{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:SS6T5OVPUDTGOW7CCUMAO2WFUO","short_pith_number":"pith:SS6T5OVP","schema_version":"1.0","canonical_sha256":"94bd3ebaafa0e6675be21518076ac5a38906cf3ba4b941e27fa6a9b7df0b5037","source":{"kind":"arxiv","id":"2412.00278","version":1},"attestation_state":"computed","paper":{"title":"Average-Over-Time Spiking Neural Networks for Uncertainty Estimation in Regression","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.NE"],"primary_cat":"cs.LG","authors_text":"Sander Boht\\'e, Tao Sun","submitted_at":"2024-11-29T23:13:52Z","abstract_excerpt":"Uncertainty estimation is a standard tool to quantify the reliability of modern deep learning models, and crucial for many real-world applications. However, efficient uncertainty estimation methods for spiking neural networks, particularly for regression models, have been lacking. Here, we introduce two methods that adapt the Average-Over-Time Spiking Neural Network (AOT-SNN) framework to regression tasks, enhancing uncertainty estimation in event-driven models. The first method uses the heteroscedastic Gaussian approach, where SNNs predict both the mean and variance at each time step, thereby"},"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":"2412.00278","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-29T23:13:52Z","cross_cats_sorted":["cs.AI","cs.NE"],"title_canon_sha256":"afab765d768b164d6f481cdbbe4612fec62e09eb02e7aa078a8294557a0faa70","abstract_canon_sha256":"212a03be7f408ba1030550c84e76744c931790df0eb07349af39afea81de482e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:42:38.823866Z","signature_b64":"NyDA/tDbeOGO+4o/UeuCfQ8xJ9k+1IMnCkcf/ud3TneG8nft5mXyvrcrMnO/ty48f8rJcL1P/pSXoQNjz79+Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"94bd3ebaafa0e6675be21518076ac5a38906cf3ba4b941e27fa6a9b7df0b5037","last_reissued_at":"2026-07-05T09:42:38.823442Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:42:38.823442Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Average-Over-Time Spiking Neural Networks for Uncertainty Estimation in Regression","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.NE"],"primary_cat":"cs.LG","authors_text":"Sander Boht\\'e, Tao Sun","submitted_at":"2024-11-29T23:13:52Z","abstract_excerpt":"Uncertainty estimation is a standard tool to quantify the reliability of modern deep learning models, and crucial for many real-world applications. However, efficient uncertainty estimation methods for spiking neural networks, particularly for regression models, have been lacking. Here, we introduce two methods that adapt the Average-Over-Time Spiking Neural Network (AOT-SNN) framework to regression tasks, enhancing uncertainty estimation in event-driven models. The first method uses the heteroscedastic Gaussian approach, where SNNs predict both the mean and variance at each time step, thereby"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.00278","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/2412.00278/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":"2412.00278","created_at":"2026-07-05T09:42:38.823504+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.00278v1","created_at":"2026-07-05T09:42:38.823504+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.00278","created_at":"2026-07-05T09:42:38.823504+00:00"},{"alias_kind":"pith_short_12","alias_value":"SS6T5OVPUDTG","created_at":"2026-07-05T09:42:38.823504+00:00"},{"alias_kind":"pith_short_16","alias_value":"SS6T5OVPUDTGOW7C","created_at":"2026-07-05T09:42:38.823504+00:00"},{"alias_kind":"pith_short_8","alias_value":"SS6T5OVP","created_at":"2026-07-05T09:42:38.823504+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/SS6T5OVPUDTGOW7CCUMAO2WFUO","json":"https://pith.science/pith/SS6T5OVPUDTGOW7CCUMAO2WFUO.json","graph_json":"https://pith.science/api/pith-number/SS6T5OVPUDTGOW7CCUMAO2WFUO/graph.json","events_json":"https://pith.science/api/pith-number/SS6T5OVPUDTGOW7CCUMAO2WFUO/events.json","paper":"https://pith.science/paper/SS6T5OVP"},"agent_actions":{"view_html":"https://pith.science/pith/SS6T5OVPUDTGOW7CCUMAO2WFUO","download_json":"https://pith.science/pith/SS6T5OVPUDTGOW7CCUMAO2WFUO.json","view_paper":"https://pith.science/paper/SS6T5OVP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.00278&json=true","fetch_graph":"https://pith.science/api/pith-number/SS6T5OVPUDTGOW7CCUMAO2WFUO/graph.json","fetch_events":"https://pith.science/api/pith-number/SS6T5OVPUDTGOW7CCUMAO2WFUO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SS6T5OVPUDTGOW7CCUMAO2WFUO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SS6T5OVPUDTGOW7CCUMAO2WFUO/action/storage_attestation","attest_author":"https://pith.science/pith/SS6T5OVPUDTGOW7CCUMAO2WFUO/action/author_attestation","sign_citation":"https://pith.science/pith/SS6T5OVPUDTGOW7CCUMAO2WFUO/action/citation_signature","submit_replication":"https://pith.science/pith/SS6T5OVPUDTGOW7CCUMAO2WFUO/action/replication_record"}},"created_at":"2026-07-05T09:42:38.823504+00:00","updated_at":"2026-07-05T09:42:38.823504+00:00"}