{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:RMDNQ5H4HJ7YMTTMK7N623NRCU","short_pith_number":"pith:RMDNQ5H4","canonical_record":{"source":{"id":"2306.05175","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-08T13:14:35Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"f263948da5206ebf654f7026cb65cb815cb6e1826910f8ca8253609855fc81e5","abstract_canon_sha256":"9c5a74b738b627f52ca23b1328cea2bc2612c6ac4dd130546411fa7b7921f17a"},"schema_version":"1.0"},"canonical_sha256":"8b06d874fc3a7f864e6c57dbed6db11506c7cbf5a1922aedd81cb80bb823c784","source":{"kind":"arxiv","id":"2306.05175","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.05175","created_at":"2026-07-05T08:31:40Z"},{"alias_kind":"arxiv_version","alias_value":"2306.05175v3","created_at":"2026-07-05T08:31:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.05175","created_at":"2026-07-05T08:31:40Z"},{"alias_kind":"pith_short_12","alias_value":"RMDNQ5H4HJ7Y","created_at":"2026-07-05T08:31:40Z"},{"alias_kind":"pith_short_16","alias_value":"RMDNQ5H4HJ7YMTTM","created_at":"2026-07-05T08:31:40Z"},{"alias_kind":"pith_short_8","alias_value":"RMDNQ5H4","created_at":"2026-07-05T08:31:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:RMDNQ5H4HJ7YMTTMK7N623NRCU","target":"record","payload":{"canonical_record":{"source":{"id":"2306.05175","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-08T13:14:35Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"f263948da5206ebf654f7026cb65cb815cb6e1826910f8ca8253609855fc81e5","abstract_canon_sha256":"9c5a74b738b627f52ca23b1328cea2bc2612c6ac4dd130546411fa7b7921f17a"},"schema_version":"1.0"},"canonical_sha256":"8b06d874fc3a7f864e6c57dbed6db11506c7cbf5a1922aedd81cb80bb823c784","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:31:40.167372Z","signature_b64":"kcYM7b+gnOHyRr46i9vftJ6kCokFXsDGnFu/JydadmuJCIvVP7yDwHu2pXDeX5lAavW+PJameCJWcIhGrVDNCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8b06d874fc3a7f864e6c57dbed6db11506c7cbf5a1922aedd81cb80bb823c784","last_reissued_at":"2026-07-05T08:31:40.166744Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:31:40.166744Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2306.05175","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:31:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"O1vWphH+sKkh5C28LPywf4RsPdZ2ZNkAO4JJm0a95cX/JDEEGytCMWkwssnTbYIUA5qGGF0XBeGc7fuxfudXCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T19:37:37.284106Z"},"content_sha256":"2f03c5eea37d8e710cef9d0e7bd326ee35306bc356b33634a802d62cdd61002d","schema_version":"1.0","event_id":"sha256:2f03c5eea37d8e710cef9d0e7bd326ee35306bc356b33634a802d62cdd61002d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:RMDNQ5H4HJ7YMTTMK7N623NRCU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Large-scale Dataset Pruning with Dynamic Uncertainty","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Bo Zhao, Muyang He, Shuo Yang, Tiejun Huang","submitted_at":"2023-06-08T13:14:35Z","abstract_excerpt":"The state of the art of many learning tasks, e.g., image classification, is advanced by collecting larger datasets and then training larger models on them. As the outcome, the increasing computational cost is becoming unaffordable. In this paper, we investigate how to prune the large-scale datasets, and thus produce an informative subset for training sophisticated deep models with negligible performance drop. We propose a simple yet effective dataset pruning method by exploring both the prediction uncertainty and training dynamics. We study dataset pruning by measuring the variation of predict"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.05175","kind":"arxiv","version":3},"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/2306.05175/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":"2026-07-05T08:31:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"137xeiKMZR9KjLVlqjjmEwjuN5yXgI/l/NfS3mWSp1Kq9JEZIOyOnE5j2q2xPP/7WXdt77rYIVAkAOb1zwM7Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T19:37:37.285018Z"},"content_sha256":"e25cd15a9ec6243026a1d0bea41810766c40f328b9b90c4aaf2f41098f7545df","schema_version":"1.0","event_id":"sha256:e25cd15a9ec6243026a1d0bea41810766c40f328b9b90c4aaf2f41098f7545df"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RMDNQ5H4HJ7YMTTMK7N623NRCU/bundle.json","state_url":"https://pith.science/pith/