{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:U3I6R7OLJLYALTGV44KIHP7RXG","short_pith_number":"pith:U3I6R7OL","canonical_record":{"source":{"id":"2105.01875","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-05-05T05:44:15Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b565b05c91501921767d3fd59001bc3f33bc83f3296a41183e082a076bf92d4e","abstract_canon_sha256":"16d075fc825c6b5b10fe7162c399c7b79951db2d4520bef7c8c4c3803019f8e0"},"schema_version":"1.0"},"canonical_sha256":"a6d1e8fdcb4af005ccd5e71483bff1b9866334937c1b1759219e8b65387732a2","source":{"kind":"arxiv","id":"2105.01875","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2105.01875","created_at":"2026-07-05T02:37:49Z"},{"alias_kind":"arxiv_version","alias_value":"2105.01875v1","created_at":"2026-07-05T02:37:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.01875","created_at":"2026-07-05T02:37:49Z"},{"alias_kind":"pith_short_12","alias_value":"U3I6R7OLJLYA","created_at":"2026-07-05T02:37:49Z"},{"alias_kind":"pith_short_16","alias_value":"U3I6R7OLJLYALTGV","created_at":"2026-07-05T02:37:49Z"},{"alias_kind":"pith_short_8","alias_value":"U3I6R7OL","created_at":"2026-07-05T02:37:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:U3I6R7OLJLYALTGV44KIHP7RXG","target":"record","payload":{"canonical_record":{"source":{"id":"2105.01875","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-05-05T05:44:15Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b565b05c91501921767d3fd59001bc3f33bc83f3296a41183e082a076bf92d4e","abstract_canon_sha256":"16d075fc825c6b5b10fe7162c399c7b79951db2d4520bef7c8c4c3803019f8e0"},"schema_version":"1.0"},"canonical_sha256":"a6d1e8fdcb4af005ccd5e71483bff1b9866334937c1b1759219e8b65387732a2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:37:49.650180Z","signature_b64":"/cJwWylbXPDGIEHYi17+873pW+C5WPdkVyFRZzynHKqRKnUjNzFWxYkeSJNSEvHv1TR9+HXQiMdSdN3GdOJZBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a6d1e8fdcb4af005ccd5e71483bff1b9866334937c1b1759219e8b65387732a2","last_reissued_at":"2026-07-05T02:37:49.649693Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:37:49.649693Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2105.01875","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":"2026-07-05T02:37:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Wo//UivW6IeniE9MrcsYh8mG4QDkjMG74dzrKMjYhRDatEkfFeRZHGBxzae82+7/Lxw5No5CzdP2o0tUg2hjCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T11:36:38.073768Z"},"content_sha256":"e99570baab2300b55e0cc8b57a9f93d9ed8f32f9ac385da26e523cc6c361e9e0","schema_version":"1.0","event_id":"sha256:e99570baab2300b55e0cc8b57a9f93d9ed8f32f9ac385da26e523cc6c361e9e0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:U3I6R7OLJLYALTGV44KIHP7RXG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Modulating Regularization Frequency for Efficient Compression-Aware Model Training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Baeseong Park, Byeongwook Kim, Dongsoo Lee, Jeongin Yun, Se Jung Kwon, Yongkweon Jeon","submitted_at":"2021-05-05T05:44:15Z","abstract_excerpt":"While model compression is increasingly important because of large neural network size, compression-aware training is challenging as it needs sophisticated model modifications and longer training time.In this paper, we introduce regularization frequency (i.e., how often compression is performed during training) as a new regularization technique for a practical and efficient compression-aware training method. For various regularization techniques, such as weight decay and dropout, optimizing the regularization strength is crucial to improve generalization in Deep Neural Networks (DNNs). While m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.01875","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/2105.01875/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":"2026-07-05T02:37:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"c1/y+tdzyirpjg1XEV/EsWTtP8xyTiFDYMGDhW9tDFqA4dNwALP/Tj3QPIvS0sgYy+lk7aq2DFDAbj8UoEMRBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T11:36:38.074146Z"},"content_sha256":"2f723c5aac8c95890b5139f1a3a389f67767387e7c20b9e598d64e2d0227da00","schema_version":"1.0","event_id":"sha256:2f723c5aac8c95890b5139f1a3a389f67767387e7c20b9e598d64e2d0227da00"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/U3I6R7OLJLYALTGV44KIHP7RXG/bundle.json","state_url":"https://pith.science