{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:3LS6ITNUY6O7NTWS2UK4OPUWHP","short_pith_number":"pith:3LS6ITNU","schema_version":"1.0","canonical_sha256":"dae5e44db4c79df6ced2d515c73e963bfb35b822df5c421eb78c8e9acdea8aae","source":{"kind":"arxiv","id":"2410.05578","version":1},"attestation_state":"computed","paper":{"title":"Swift Sampler: Efficient Learning of Sampler by 10 Parameters","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Canran Xiao, Chuming Li, Jiawei Yao","submitted_at":"2024-10-08T00:26:29Z","abstract_excerpt":"Data selection is essential for training deep learning models. An effective data sampler assigns proper sampling probability for training data and helps the model converge to a good local minimum with high performance. Previous studies in data sampling are mainly based on heuristic rules or learning through a huge amount of time-consuming trials. In this paper, we propose an automatic \\textbf{swift sampler} search algorithm, \\textbf{SS}, to explore automatically learning effective samplers efficiently. In particular, \\textbf{SS} utilizes a novel formulation to map a sampler to a low dimension "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2410.05578","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-08T00:26:29Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"051fdc435ac4e80ef4d24f17ec4297f6140a17e1c190b8dccff46744ab3f9cd1","abstract_canon_sha256":"9dc5f7ec5bf1b2dcaa806aa4a6c30de59930cde3d91a76b6a5583716a0ae3948"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:17:21.214510Z","signature_b64":"AuUnc/SJtbx6aGFKDlqnY61alvLk5rrXP9+ZYkUdQUocW0q+nLbHSrOvAjvW2yJ7h5NOxIUpU4vn5g0DUN1UCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dae5e44db4c79df6ced2d515c73e963bfb35b822df5c421eb78c8e9acdea8aae","last_reissued_at":"2026-07-05T09:17:21.214029Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:17:21.214029Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Swift Sampler: Efficient Learning of Sampler by 10 Parameters","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Canran Xiao, Chuming Li, Jiawei Yao","submitted_at":"2024-10-08T00:26:29Z","abstract_excerpt":"Data selection is essential for training deep learning models. An effective data sampler assigns proper sampling probability for training data and helps the model converge to a good local minimum with high performance. Previous studies in data sampling are mainly based on heuristic rules or learning through a huge amount of time-consuming trials. In this paper, we propose an automatic \\textbf{swift sampler} search algorithm, \\textbf{SS}, to explore automatically learning effective samplers efficiently. In particular, \\textbf{SS} utilizes a novel formulation to map a sampler to a low dimension "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.05578","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/2410.05578/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2410.05578","created_at":"2026-07-05T09:17:21.214087+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.05578v1","created_at":"2026-07-05T09:17:21.214087+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.05578","created_at":"2026-07-05T09:17:21.214087+00:00"},{"alias_kind":"pith_short_12","alias_value":"3LS6ITNUY6O7","created_at":"2026-07-05T09:17:21.214087+00:00"},{"alias_kind":"pith_short_16","alias_value":"3LS6ITNUY6O7NTWS","created_at":"2026-07-05T09:17:21.214087+00:00"},{"alias_kind":"pith_short_8","alias_value":"3LS6ITNU","created_at":"2026-07-05T09:17:21.214087+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.22394","citing_title":"Curvature-Adaptive Consistency Flow Matching: Autonomous Trajectory Optimization via Reinforcement Learning","ref_index":65,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3LS6ITNUY6O7NTWS2UK4OPUWHP","json":"https://pith.science/pith/3LS6ITNUY6O7NTWS2UK4OPUWHP.json","graph_json":"https://pith.science/api/pith-number/3LS6ITNUY6O7NTWS2UK4OPUWHP/graph.json","events_json":"https://pith.science/api/pith-number/3LS6ITNUY6O7NTWS2UK4OPUWHP/events.json","paper":"https://pith.science/paper/3LS6ITNU"},"agent_actions":{"view_html":"https://pith.science/pith/3LS6ITNUY6O7NTWS2UK4OPUWHP","download_json":"https://pith.science/pith/3LS6ITNUY6O7NTWS2UK4OPUWHP.json","view_paper":"https://pith.science/paper/3LS6ITNU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.05578&json=true","fetch_graph":"https://pith.science/api/pith-number/3LS6ITNUY6O7NTWS2UK4OPUWHP/graph.json","fetch_events":"https://pith.science/api/pith-number/3LS6ITNUY6O7NTWS2UK4OPUWHP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3LS6ITNUY6O7NTWS2UK4OPUWHP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3LS6ITNUY6O7NTWS2UK4OPUWHP/action/storage_attestation","attest_author":"https://pith.science/pith/3LS6ITNUY6O7NTWS2UK4OPUWHP/action/author_attestation","sign_citation":"https://pith.science/pith/3LS6ITNUY6O7NTWS2UK4OPUWHP/action/citation_signature","submit_replication":"https://pith.science/pith/3LS6ITNUY6O7NTWS2UK4OPUWHP/action/replication_record"}},"created_at":"2026-07-05T09:17:21.214087+00:00","updated_at":"2026-07-05T09:17:21.214087+00:00"}