{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:RU57NQVIB52BMO6SM5YLNH7XBH","short_pith_number":"pith:RU57NQVI","canonical_record":{"source":{"id":"2311.15134","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-11-25T22:51:01Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"8990c5e5d54da3d5df7fce835774c8b1b718da932741746a68b53ce4476a1b06","abstract_canon_sha256":"e840fcce845597793129366c87225edd7e61c45b23e6cd5a771dc74fce718852"},"schema_version":"1.0"},"canonical_sha256":"8d3bf6c2a80f74163bd26770b69ff709f2e6ad503f641c6c388909646aafda9e","source":{"kind":"arxiv","id":"2311.15134","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.15134","created_at":"2026-07-05T07:16:59Z"},{"alias_kind":"arxiv_version","alias_value":"2311.15134v1","created_at":"2026-07-05T07:16:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.15134","created_at":"2026-07-05T07:16:59Z"},{"alias_kind":"pith_short_12","alias_value":"RU57NQVIB52B","created_at":"2026-07-05T07:16:59Z"},{"alias_kind":"pith_short_16","alias_value":"RU57NQVIB52BMO6S","created_at":"2026-07-05T07:16:59Z"},{"alias_kind":"pith_short_8","alias_value":"RU57NQVI","created_at":"2026-07-05T07:16:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:RU57NQVIB52BMO6SM5YLNH7XBH","target":"record","payload":{"canonical_record":{"source":{"id":"2311.15134","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-11-25T22:51:01Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"8990c5e5d54da3d5df7fce835774c8b1b718da932741746a68b53ce4476a1b06","abstract_canon_sha256":"e840fcce845597793129366c87225edd7e61c45b23e6cd5a771dc74fce718852"},"schema_version":"1.0"},"canonical_sha256":"8d3bf6c2a80f74163bd26770b69ff709f2e6ad503f641c6c388909646aafda9e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:16:59.496985Z","signature_b64":"AE/n401or8QBtrOi/SY9ylDmt/AZmgHErHW3U1TnTRGYSTJQXvSKJ7+1Dr044hHgUQAGIUgq4U87eah4hoA9Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8d3bf6c2a80f74163bd26770b69ff709f2e6ad503f641c6c388909646aafda9e","last_reissued_at":"2026-07-05T07:16:59.496485Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:16:59.496485Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2311.15134","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":"2026-07-05T07:16:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3c7TFs3R8ArduxvDNmpHXJKgxChOFjpheYM/zDUJShtGNeYsdBt1D+705PP+O1khxUEO+oWWu/yd4Quk6XPuDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T10:22:43.523737Z"},"content_sha256":"fbba42edd2bb554982634c44f15754816d963d75a125a34ad1096ec4080a1321","schema_version":"1.0","event_id":"sha256:fbba42edd2bb554982634c44f15754816d963d75a125a34ad1096ec4080a1321"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:RU57NQVIB52BMO6SM5YLNH7XBH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SwiftLearn: A Data-Efficient Training Method of Deep Learning Models using Importance Sampling","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Austin Wen, Farnoosh Javadi, Foozhan Ataiefard, Habib Hajimolahoseini, Kangling Liu, Mehdi Ahmadi, Mohammad Hassanpour, Omar Mohamed Awad, Saina Asani, Walid Ahmed, Yang Liu","submitted_at":"2023-11-25T22:51:01Z","abstract_excerpt":"In this paper, we present SwiftLearn, a data-efficient approach to accelerate training of deep learning models using a subset of data samples selected during the warm-up stages of training. This subset is selected based on an importance criteria measured over the entire dataset during warm-up stages, aiming to preserve the model performance with fewer examples during the rest of training. The importance measure we propose could be updated during training every once in a while, to make sure that all of the data samples have a chance to return to the training loop if they show a higher importanc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.15134","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/2311.15134/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":"2026-07-05T07:16:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bJk54purSm69C1LKq42s+cb2AmRa3WBdk5ZDQSasB1eRcdWDlEu0uSFEaiz2NCQUb4FOTmTXJqXfHKq1yvPSAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T10:22:43.524118Z"},"content_sha256":"a58280ab013df2f1cd80f871fa770a343df727a0caae7fea38d9d73e1db30189","schema_version":"1.0","event_id":"sha256:a58280ab013df2f1cd80f871fa770a343df727a0caae7fea38d9d73e1db30189"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RU57NQVIB52BMO6SM5YLNH7XBH/bundle.json","state_url