{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:TA6LAEHYI7AJNHLDVFXDJV5PRA","short_pith_number":"pith:TA6LAEHY","schema_version":"1.0","canonical_sha256":"983cb010f847c0969d63a96e34d7af8828c3cd4cd2714df22fe2aa710dc08b1e","source":{"kind":"arxiv","id":"2106.16057","version":1},"attestation_state":"computed","paper":{"title":"DAEMA: Denoising Autoencoder with Mask Attention","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Damien Fourure, Muhammad Usama Javaid, Nicolas Posocco, Simon Tihon, Thomas Peel","submitted_at":"2021-06-30T13:32:23Z","abstract_excerpt":"Missing data is a recurrent and challenging problem, especially when using machine learning algorithms for real-world applications. For this reason, missing data imputation has become an active research area, in which recent deep learning approaches have achieved state-of-the-art results. We propose DAEMA (Denoising Autoencoder with Mask Attention), an algorithm based on a denoising autoencoder architecture with an attention mechanism. While most imputation algorithms use incomplete inputs as they would use complete data - up to basic preprocessing (e.g. mean imputation) - DAEMA leverages a ma"},"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":"2106.16057","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-06-30T13:32:23Z","cross_cats_sorted":[],"title_canon_sha256":"ba886c1fb3fd260629560728b68c172f1da2dc73c809ea0fb32f8ed3ab7f1769","abstract_canon_sha256":"01517b934392feb57399e80589db36fab27340076f2f9fb44b2eb1263455d73c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:54:02.986614Z","signature_b64":"zzIO42XD4KEI1iWqfndwYLgFcvLwEhxWSqncx9+qRz8zwzp9yqx1RBQeyfCYXwANHF1EAluAexp/HYOXnbcdCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"983cb010f847c0969d63a96e34d7af8828c3cd4cd2714df22fe2aa710dc08b1e","last_reissued_at":"2026-07-05T02:54:02.986183Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:54:02.986183Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DAEMA: Denoising Autoencoder with Mask Attention","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Damien Fourure, Muhammad Usama Javaid, Nicolas Posocco, Simon Tihon, Thomas Peel","submitted_at":"2021-06-30T13:32:23Z","abstract_excerpt":"Missing data is a recurrent and challenging problem, especially when using machine learning algorithms for real-world applications. For this reason, missing data imputation has become an active research area, in which recent deep learning approaches have achieved state-of-the-art results. We propose DAEMA (Denoising Autoencoder with Mask Attention), an algorithm based on a denoising autoencoder architecture with an attention mechanism. While most imputation algorithms use incomplete inputs as they would use complete data - up to basic preprocessing (e.g. mean imputation) - DAEMA leverages a ma"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.16057","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/2106.16057/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":"2106.16057","created_at":"2026-07-05T02:54:02.986244+00:00"},{"alias_kind":"arxiv_version","alias_value":"2106.16057v1","created_at":"2026-07-05T02:54:02.986244+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.16057","created_at":"2026-07-05T02:54:02.986244+00:00"},{"alias_kind":"pith_short_12","alias_value":"TA6LAEHYI7AJ","created_at":"2026-07-05T02:54:02.986244+00:00"},{"alias_kind":"pith_short_16","alias_value":"TA6LAEHYI7AJNHLD","created_at":"2026-07-05T02:54:02.986244+00:00"},{"alias_kind":"pith_short_8","alias_value":"TA6LAEHY","created_at":"2026-07-05T02:54:02.986244+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/TA6LAEHYI7AJNHLDVFXDJV5PRA","json":"https://pith.science/pith/TA6LAEHYI7AJNHLDVFXDJV5PRA.json","graph_json":"https://pith.science/api/pith-number/TA6LAEHYI7AJNHLDVFXDJV5PRA/graph.json","events_json":"https://pith.science/api/pith-number/TA6LAEHYI7AJNHLDVFXDJV5PRA/events.json","paper":"https://pith.science/paper/TA6LAEHY"},"agent_actions":{"view_html":"https://pith.science/pith/TA6LAEHYI7AJNHLDVFXDJV5PRA","download_json":"https://pith.science/pith/TA6LAEHYI7AJNHLDVFXDJV5PRA.json","view_paper":"https://pith.science/paper/TA6LAEHY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2106.16057&json=true","fetch_graph":"https://pith.science/api/pith-number/TA6LAEHYI7AJNHLDVFXDJV5PRA/graph.json","fetch_events":"https://pith.science/api/pith-number/TA6LAEHYI7AJNHLDVFXDJV5PRA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TA6LAEHYI7AJNHLDVFXDJV5PRA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TA6LAEHYI7AJNHLDVFXDJV5PRA/action/storage_attestation","attest_author":"https://pith.science/pith/TA6LAEHYI7AJNHLDVFXDJV5PRA/action/author_attestation","sign_citation":"https://pith.science/pith/TA6LAEHYI7AJNHLDVFXDJV5PRA/action/citation_signature","submit_replication":"https://pith.science/pith/TA6LAEHYI7AJNHLDVFXDJV5PRA/action/replication_record"}},"created_at":"2026-07-05T02:54:02.986244+00:00","updated_at":"2026-07-05T02:54:02.986244+00:00"}