{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:QKZYOKI7VFTY3LMHPRTWHTB7RR","short_pith_number":"pith:QKZYOKI7","canonical_record":{"source":{"id":"2110.04572","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-09T13:56:48Z","cross_cats_sorted":[],"title_canon_sha256":"f9c84e8f80b4adf68bafb726551069f4da6a2a5971e747065d26f62704f80770","abstract_canon_sha256":"43844e150f06c210fb275fbdd0f247a98d6424fbbd0e865b1544fa9cda889a95"},"schema_version":"1.0"},"canonical_sha256":"82b387291fa9678dad877c6763cc3f8c46f60a3884c2c8dd9cda0b722e6e4041","source":{"kind":"arxiv","id":"2110.04572","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2110.04572","created_at":"2026-07-05T03:21:18Z"},{"alias_kind":"arxiv_version","alias_value":"2110.04572v1","created_at":"2026-07-05T03:21:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.04572","created_at":"2026-07-05T03:21:18Z"},{"alias_kind":"pith_short_12","alias_value":"QKZYOKI7VFTY","created_at":"2026-07-05T03:21:18Z"},{"alias_kind":"pith_short_16","alias_value":"QKZYOKI7VFTY3LMH","created_at":"2026-07-05T03:21:18Z"},{"alias_kind":"pith_short_8","alias_value":"QKZYOKI7","created_at":"2026-07-05T03:21:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:QKZYOKI7VFTY3LMHPRTWHTB7RR","target":"record","payload":{"canonical_record":{"source":{"id":"2110.04572","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-09T13:56:48Z","cross_cats_sorted":[],"title_canon_sha256":"f9c84e8f80b4adf68bafb726551069f4da6a2a5971e747065d26f62704f80770","abstract_canon_sha256":"43844e150f06c210fb275fbdd0f247a98d6424fbbd0e865b1544fa9cda889a95"},"schema_version":"1.0"},"canonical_sha256":"82b387291fa9678dad877c6763cc3f8c46f60a3884c2c8dd9cda0b722e6e4041","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:21:18.556544Z","signature_b64":"3UPhkKFEeeX+sxkFlxGDyQRcol1lHPxTLeuEEHsmSWKLDiZTG5B08db2+4fDMFOZKxFTNFA9kQ9W37axGPUKCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"82b387291fa9678dad877c6763cc3f8c46f60a3884c2c8dd9cda0b722e6e4041","last_reissued_at":"2026-07-05T03:21:18.556110Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:21:18.556110Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2110.04572","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":"2026-07-05T03:21:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NlXQokvXkEG81fTu9E6hnUeiWXc+1TYdvEkMQcZ0+HHFVjoFtCzbYFBViumOm1t0F9JpiG82xMiSVrPR1XPuBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T17:16:40.182678Z"},"content_sha256":"092a5425d5c8271df5cdead5313b014081da825c84386310d56ad4a63165e44b","schema_version":"1.0","event_id":"sha256:092a5425d5c8271df5cdead5313b014081da825c84386310d56ad4a63165e44b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:QKZYOKI7VFTY3LMHPRTWHTB7RR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"X-model: Improving Data Efficiency in Deep Learning with A Minimax Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jianmin Wang, Mingsheng Long, Ximei Wang, Xinyang Chen","submitted_at":"2021-10-09T13:56:48Z","abstract_excerpt":"To mitigate the burden of data labeling, we aim at improving data efficiency for both classification and regression setups in deep learning. However, the current focus is on classification problems while rare attention has been paid to deep regression, which usually requires more human effort to labeling. Further, due to the intrinsic difference between categorical and continuous label space, the common intuitions for classification, e.g., cluster assumptions or pseudo labeling strategies, cannot be naturally adapted into deep regression. To this end, we first delved into the existing data-eff"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.04572","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/2110.04572/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":"2026-07-05T03:21:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yVBZk86g7bQ+i0GJ4S4lci0MCLqbCCxr4aAvGanjbZPgciSlu9VxV8epEbxvxgBN3Rd9Y7taZXlXfxtyVpxZAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T17:16:40.183852Z"},"content_sha256":"c5c74a0e18ce7864aa978ec17dcf953d4c6b075d401a0407ed69614e32eb6032","schema_version":"1.0","event_id":"sha256:c5c74a0e18ce7864aa978ec17dcf953d4c6b075d401a0407ed69614e32eb6032"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QKZYOKI7VFTY3LMHPRTWHTB7RR/bundle.json","state_url":"https://pith.science/pith/QKZYOKI7VFTY3LMHPRTWHTB7RR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QKZYOKI7VFTY3LMHPRTWHTB7RR/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":"2026-08-05T17:16:40Z","links":{"resolver":"https://pith.science/pith/QKZYOKI7VFTY3LMHPRTWHTB7RR","bundle":"https://pith.science/pith/QKZYOKI7VFTY3LMHPRTWHTB7RR/bundle.json","state":"https://pith.science/pith/QKZYOKI7VFTY3LMHPRTWHTB7RR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QKZYOKI7VFTY3LMHPRTWHTB7RR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:QKZYOKI7VFTY3LMHPRTWHTB7RR","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":"43844e150f06c210fb275fbdd0f247a98d6424fbbd0e865b1544fa9cda889a95","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-09T13:56:48Z","title_canon_sha256":"f9c84e8f80b4adf68bafb726551069f4da6a2a5971e747065d26f62704f80770"},"schema_version":"1.0","source":{"id":"2110.04572","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2110.04572","created_at":"2026-07-05T03:21:18Z"},{"alias_kind":"arxiv_version","alias_value":"2110.04572v1","created_at":"2026-07-05T03:21:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.04572","created_at":"2026-07-05T03:21:18Z"},{"alias_kind":"pith_short_12","alias_value":"QKZYOKI7VFTY","created_at":"2026-07-05T03:21:18Z"},{"alias_kind":"pith_short_16","alias_value":"QKZYOKI7VFTY3LMH","created_at":"2026-07-05T03:21:18Z"},{"alias_kind":"pith_short_8","alias_value":"QKZYOKI7","created_at":"2026-07-05T03:21:18Z"}],"graph_snapshots":[{"event_id":"sha256:c5c74a0e18ce7864aa978ec17dcf953d4c6b075d401a0407ed69614e32eb6032","target":"graph","created_at":"2026-07-05T03:21:18Z","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/2110.04572/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"To mitigate the burden of data labeling, we aim at improving data efficiency for both classification and regression setups in deep learning. However, the current focus is on classification problems while rare attention has been paid to deep regression, which usually requires more human effort to labeling. Further, due to the intrinsic difference between categorical and continuous label space, the common intuitions for classification, e.g., cluster assumptions or pseudo labeling strategies, cannot be naturally adapted into deep regression. To this end, we first delved into the existing data-eff","authors_text":"Jianmin Wang, Mingsheng Long, Ximei Wang, Xinyang Chen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-09T13:56:48Z","title":"X-model: Improving Data Efficiency in Deep Learning with A Minimax Model"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.04572","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":"sha256:092a5425d5c8271df5cdead5313b014081da825c84386310d56ad4a63165e44b","target":"record","created_at":"2026-07-05T03:21:18Z","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":"43844e150f06c210fb275fbdd0f247a98d6424fbbd0e865b1544fa9cda889a95","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-09T13:56:48Z","title_canon_sha256":"f9c84e8f80b4adf68bafb726551069f4da6a2a5971e747065d26f62704f80770"},"schema_version":"1.0","source":{"id":"2110.04572","kind":"arxiv","version":1}},"canonical_sha256":"82b387291fa9678dad877c6763cc3f8c46f60a3884c2c8dd9cda0b722e6e4041","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"82b387291fa9678dad877c6763cc3f8c46f60a3884c2c8dd9cda0b722e6e4041","first_computed_at":"2026-07-05T03:21:18.556110Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:21:18.556110Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3UPhkKFEeeX+sxkFlxGDyQRcol1lHPxTLeuEEHsmSWKLDiZTG5B08db2+4fDMFOZKxFTNFA9kQ9W37axGPUKCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T03:21:18.556544Z","signed_message":"canonical_sha256_bytes"},"source_id":"2110.04572","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:092a5425d5c8271df5cdead5313b014081da825c84386310d56ad4a63165e44b","sha256:c5c74a0e18ce7864aa978ec17dcf953d4c6b075d401a0407ed69614e32eb6032"],"state_sha256":"08fc3095f304d84fa211ce1b9a23ba37f10187c07d578403126724ef05bd21fa"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9VSi6yp0tekmTc06ujU8oA7e6BrucQXqMWq4dpnLr3L0tjoNhW+oeHT0Ojvx9Hcw2Izy/8x7RaW6H5VnQXYeDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T17:16:40.193725Z","bundle_sha256":"1a55ee3a7bc089786229e9cebac5fa21381d713db18b4ffd8b06e089cf6ce1b8"}}