"canonical_record":{"source":{"id":"1911.03080","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-11-08T06:42:07Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"cd74d8b40f5a52dfe3162692e98f6706ebbfe82a4a19f4d627feee2f69eb44f7","abstract_canon_sha256":"1a5aae140a87bfa3c76b93cb6f73ed7879f96967bc720bc4ab1522d0e2cc5661"},"schema_version":"1.0"},"canonical_sha256":"0a55158a881053bc9da02c4c527d7a1fe5bbb8e0dbd89a9c7849b298ca353c7b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:17:59.838695Z","signature_b64":"VasaepcqhxKU22xQjSQ5bgNsWAUlf50JMlIj/yL+nQNAFimM+COmefpAdzYX37SbdEbJNc8/BJrjOZRHuuqxDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0a55158a881053bc9da02c4c527d7a1fe5bbb8e0dbd89a9c7849b298ca353c7b","last_reissued_at":"2026-07-05T00:17:59.838344Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:17:59.838344Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1911.03080","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":"2026-07-05T00:17:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"faII9fJzdF69Hq5QOrmIcnhl0geqKNPEGeECUIx7npn9uVDqn2xB8vvUAYPbKFatdvx5n7ioy+rWGWV7ofVRDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T18:00:14.015983Z"},"content_sha256":"f644e24c236d5b0d8da15159c9938b98a295d91cf1eb1ea0d6166a63b2bbe6e2","schema_version":"1.0","event_id":"sha256:f644e24c236d5b0d8da15159c9938b98a295d91cf1eb1ea0d6166a63b2bbe6e2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:BJKRLCUICBJ3ZHNAFRGFE7L2D7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep geometric knowledge distillation with graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Antonio Ortega, Carlos Lassance, Ghouthi Boukli Hacene, Jian Tang, Myriam Bontonou, Vincent Gripon","submitted_at":"2019-11-08T06:42:07Z","abstract_excerpt":"In most cases deep learning architectures are trained disregarding the amount of operations and energy consumption. However, some applications, like embedded systems, can be resource-constrained during inference. A popular approach to reduce the size of a deep learning architecture consists in distilling knowledge from a bigger network (teacher) to a smaller one (student). Directly training the student to mimic the teacher representation can be effective, but it requires that both share the same latent space dimensions. In this work, we focus instead on relative knowledge distillation (RKD), w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.03080","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1911.03080/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":"2026-07-05T00:17:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"G5nK1n8oVf1OYrPR9SJPHjKclwmLS99AOIBuacgZiELl+H9+LDrpwMvnISmhN3rqWvftmvyU4xETROn4qIZmCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T18:00:14.016637Z"},"content_sha256":"252f7936466b59a5bb21431b5bbb6f63a52d0880ffadf2966cca9e4d5b96161e","schema_version":"1.0","event_id":"sha256:252f7936466b59a5bb21431b5bbb6f63a52d0880ffadf2966cca9e4d5b96161e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BJKRLCUICBJ3ZHNAFRGFE7L2D7/bundle.json","state_url":"https://pith.science/pith/BJKRLCUICBJ3ZHNAFRGFE7L2D7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BJKRLCUICBJ3ZHNAFRGFE7L2D7/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":"2026-08-03T18:00:14Z","links":{"resolver":"https://pith.science/pith/BJKRLCUICBJ3ZHNAFRGFE7L2D7","bundle":"https://pith.science/pith/BJKRLCUICBJ3ZHNAFRGFE7L2D7/bundle.json","state":"https://pith.science/pith/BJKRLCUICBJ3ZHNAFRGFE7L2D7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BJKRLCUICBJ3ZHNAFRGFE7L2D7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:BJKRLCUICBJ3ZHNAFRGFE7L2D7","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":"1a5aae140a87bfa3c76