{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:357J27BXULKPY66ALHAV45GLOV","short_pith_number":"pith:357J27BX","schema_version":"1.0","canonical_sha256":"df7e9d7c37a2d4fc7bc059c15e74cb75739f66737741514b43c18cdabbde517d","source":{"kind":"arxiv","id":"2507.16881","version":1},"attestation_state":"computed","paper":{"title":"Confidence Optimization for Probabilistic Encoding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Pengjiu Xia, Wenchao Wei, Yidian Huang, Yuwen Tan","submitted_at":"2025-07-22T15:32:27Z","abstract_excerpt":"Probabilistic encoding introduces Gaussian noise into neural networks, enabling a smooth transition from deterministic to uncertain states and enhancing generalization ability. However, the randomness of Gaussian noise distorts point-based distance measurements in classification tasks. To mitigate this issue, we propose a confidence optimization probabilistic encoding (CPE) method that improves distance reliability and enhances representation learning. Specifically, we refine probabilistic encoding with two key strategies: First, we introduce a confidence-aware mechanism to adjust distance cal"},"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":"2507.16881","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-07-22T15:32:27Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"25c2148ff1366c3bb4aebcdbe6763d8fe8da99e290530146168de369773a7486","abstract_canon_sha256":"48f36ab45b695ba07b81ec6c3adb42514074a80aed353aeccf6c28b2d4136ebe"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:41:57.692859Z","signature_b64":"lRBBl0HKCjdm2OfheWqT+c8Gg8zsPXsNdXApWISNHKUovg/c6BXbuaJXX0heTkxc3EEhwHIt8qHsve8NFWI6CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"df7e9d7c37a2d4fc7bc059c15e74cb75739f66737741514b43c18cdabbde517d","last_reissued_at":"2026-07-05T11:41:57.692339Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:41:57.692339Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Confidence Optimization for Probabilistic Encoding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Pengjiu Xia, Wenchao Wei, Yidian Huang, Yuwen Tan","submitted_at":"2025-07-22T15:32:27Z","abstract_excerpt":"Probabilistic encoding introduces Gaussian noise into neural networks, enabling a smooth transition from deterministic to uncertain states and enhancing generalization ability. However, the randomness of Gaussian noise distorts point-based distance measurements in classification tasks. To mitigate this issue, we propose a confidence optimization probabilistic encoding (CPE) method that improves distance reliability and enhances representation learning. Specifically, we refine probabilistic encoding with two key strategies: First, we introduce a confidence-aware mechanism to adjust distance cal"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.16881","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/2507.16881/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":"2507.16881","created_at":"2026-07-05T11:41:57.692400+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.16881v1","created_at":"2026-07-05T11:41:57.692400+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.16881","created_at":"2026-07-05T11:41:57.692400+00:00"},{"alias_kind":"pith_short_12","alias_value":"357J27BXULKP","created_at":"2026-07-05T11:41:57.692400+00:00"},{"alias_kind":"pith_short_16","alias_value":"357J27BXULKPY66A","created_at":"2026-07-05T11:41:57.692400+00:00"},{"alias_kind":"pith_short_8","alias_value":"357J27BX","created_at":"2026-07-05T11:41:57.692400+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/357J27BXULKPY66ALHAV45GLOV","json":"https://pith.science/pith/357J27BXULKPY66ALHAV45GLOV.json","graph_json":"https://pith.science/api/pith-number/357J27BXULKPY66ALHAV45GLOV/graph.json","events_json":"https://pith.science/api/pith-number/357J27BXULKPY66ALHAV45GLOV/events.json","paper":"https://pith.science/paper/357J27BX"},"agent_actions":{"view_html":"https://pith.science/pith/357J27BXULKPY66ALHAV45GLOV","download_json":"https://pith.science/pith/357J27BXULKPY66ALHAV45GLOV.json","view_paper":"https://pith.science/paper/357J27BX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.16881&json=true","fetch_graph":"https://pith.science/api/pith-number/357J27BXULKPY66ALHAV45GLOV/graph.json","fetch_events":"https://pith.science/api/pith-number/357J27BXULKPY66ALHAV45GLOV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/357J27BXULKPY66ALHAV45GLOV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/357J27BXULKPY66ALHAV45GLOV/action/storage_attestation","attest_author":"https://pith.science/pith/357J27BXULKPY66ALHAV45GLOV/action/author_attestation","sign_citation":"https://pith.science/pith/357J27BXULKPY66ALHAV45GLOV/action/citation_signature","submit_replication":"https://pith.science/pith/357J27BXULKPY66ALHAV45GLOV/action/replication_record"}},"created_at":"2026-07-05T11:41:57.692400+00:00","updated_at":"2026-07-05T11:41:57.692400+00:00"}