RMDNQ5H4HJ7YMTTMK7N623NRCU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RMDNQ5H4HJ7YMTTMK7N623NRCU/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":"2026-08-06T19:37:37Z","links":{"resolver":"https://pith.science/pith/RMDNQ5H4HJ7YMTTMK7N623NRCU","bundle":"https://pith.science/pith/RMDNQ5H4HJ7YMTTMK7N623NRCU/bundle.json","state":"https://pith.science/pith/RMDNQ5H4HJ7YMTTMK7N623NRCU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RMDNQ5H4HJ7YMTTMK7N623NRCU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:RMDNQ5H4HJ7YMTTMK7N623NRCU","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":"9c5a74b738b627f52ca23b1328cea2bc2612c6ac4dd130546411fa7b7921f17a","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-08T13:14:35Z","title_canon_sha256":"f263948da5206ebf654f7026cb65cb815cb6e1826910f8ca8253609855fc81e5"},"schema_version":"1.0","source":{"id":"2306.05175","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.05175","created_at":"2026-07-05T08:31:40Z"},{"alias_kind":"arxiv_version","alias_value":"2306.05175v3","created_at":"2026-07-05T08:31:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.05175","created_at":"2026-07-05T08:31:40Z"},{"alias_kind":"pith_short_12","alias_value":"RMDNQ5H4HJ7Y","created_at":"2026-07-05T08:31:40Z"},{"alias_kind":"pith_short_16","alias_value":"RMDNQ5H4HJ7YMTTM","created_at":"2026-07-05T08:31:40Z"},{"alias_kind":"pith_short_8","alias_value":"RMDNQ5H4","created_at":"2026-07-05T08:31:40Z"}],"graph_snapshots":[{"event_id":"sha256:e25cd15a9ec6243026a1d0bea41810766c40f328b9b90c4aaf2f41098f7545df","target":"graph","created_at":"2026-07-05T08:31:40Z","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/2306.05175/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The state of the art of many learning tasks, e.g., image classification, is advanced by collecting larger datasets and then training larger models on them. As the outcome, the increasing computational cost is becoming unaffordable. In this paper, we investigate how to prune the large-scale datasets, and thus produce an informative subset for training sophisticated deep models with negligible performance drop. We propose a simple yet effective dataset pruning method by exploring both the prediction uncertainty and training dynamics. We study dataset pruning by measuring the variation of predict","authors_text":"Bo Zhao, Muyang He, Shuo Yang, Tiejun Huang","cross_cats":["cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-08T13:14:35Z","title":"Large-scale Dataset Pruning with Dynamic Uncertainty"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.05175","kind":"arxiv","version":3},"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":"sha256:2f03c5eea37d8e710cef9d0e7bd326ee35306bc356b33634a802d62cdd61002d","target":"record","created_at":"2026-07-05T08:31:40Z","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":"9c5a74b738b627f52ca23b1328cea2bc2612c6ac4dd130546411fa7b7921f17a","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-08T13:14:35Z","title_canon_sha256":"f263948da5206ebf654f7026cb65cb815cb6e1826910f8ca8253609855fc81e5"},"schema_version":"1.0","source":{"id":"2306.05175","kind":"arxiv","version":3}},"canonical_sha256":"8b06d874fc3a7f864e6c57dbed6db11506c7cbf5a1922aedd81cb80bb823c784","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8b06d874fc3a7f864e6c57dbed6db11506c7cbf5a1922aedd81cb80bb823c784","first_computed_at":"2026-07-05T08:31:40.166744Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:31:40.166744Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kcYM7b+gnOHyRr46i9vftJ6kCokFXsDGnFu/JydadmuJCIvVP7yDwHu2pXDeX5lAavW+PJameCJWcIhGrVDNCA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:31:40.167372Z","signed_message":"canonical_sha256_bytes"},"source_id":"2306.05175","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2f03c5eea37d8e710cef9d0e7bd326ee35306bc356b33634a802d62cdd61002d","sha256:e25cd15a9ec6243026a1d0bea41810766c40f328b9b90c4aaf2f41098f7545df"],"state_sha256":"6c6a36512a06a0e8a432c55b501c4fa7032930d0c833aaf3ed5decb4e23d0e6f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7pl4m7PMNcVkJtc1OgQCLWDy9EuVGhtHkISrqh0qgjUFzcg1wvORQSHCFuT1GyWSuuwORdKnnYJH+zQXJ2qZCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T19:37:37.290714Z","bundle_sha256":"26e49579996fa5eb28518ecbb6d31948ca453b3914d2d1149bf7461ad048ca01"}}