{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:V46RXMTOBIWLFQ6BP3VYUPTGV4","short_pith_number":"pith:V46RXMTO","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"},"canonical_sha256":"af3d1bb26e0a2cb2c3c17eeb8a3e66af1b58de84d50f280f993c57dad7a31327","source":{"kind":"arxiv","id":"1707.07287","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1707.07287","created_at":"2026-05-18T00:06:41Z"},{"alias_kind":"arxiv_version","alias_value":"1707.07287v3","created_at":"2026-05-18T00:06:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.07287","created_at":"2026-05-18T00:06:41Z"},{"alias_kind":"pith_short_12","alias_value":"V46RXMTOBIWL","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_16","alias_value":"V46RXMTOBIWLFQ6B","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_8","alias_value":"V46RXMTO","created_at":"2026-05-18T12:31:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:V46RXMTOBIWLFQ6BP3VYUPTGV4","target":"record","payload":{"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"},"canonical_sha256":"af3d1bb26e0a2cb2c3c17eeb8a3e66af1b58de84d50f280f993c57dad7a31327","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"},"source_kind":"arxiv","source_id":"1707.07287","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:06:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CRSjDtxVb0sDEYMA6UUGHtKz6tPMr0RgZFI5fkquAuXX5kRJig8znxvle4QqbLTSNsUgnM+rq8eGV/joam08Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T19:55:54.197099Z"},"content_sha256":"ec2638d831453fa75bbb02b5f11692f24a612b51c60b82a749e3d0c66f4ee57d","schema_version":"1.0","event_id":"sha256:ec2638d831453fa75bbb02b5f11692f24a612b51c60b82a749e3d0c66f4ee57d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:V46RXMTOBIWLFQ6BP3VYUPTGV4","target":"graph","payload":{"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:06:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"u/le2ofCGx3Yd+BzzX5O6UWL3EgKk0M/RmaWZFyfSXEY+m3E/nlz+o8wZkR8Ymw2J+e4ehTdIrrbANA/pL8dAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T19:55:54.197775Z"},"content_sha256":"7fcd20440b8331e1081f4ee737fc35d630edc41d7b88f7e3baadbe1887a60b77","schema_version":"1.0","event_id":"sha256:7fcd20440b8331e1081f4ee737fc35d630edc41d7b88f7e3baadbe1887a60b77"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/V46RXMTOBIWLFQ6BP3VYUPTGV4/bundle.json","state_url":"https://pith.science/pith/V46RXMTOBIWLFQ6BP3VYUPTGV4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/V46RXMTOBIWLFQ6BP3VYUPTGV4/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":"2026-06-06T19:55:54Z","links":{"resolver":"https://pith.science/pith/V46RXMTOBIWLFQ6BP3VYUPTGV4","bundle":"https://pith.science/pith/V46RXMTOBIWLFQ6BP3VYUPTGV4/bundle.json","state":"https://pith.science/pith/V46RXMTOBIWLFQ6BP3VYUPTGV4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/V46RXMTOBIWLFQ6BP3VYUPTGV4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:V46RXMTOBIWLFQ6BP3VYUPTGV4","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":"1aa250ea2e450a6172dd5d04ad8cc7877b5a55d4f627a199124887f79d350217","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-07-23T12:07:58Z","title_canon_sha256":"29fd5c1b3346d431b9b0fc60d3200c1011c44b3189b609a94073ab257c0f59bb"},"schema_version":"1.0","source":{"id":"1707.07287","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1707.07287","created_at":"2026-05-18T00:06:41Z"},{"alias_kind":"arxiv_version","alias_value":"1707.07287v3","created_at":"2026-05-18T00:06:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.07287","created_at":"2026-05-18T00:06:41Z"},{"alias_kind":"pith_short_12","alias_value":"V46RXMTOBIWL","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_16","alias_value":"V46RXMTOBIWLFQ6B","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_8","alias_value":"V46RXMTO","created_at":"2026-05-18T12:31:49Z"}],"graph_snapshots":[{"event_id":"sha256:7fcd20440b8331e1081f4ee737fc35d630edc41d7b88f7e3baadbe1887a60b77","target":"graph","created_at":"2026-05-18T00:06:41Z","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"},"paper":{"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","authors_text":"Hannes Stuke, Pavel Gurevich","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-07-23T12:07:58Z","title":"Pairing an arbitrary regressor with an artificial neural network estimating aleatoric uncertainty"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.07287","kind":"arxiv","version":3},"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:ec2638d831453fa75bbb02b5f11692f24a612b51c60b82a749e3d0c66f4ee57d","target":"record","created_at":"2026-05-18T00:06:41Z","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":"1aa250ea2e450a6172dd5d04ad8cc7877b5a55d4f627a199124887f79d350217","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-07-23T12:07:58Z","title_canon_sha256":"29fd5c1b3346d431b9b0fc60d3200c1011c44b3189b609a94073ab257c0f59bb"},"schema_version":"1.0","source":{"id":"1707.07287","kind":"arxiv","version":3}},"canonical_sha256":"af3d1bb26e0a2cb2c3c17eeb8a3e66af1b58de84d50f280f993c57dad7a31327","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"af3d1bb26e0a2cb2c3c17eeb8a3e66af1b58de84d50f280f993c57dad7a31327","first_computed_at":"2026-05-18T00:06:41.663952Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:06:41.663952Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XJ6ynQuNlxQZTI8QrYj/z8ka1Zrtr4W9k7MGp5R51yAX+EGDwu2q2V8UOJgbMHNiqcU8lCUL6IpqYSwnLlf0DA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:06:41.664586Z","signed_message":"canonical_sha256_bytes"},"source_id":"1707.07287","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ec2638d831453fa75bbb02b5f11692f24a612b51c60b82a749e3d0c66f4ee57d","sha256:7fcd20440b8331e1081f4ee737fc35d630edc41d7b88f7e3baadbe1887a60b77"],"state_sha256":"8e016a28a41fe5290a8c10f2299ee0c853f7e62f8b425fe1cf5fc4b13f3cd85a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nFeafNeipbmkItj72K9uAfldiNqzFuFVL6TjkMd1SG4sM7xPgIvDnaEOiweikYAOSkNYKhEBruoO+wfbuvC9Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T19:55:54.201993Z","bundle_sha256":"250ffe5f4aa6df1c23dba5064c539863b9babe98a0a6b20deae5af456247ed86"}}