{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:PXL34FU4U3DXBFG3ADE7FQSRY6","short_pith_number":"pith:PXL34FU4","schema_version":"1.0","canonical_sha256":"7dd7be169ca6c77094db00c9f2c251c78d041ca59bb13f5892ce0a1c0391f492","source":{"kind":"arxiv","id":"2005.01557","version":1},"attestation_state":"computed","paper":{"title":"Off-the-shelf deep learning is not enough: parsimony, Bayes and causality","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.dis-nn","cs.LG","stat.ML"],"primary_cat":"physics.comp-ph","authors_text":"Lukas Vlcek, Maxim Ziatdinov, Rama K. Vasudevan, Sergei V. Kalinin","submitted_at":"2020-05-04T15:16:30Z","abstract_excerpt":"Deep neural networks (\"deep learning\") have emerged as a technology of choice to tackle problems in natural language processing, computer vision, speech recognition and gameplay, and in just a few years has led to superhuman level performance and ushered in a new wave of \"AI.\" Buoyed by these successes, researchers in the physical sciences have made steady progress in incorporating deep learning into their respective domains. However, such adoption brings substantial challenges that need to be recognized and confronted. Here, we discuss both opportunities and roadblocks to implementation of de"},"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":"2005.01557","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2020-05-04T15:16:30Z","cross_cats_sorted":["cond-mat.dis-nn","cs.LG","stat.ML"],"title_canon_sha256":"6b246a732ff7af6613aecf8dd8937faa48a8fcf2f0587378a84fbce08d31dfda","abstract_canon_sha256":"d059460009d2eba11db026f98cf0b213cfbc4652b8e07db12f6ab1d6f5c7a6a5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:00:08.881323Z","signature_b64":"UKNPnH267GUt6GFUffwkfRrSuMGkN5yZSkJ9sLILIo4JHaxr90pADoUkGdoE+ZD67esmdBeJO1S8i18zDhCWDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7dd7be169ca6c77094db00c9f2c251c78d041ca59bb13f5892ce0a1c0391f492","last_reissued_at":"2026-07-05T01:00:08.880834Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:00:08.880834Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Off-the-shelf deep learning is not enough: parsimony, Bayes and causality","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.dis-nn","cs.LG","stat.ML"],"primary_cat":"physics.comp-ph","authors_text":"Lukas Vlcek, Maxim Ziatdinov, Rama K. Vasudevan, Sergei V. Kalinin","submitted_at":"2020-05-04T15:16:30Z","abstract_excerpt":"Deep neural networks (\"deep learning\") have emerged as a technology of choice to tackle problems in natural language processing, computer vision, speech recognition and gameplay, and in just a few years has led to superhuman level performance and ushered in a new wave of \"AI.\" Buoyed by these successes, researchers in the physical sciences have made steady progress in incorporating deep learning into their respective domains. However, such adoption brings substantial challenges that need to be recognized and confronted. Here, we discuss both opportunities and roadblocks to implementation of de"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.01557","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/2005.01557/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":"2005.01557","created_at":"2026-07-05T01:00:08.880896+00:00"},{"alias_kind":"arxiv_version","alias_value":"2005.01557v1","created_at":"2026-07-05T01:00:08.880896+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.01557","created_at":"2026-07-05T01:00:08.880896+00:00"},{"alias_kind":"pith_short_12","alias_value":"PXL34FU4U3DX","created_at":"2026-07-05T01:00:08.880896+00:00"},{"alias_kind":"pith_short_16","alias_value":"PXL34FU4U3DXBFG3","created_at":"2026-07-05T01:00:08.880896+00:00"},{"alias_kind":"pith_short_8","alias_value":"PXL34FU4","created_at":"2026-07-05T01:00:08.880896+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/PXL34FU4U3DXBFG3ADE7FQSRY6","json":"https://pith.science/pith/PXL34FU4U3DXBFG3ADE7FQSRY6.json","graph_json":"https://pith.science/api/pith-number/PXL34FU4U3DXBFG3ADE7FQSRY6/graph.json","events_json":"https://pith.science/api/pith-number/PXL34FU4U3DXBFG3ADE7FQSRY6/events.json","paper":"https://pith.science/paper/PXL34FU4"},"agent_actions":{"view_html":"https://pith.science/pith/PXL34FU4U3DXBFG3ADE7FQSRY6","download_json":"https://pith.science/pith/PXL34FU4U3DXBFG3ADE7FQSRY6.json","view_paper":"https://pith.science/paper/PXL34FU4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2005.01557&json=true","fetch_graph":"https://pith.science/api/pith-number/PXL34FU4U3DXBFG3ADE7FQSRY6/graph.json","fetch_events":"https://pith.science/api/pith-number/PXL34FU4U3DXBFG3ADE7FQSRY6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PXL34FU4U3DXBFG3ADE7FQSRY6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PXL34FU4U3DXBFG3ADE7FQSRY6/action/storage_attestation","attest_author":"https://pith.science/pith/PXL34FU4U3DXBFG3ADE7FQSRY6/action/author_attestation","sign_citation":"https://pith.science/pith/PXL34FU4U3DXBFG3ADE7FQSRY6/action/citation_signature","submit_replication":"https://pith.science/pith/PXL34FU4U3DXBFG3ADE7FQSRY6/action/replication_record"}},"created_at":"2026-07-05T01:00:08.880896+00:00","updated_at":"2026-07-05T01:00:08.880896+00:00"}