{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:P2UAAC2CLWQWEGUERRU3NUJTB6","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":"17b5b57833a1ffb6d7e0207646a39f1a64bba974cdbdd40a56572c2ecbd81fbf","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-12-06T12:15:28Z","title_canon_sha256":"dcc1c15378a95ac92bb024e34f077265b298659590433dfa7dc95f6fec926b07"},"schema_version":"1.0","source":{"id":"2112.02962","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2112.02962","created_at":"2026-07-05T04:55:28Z"},{"alias_kind":"arxiv_version","alias_value":"2112.02962v4","created_at":"2026-07-05T04:55:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.02962","created_at":"2026-07-05T04:55:28Z"},{"alias_kind":"pith_short_12","alias_value":"P2UAAC2CLWQW","created_at":"2026-07-05T04:55:28Z"},{"alias_kind":"pith_short_16","alias_value":"P2UAAC2CLWQWEGUE","created_at":"2026-07-05T04:55:28Z"},{"alias_kind":"pith_short_8","alias_value":"P2UAAC2C","created_at":"2026-07-05T04:55:28Z"}],"graph_snapshots":[{"event_id":"sha256:743fc11da38c4f3e2b6e7e0be80caabd95ad7ed69ca5fe42e2e46e2a09860024","target":"graph","created_at":"2026-07-05T04:55:28Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2112.02962/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Tabular data are ubiquitous in real world applications. Although many commonly-used neural components (e.g., convolution) and extensible neural networks (e.g., ResNet) have been developed by the machine learning community, few of them were effective for tabular data and few designs were adequately tailored for tabular data structures. In this paper, we propose a novel and flexible neural component for tabular data, called Abstract Layer (AbstLay), which learns to explicitly group correlative input features and generate higher-level features for semantics abstraction. Also, we design a structur","authors_text":"Danny Z. Chen, Jian Wu, Jintai Chen, Kuanlun Liao, Yao Wan","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-12-06T12:15:28Z","title":"DANets: Deep Abstract Networks for Tabular Data Classification and Regression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.02962","kind":"arxiv","version":4},"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":"sha256:129fd2629e7d476782ef46c129d119772c24940dbc2ad5a549e936d121824d5e","target":"record","created_at":"2026-07-05T04:55:28Z","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":"17b5b57833a1ffb6d7e0207646a39f1a64bba974cdbdd40a56572c2ecbd81fbf","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-12-06T12:15:28Z","title_canon_sha256":"dcc1c15378a95ac92bb024e34f077265b298659590433dfa7dc95f6fec926b07"},"schema_version":"1.0","source":{"id":"2112.02962","kind":"arxiv","version":4}},"canonical_sha256":"7ea8000b425da1621a848c69b6d1330fb21f0506d8dffff1a4f5f42ce51e815c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7ea8000b425da1621a848c69b6d1330fb21f0506d8dffff1a4f5f42ce51e815c","first_computed_at":"2026-07-05T04:55:28.714434Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:55:28.714434Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uNVrak01CBcRzTb1bNRG0XKvfKJDdmGF2U7tHCh7Vfg30jvhde00vXnQq1MJj1yZhlypjeHAVzyumiKCdJV5Dg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:55:28.715021Z","signed_message":"canonical_sha256_bytes"},"source_id":"2112.02962","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:129fd2629e7d476782ef46c129d119772c24940dbc2ad5a549e936d121824d5e","sha256:743fc11da38c4f3e2b6e7e0be80caabd95ad7ed69ca5fe42e2e46e2a09860024"],"state_sha256":"f9af1a0442847810238e8c9f2967d4512fda0859bfd78c5c43e4ebc3bf5da56a"}