{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:V46RXMTOBIWLFQ6BP3VYUPTGV4","short_pith_number":"pith:V46RXMTO","schema_version":"1.0","canonical_sha256":"af3d1bb26e0a2cb2c3c17eeb8a3e66af1b58de84d50f280f993c57dad7a31327","source":{"kind":"arxiv","id":"1707.07287","version":3},"attestation_state":"computed","paper":{"title":"Pairing an arbitrary regressor with an artificial neural network estimating aleatoric uncertainty","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Hannes Stuke, Pavel Gurevich","submitted_at":"2017-07-23T12:07:58Z","abstract_excerpt":"We suggest a general approach to quantification of different forms of aleatoric uncertainty in regression tasks performed by artificial neural networks. It is based on the simultaneous training of two neural networks with a joint loss function and a specific hyperparameter $\\lambda>0$ that allows for automatically detecting noisy and clean regions in the input space and controlling their {\\em relative contribution} to the loss and its gradients. After the model has been trained, one of the networks performs predictions and the other quantifies the uncertainty of these predictions by estimating"},"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":"1707.07287","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-07-23T12:07:58Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"29fd5c1b3346d431b9b0fc60d3200c1011c44b3189b609a94073ab257c0f59bb","abstract_canon_sha256":"1aa250ea2e450a6172dd5d04ad8cc7877b5a55d4f627a199124887f79d350217"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:06:41.664586Z","signature_b64":"XJ6ynQuNlxQZTI8QrYj/z8ka1Zrtr4W9k7MGp5R51yAX+EGDwu2q2V8UOJgbMHNiqcU8lCUL6IpqYSwnLlf0DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"af3d1bb26e0a2cb2c3c17eeb8a3e66af1b58de84d50f280f993c57dad7a31327","last_reissued_at":"2026-05-18T00:06:41.663952Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:06:41.663952Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Pairing an arbitrary regressor with an artificial neural network estimating aleatoric uncertainty","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Hannes Stuke, Pavel Gurevich","submitted_at":"2017-07-23T12:07:58Z","abstract_excerpt":"We suggest a general approach to quantification of different forms of aleatoric uncertainty in regression tasks performed by artificial neural networks. It is based on the simultaneous training of two neural networks with a joint loss function and a specific hyperparameter $\\lambda>0$ that allows for automatically detecting noisy and clean regions in the input space and controlling their {\\em relative contribution} to the loss and its gradients. After the model has been trained, one of the networks performs predictions and the other quantifies the uncertainty of these predictions by estimating"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.07287","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":""},"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":"1707.07287","created_at":"2026-05-18T00:06:41.664051+00:00"},{"alias_kind":"arxiv_version","alias_value":"1707.07287v3","created_at":"2026-05-18T00:06:41.664051+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.07287","created_at":"2026-05-18T00:06:41.664051+00:00"},{"alias_kind":"pith_short_12","alias_value":"V46RXMTOBIWL","created_at":"2026-05-18T12:31:49.984773+00:00"},{"alias_kind":"pith_short_16","alias_value":"V46RXMTOBIWLFQ6B","created_at":"2026-05-18T12:31:49.984773+00:00"},{"alias_kind":"pith_short_8","alias_value":"V46RXMTO","created_at":"2026-05-18T12:31:49.984773+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/V46RXMTOBIWLFQ6BP3VYUPTGV4","json":"https://pith.science/pith/V46RXMTOBIWLFQ6BP3VYUPTGV4.json","graph_json":"https://pith.science/api/pith-number/V46RXMTOBIWLFQ6BP3VYUPTGV4/graph.json","events_json":"https://pith.science/api/pith-number/V46RXMTOBIWLFQ6BP3VYUPTGV4/events.json","paper":"https://pith.science/paper/V46RXMTO"},"agent_actions":{"view_html":"https://pith.science/pith/V46RXMTOBIWLFQ6BP3VYUPTGV4","download_json":"https://pith.science/pith/V46RXMTOBIWLFQ6BP3VYUPTGV4.json","view_paper":"https://pith.science/paper/V46RXMTO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1707.07287&json=true","fetch_graph":"https://pith.science/api/pith-number/V46RXMTOBIWLFQ6BP3VYUPTGV4/graph.json","fetch_events":"https://pith.science/api/pith-number/V46RXMTOBIWLFQ6BP3VYUPTGV4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/V46RXMTOBIWLFQ6BP3VYUPTGV4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/V46RXMTOBIWLFQ6BP3VYUPTGV4/action/storage_attestation","attest_author":"https://pith.science/pith/V46RXMTOBIWLFQ6BP3VYUPTGV4/action/author_attestation","sign_citation":"https://pith.science/pith/V46RXMTOBIWLFQ6BP3VYUPTGV4/action/citation_signature","submit_replication":"https://pith.science/pith/V46RXMTOBIWLFQ6BP3VYUPTGV4/action/replication_record"}},"created_at":"2026-05-18T00:06:41.664051+00:00","updated_at":"2026-05-18T00:06:41.664051+00:00"}