b93cb6f73ed7879f96967bc720bc4ab1522d0e2cc5661","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-11-08T06:42:07Z","title_canon_sha256":"cd74d8b40f5a52dfe3162692e98f6706ebbfe82a4a19f4d627feee2f69eb44f7"},"schema_version":"1.0","source":{"id":"1911.03080","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1911.03080","created_at":"2026-07-05T00:17:59Z"},{"alias_kind":"arxiv_version","alias_value":"1911.03080v1","created_at":"2026-07-05T00:17:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.03080","created_at":"2026-07-05T00:17:59Z"},{"alias_kind":"pith_short_12","alias_value":"BJKRLCUICBJ3","created_at":"2026-07-05T00:17:59Z"},{"alias_kind":"pith_short_16","alias_value":"BJKRLCUICBJ3ZHNA","created_at":"2026-07-05T00:17:59Z"},{"alias_kind":"pith_short_8","alias_value":"BJKRLCUI","created_at":"2026-07-05T00:17:59Z"}],"graph_snapshots":[{"event_id":"sha256:252f7936466b59a5bb21431b5bbb6f63a52d0880ffadf2966cca9e4d5b96161e","target":"graph","created_at":"2026-07-05T00:17:59Z","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/1911.03080/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In most cases deep learning architectures are trained disregarding the amount of operations and energy consumption. However, some applications, like embedded systems, can be resource-constrained during inference. A popular approach to reduce the size of a deep learning architecture consists in distilling knowledge from a bigger network (teacher) to a smaller one (student). Directly training the student to mimic the teacher representation can be effective, but it requires that both share the same latent space dimensions. In this work, we focus instead on relative knowledge distillation (RKD), w","authors_text":"Antonio Ortega, Carlos Lassance, Ghouthi Boukli Hacene, Jian Tang, Myriam Bontonou, Vincent Gripon","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-11-08T06:42:07Z","title":"Deep geometric knowledge distillation with graphs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.03080","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:f644e24c236d5b0d8da15159c9938b98a295d91cf1eb1ea0d6166a63b2bbe6e2","target":"record","created_at":"2026-07-05T00:17:59Z","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":"1a5aae140a87bfa3c76b93cb6f73ed7879f96967bc720bc4ab1522d0e2cc5661","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-11-08T06:42:07Z","title_canon_sha256":"cd74d8b40f5a52dfe3162692e98f6706ebbfe82a4a19f4d627feee2f69eb44f7"},"schema_version":"1.0","source":{"id":"1911.03080","kind":"arxiv","version":1}},"canonical_sha256":"0a55158a881053bc9da02c4c527d7a1fe5bbb8e0dbd89a9c7849b298ca353c7b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0a55158a881053bc9da02c4c527d7a1fe5bbb8e0dbd89a9c7849b298ca353c7b","first_computed_at":"2026-07-05T00:17:59.838344Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:17:59.838344Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"VasaepcqhxKU22xQjSQ5bgNsWAUlf50JMlIj/yL+nQNAFimM+COmefpAdzYX37SbdEbJNc8/BJrjOZRHuuqxDg==","signature_status":"signed_v1","signed_at":"2026-07-05T00:17:59.838695Z","signed_message":"canonical_sha256_bytes"},"source_id":"1911.03080","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f644e24c236d5b0d8da15159c9938b98a295d91cf1eb1ea0d6166a63b2bbe6e2","sha256:252f7936466b59a5bb21431b5bbb6f63a52d0880ffadf2966cca9e4d5b96161e"],"state_sha256":"b34f987142ca3d3630e2734d26765c32901a968e03f331164c88778ce207bc3d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LKSzWutlWZX3072J3ZbJ2Cn7N4NU60eENjAQOVL5R0KXHoSPbD+7Cn2TKvjSDJTZQFT7gftNtMJm/oavILTjCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T18:00:14.022170Z","bundle_sha256":"c86fc88871b2f870ea6948f5649a90c1f06da1f2584f93f8fcb33bfb49e75355"}}