/pith/U3I6R7OLJLYALTGV44KIHP7RXG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/U3I6R7OLJLYALTGV44KIHP7RXG/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":"2026-07-07T11:36:38Z","links":{"resolver":"https://pith.science/pith/U3I6R7OLJLYALTGV44KIHP7RXG","bundle":"https://pith.science/pith/U3I6R7OLJLYALTGV44KIHP7RXG/bundle.json","state":"https://pith.science/pith/U3I6R7OLJLYALTGV44KIHP7RXG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/U3I6R7OLJLYALTGV44KIHP7RXG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:U3I6R7OLJLYALTGV44KIHP7RXG","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":"16d075fc825c6b5b10fe7162c399c7b79951db2d4520bef7c8c4c3803019f8e0","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-05-05T05:44:15Z","title_canon_sha256":"b565b05c91501921767d3fd59001bc3f33bc83f3296a41183e082a076bf92d4e"},"schema_version":"1.0","source":{"id":"2105.01875","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2105.01875","created_at":"2026-07-05T02:37:49Z"},{"alias_kind":"arxiv_version","alias_value":"2105.01875v1","created_at":"2026-07-05T02:37:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.01875","created_at":"2026-07-05T02:37:49Z"},{"alias_kind":"pith_short_12","alias_value":"U3I6R7OLJLYA","created_at":"2026-07-05T02:37:49Z"},{"alias_kind":"pith_short_16","alias_value":"U3I6R7OLJLYALTGV","created_at":"2026-07-05T02:37:49Z"},{"alias_kind":"pith_short_8","alias_value":"U3I6R7OL","created_at":"2026-07-05T02:37:49Z"}],"graph_snapshots":[{"event_id":"sha256:2f723c5aac8c95890b5139f1a3a389f67767387e7c20b9e598d64e2d0227da00","target":"graph","created_at":"2026-07-05T02:37:49Z","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/2105.01875/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"While model compression is increasingly important because of large neural network size, compression-aware training is challenging as it needs sophisticated model modifications and longer training time.In this paper, we introduce regularization frequency (i.e., how often compression is performed during training) as a new regularization technique for a practical and efficient compression-aware training method. For various regularization techniques, such as weight decay and dropout, optimizing the regularization strength is crucial to improve generalization in Deep Neural Networks (DNNs). While m","authors_text":"Baeseong Park, Byeongwook Kim, Dongsoo Lee, Jeongin Yun, Se Jung Kwon, Yongkweon Jeon","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-05-05T05:44:15Z","title":"Modulating Regularization Frequency for Efficient Compression-Aware Model Training"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.01875","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:e99570baab2300b55e0cc8b57a9f93d9ed8f32f9ac385da26e523cc6c361e9e0","target":"record","created_at":"2026-07-05T02:37:49Z","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":"16d075fc825c6b5b10fe7162c399c7b79951db2d4520bef7c8c4c3803019f8e0","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-05-05T05:44:15Z","title_canon_sha256":"b565b05c91501921767d3fd59001bc3f33bc83f3296a41183e082a076bf92d4e"},"schema_version":"1.0","source":{"id":"2105.01875","kind":"arxiv","version":1}},"canonical_sha256":"a6d1e8fdcb4af005ccd5e71483bff1b9866334937c1b1759219e8b65387732a2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a6d1e8fdcb4af005ccd5e71483bff1b9866334937c1b1759219e8b65387732a2","first_computed_at":"2026-07-05T02:37:49.649693Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:37:49.649693Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/cJwWylbXPDGIEHYi17+873pW+C5WPdkVyFRZzynHKqRKnUjNzFWxYkeSJNSEvHv1TR9+HXQiMdSdN3GdOJZBg==","signature_status":"signed_v1","signed_at":"2026-07-05T02:37:49.650180Z","signed_message":"canonical_sha256_bytes"},"source_id":"2105.01875","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e99570baab2300b55e0cc8b57a9f93d9ed8f32f9ac385da26e523cc6c361e9e0","sha256:2f723c5aac8c95890b5139f1a3a389f67767387e7c20b9e598d64e2d0227da00"],"state_sha256":"86a20071ba3995e7ede34944def3faf069deb076889a6ca94f38b34b71708055"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"d9WOnnjd6NEK18qQQKem/eW84ThPpQocnZ8D+K89QjJzO1j7qEpk9J+Y3x311c37FlLCSmpShBjmQEJejuoDBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T11:36:38.076131Z","bundle_sha256":"42ad81f165e3b0082909f70e4a58f76de967ad6455bd72f4356ca2849235b3c8"}}