":"https://pith.science/pith/RU57NQVIB52BMO6SM5YLNH7XBH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RU57NQVIB52BMO6SM5YLNH7XBH/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":"2026-08-20T10:22:43Z","links":{"resolver":"https://pith.science/pith/RU57NQVIB52BMO6SM5YLNH7XBH","bundle":"https://pith.science/pith/RU57NQVIB52BMO6SM5YLNH7XBH/bundle.json","state":"https://pith.science/pith/RU57NQVIB52BMO6SM5YLNH7XBH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RU57NQVIB52BMO6SM5YLNH7XBH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:RU57NQVIB52BMO6SM5YLNH7XBH","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":"e840fcce845597793129366c87225edd7e61c45b23e6cd5a771dc74fce718852","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-11-25T22:51:01Z","title_canon_sha256":"8990c5e5d54da3d5df7fce835774c8b1b718da932741746a68b53ce4476a1b06"},"schema_version":"1.0","source":{"id":"2311.15134","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.15134","created_at":"2026-07-05T07:16:59Z"},{"alias_kind":"arxiv_version","alias_value":"2311.15134v1","created_at":"2026-07-05T07:16:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.15134","created_at":"2026-07-05T07:16:59Z"},{"alias_kind":"pith_short_12","alias_value":"RU57NQVIB52B","created_at":"2026-07-05T07:16:59Z"},{"alias_kind":"pith_short_16","alias_value":"RU57NQVIB52BMO6S","created_at":"2026-07-05T07:16:59Z"},{"alias_kind":"pith_short_8","alias_value":"RU57NQVI","created_at":"2026-07-05T07:16:59Z"}],"graph_snapshots":[{"event_id":"sha256:a58280ab013df2f1cd80f871fa770a343df727a0caae7fea38d9d73e1db30189","target":"graph","created_at":"2026-07-05T07:16:59Z","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/2311.15134/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, we present SwiftLearn, a data-efficient approach to accelerate training of deep learning models using a subset of data samples selected during the warm-up stages of training. This subset is selected based on an importance criteria measured over the entire dataset during warm-up stages, aiming to preserve the model performance with fewer examples during the rest of training. The importance measure we propose could be updated during training every once in a while, to make sure that all of the data samples have a chance to return to the training loop if they show a higher importanc","authors_text":"Austin Wen, Farnoosh Javadi, Foozhan Ataiefard, Habib Hajimolahoseini, Kangling Liu, Mehdi Ahmadi, Mohammad Hassanpour, Omar Mohamed Awad, Saina Asani, Walid Ahmed, Yang Liu","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-11-25T22:51:01Z","title":"SwiftLearn: A Data-Efficient Training Method of Deep Learning Models using Importance Sampling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.15134","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:fbba42edd2bb554982634c44f15754816d963d75a125a34ad1096ec4080a1321","target":"record","created_at":"2026-07-05T07:16:59Z","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":"e840fcce845597793129366c87225edd7e61c45b23e6cd5a771dc74fce718852","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-11-25T22:51:01Z","title_canon_sha256":"8990c5e5d54da3d5df7fce835774c8b1b718da932741746a68b53ce4476a1b06"},"schema_version":"1.0","source":{"id":"2311.15134","kind":"arxiv","version":1}},"canonical_sha256":"8d3bf6c2a80f74163bd26770b69ff709f2e6ad503f641c6c388909646aafda9e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8d3bf6c2a80f74163bd26770b69ff709f2e6ad503f641c6c388909646aafda9e","first_computed_at":"2026-07-05T07:16:59.496485Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:16:59.496485Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AE/n401or8QBtrOi/SY9ylDmt/AZmgHErHW3U1TnTRGYSTJQXvSKJ7+1Dr044hHgUQAGIUgq4U87eah4hoA9Aw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:16:59.496985Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.15134","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fbba42edd2bb554982634c44f15754816d963d75a125a34ad1096ec4080a1321","sha256:a58280ab013df2f1cd80f871fa770a343df727a0caae7fea38d9d73e1db30189"],"state_sha256":"0d46054f353b7fb877d8bd0d957bb7d4166beb0b7fd800adbb2f350f20e945e4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Cy0Oa6H6icjWuASasGDlcV6WzqJhfnYnvWZGto0NbZH2baRDrl5f+LwqU24vMIQWxw92YtOH+NsMA/I6lwy6Cg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T10:22:43.526457Z","bundle_sha256":"7c16a1dca4911efd44db79a00cb68e6ff27c0c89513c4e5f8b1f32b5d59671c0"}}