{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:H3HMGN5NL56N52W7IFOLCTGFZH","short_pith_number":"pith:H3HMGN5N","schema_version":"1.0","canonical_sha256":"3ecec337ad5f7cdeeadf415cb14cc5c9fb1591d670ae1f1b6ab1b3169d255ff1","source":{"kind":"arxiv","id":"2406.00332","version":1},"attestation_state":"computed","paper":{"title":"A Structured Review of Literature on Uncertainty in Machine Learning & Deep Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Ali Mosleh, Fahimeh Fakour, Ramin Ramezani","submitted_at":"2024-06-01T07:17:38Z","abstract_excerpt":"The adaptation and use of Machine Learning (ML) in our daily lives has led to concerns in lack of transparency, privacy, reliability, among others. As a result, we are seeing research in niche areas such as interpretability, causality, bias and fairness, and reliability. In this survey paper, we focus on a critical concern for adaptation of ML in risk-sensitive applications, namely understanding and quantifying uncertainty. Our paper approaches this topic in a structured way, providing a review of the literature in the various facets that uncertainty is enveloped in the ML process. We begin by"},"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":"2406.00332","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-01T07:17:38Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"dea5a87df494723c0d4463aef14b859c31e334e3e7a3190cafaaf3fa98eff614","abstract_canon_sha256":"fea9b8e33bf6457c7419bd49ee0ae13fd81803ea69dabe24ac6b71631f0a678f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:26:00.279848Z","signature_b64":"crJFSYOz3IZua3Y3VV+0Mkm9ebbmLKQaS5XJj+qdMMSGER70C8/tMSF+juKVkgZt3MPjYjJJd2RVK3fUvCXmCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3ecec337ad5f7cdeeadf415cb14cc5c9fb1591d670ae1f1b6ab1b3169d255ff1","last_reissued_at":"2026-07-05T08:26:00.279397Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:26:00.279397Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Structured Review of Literature on Uncertainty in Machine Learning & Deep Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Ali Mosleh, Fahimeh Fakour, Ramin Ramezani","submitted_at":"2024-06-01T07:17:38Z","abstract_excerpt":"The adaptation and use of Machine Learning (ML) in our daily lives has led to concerns in lack of transparency, privacy, reliability, among others. As a result, we are seeing research in niche areas such as interpretability, causality, bias and fairness, and reliability. In this survey paper, we focus on a critical concern for adaptation of ML in risk-sensitive applications, namely understanding and quantifying uncertainty. Our paper approaches this topic in a structured way, providing a review of the literature in the various facets that uncertainty is enveloped in the ML process. We begin by"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.00332","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/2406.00332/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":"2406.00332","created_at":"2026-07-05T08:26:00.279462+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.00332v1","created_at":"2026-07-05T08:26:00.279462+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.00332","created_at":"2026-07-05T08:26:00.279462+00:00"},{"alias_kind":"pith_short_12","alias_value":"H3HMGN5NL56N","created_at":"2026-07-05T08:26:00.279462+00:00"},{"alias_kind":"pith_short_16","alias_value":"H3HMGN5NL56N52W7","created_at":"2026-07-05T08:26:00.279462+00:00"},{"alias_kind":"pith_short_8","alias_value":"H3HMGN5N","created_at":"2026-07-05T08:26:00.279462+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.28416","citing_title":"AEGIS: A Semantic GAN and Evidential Learning Frameworkfor Robust Adversarial Detection in Vision Sensors","ref_index":47,"is_internal_anchor":false},{"citing_arxiv_id":"2508.03578","citing_title":"RadProPoser: Probabilistic Radar Tensor Human Pose Estimation That Knows Its Limits","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2604.08639","citing_title":"VOLTA: The Surprising Ineffectiveness of Auxiliary Losses for Calibrated Deep Learning","ref_index":4,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/H3HMGN5NL56N52W7IFOLCTGFZH","json":"https://pith.science/pith/H3HMGN5NL56N52W7IFOLCTGFZH.json","graph_json":"https://pith.science/api/pith-number/H3HMGN5NL56N52W7IFOLCTGFZH/graph.json","events_json":"https://pith.science/api/pith-number/H3HMGN5NL56N52W7IFOLCTGFZH/events.json","paper":"https://pith.science/paper/H3HMGN5N"},"agent_actions":{"view_html":"https://pith.science/pith/H3HMGN5NL56N52W7IFOLCTGFZH","download_json":"https://pith.science/pith/H3HMGN5NL56N52W7IFOLCTGFZH.json","view_paper":"https://pith.science/paper/H3HMGN5N","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.00332&json=true","fetch_graph":"https://pith.science/api/pith-number/H3HMGN5NL56N52W7IFOLCTGFZH/graph.json","fetch_events":"https://pith.science/api/pith-number/H3HMGN5NL56N52W7IFOLCTGFZH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/H3HMGN5NL56N52W7IFOLCTGFZH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/H3HMGN5NL56N52W7IFOLCTGFZH/action/storage_attestation","attest_author":"https://pith.science/pith/H3HMGN5NL56N52W7IFOLCTGFZH/action/author_attestation","sign_citation":"https://pith.science/pith/H3HMGN5NL56N52W7IFOLCTGFZH/action/citation_signature","submit_replication":"https://pith.science/pith/H3HMGN5NL56N52W7IFOLCTGFZH/action/replication_record"}},"created_at":"2026-07-05T08:26:00.279462+00:00","updated_at":"2026-07-05T08:26:00.279462+00:00"}