{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:JKPIPQ3RMSHUDD37ZZK36LE2Z3","short_pith_number":"pith:JKPIPQ3R","schema_version":"1.0","canonical_sha256":"4a9e87c371648f418f7fce55bf2c9aced3e28e135ba58f4e3b587fa348b8069c","source":{"kind":"arxiv","id":"2003.11755","version":1},"attestation_state":"computed","paper":{"title":"A Survey of Deep Learning for Scientific Discovery","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Eric Schmidt, Maithra Raghu","submitted_at":"2020-03-26T06:16:08Z","abstract_excerpt":"Over the past few years, we have seen fundamental breakthroughs in core problems in machine learning, largely driven by advances in deep neural networks. At the same time, the amount of data collected in a wide array of scientific domains is dramatically increasing in both size and complexity. Taken together, this suggests many exciting opportunities for deep learning applications in scientific settings. But a significant challenge to this is simply knowing where to start. The sheer breadth and diversity of different deep learning techniques makes it difficult to determine what scientific prob"},"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":"2003.11755","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-03-26T06:16:08Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"d5497a8fedbe58e865c9bc3a24b70569b43d2441c3812f99b330de56874d7bb7","abstract_canon_sha256":"39b70e8a3c5eae30c2170aa1bb9c538b8f5ee1607e00d8d74811917bd180259b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:50:47.091919Z","signature_b64":"Q/D3bmP1lLaOiqWi2fePlXBWIdxbrDByet/K9pkWqQuVqZc5IVDHosPt7yuy+RpyoBIUc3pyLRaGqrwoWCyqCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4a9e87c371648f418f7fce55bf2c9aced3e28e135ba58f4e3b587fa348b8069c","last_reissued_at":"2026-07-05T00:50:47.091448Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:50:47.091448Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Survey of Deep Learning for Scientific Discovery","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Eric Schmidt, Maithra Raghu","submitted_at":"2020-03-26T06:16:08Z","abstract_excerpt":"Over the past few years, we have seen fundamental breakthroughs in core problems in machine learning, largely driven by advances in deep neural networks. At the same time, the amount of data collected in a wide array of scientific domains is dramatically increasing in both size and complexity. Taken together, this suggests many exciting opportunities for deep learning applications in scientific settings. But a significant challenge to this is simply knowing where to start. The sheer breadth and diversity of different deep learning techniques makes it difficult to determine what scientific prob"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.11755","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/2003.11755/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":"2003.11755","created_at":"2026-07-05T00:50:47.091514+00:00"},{"alias_kind":"arxiv_version","alias_value":"2003.11755v1","created_at":"2026-07-05T00:50:47.091514+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.11755","created_at":"2026-07-05T00:50:47.091514+00:00"},{"alias_kind":"pith_short_12","alias_value":"JKPIPQ3RMSHU","created_at":"2026-07-05T00:50:47.091514+00:00"},{"alias_kind":"pith_short_16","alias_value":"JKPIPQ3RMSHUDD37","created_at":"2026-07-05T00:50:47.091514+00:00"},{"alias_kind":"pith_short_8","alias_value":"JKPIPQ3R","created_at":"2026-07-05T00:50:47.091514+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2506.18498","citing_title":"A scalable estimator of higher-order information in complex dynamical systems","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2601.16294","citing_title":"Space Filling Curves is All You Need: Communication-Avoiding Matrix Multiplication Made Simple","ref_index":10,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JKPIPQ3RMSHUDD37ZZK36LE2Z3","json":"https://pith.science/pith/JKPIPQ3RMSHUDD37ZZK36LE2Z3.json","graph_json":"https://pith.science/api/pith-number/JKPIPQ3RMSHUDD37ZZK36LE2Z3/graph.json","events_json":"https://pith.science/api/pith-number/JKPIPQ3RMSHUDD37ZZK36LE2Z3/events.json","paper":"https://pith.science/paper/JKPIPQ3R"},"agent_actions":{"view_html":"https://pith.science/pith/JKPIPQ3RMSHUDD37ZZK36LE2Z3","download_json":"https://pith.science/pith/JKPIPQ3RMSHUDD37ZZK36LE2Z3.json","view_paper":"https://pith.science/paper/JKPIPQ3R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2003.11755&json=true","fetch_graph":"https://pith.science/api/pith-number/JKPIPQ3RMSHUDD37ZZK36LE2Z3/graph.json","fetch_events":"https://pith.science/api/pith-number/JKPIPQ3RMSHUDD37ZZK36LE2Z3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JKPIPQ3RMSHUDD37ZZK36LE2Z3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JKPIPQ3RMSHUDD37ZZK36LE2Z3/action/storage_attestation","attest_author":"https://pith.science/pith/JKPIPQ3RMSHUDD37ZZK36LE2Z3/action/author_attestation","sign_citation":"https://pith.science/pith/JKPIPQ3RMSHUDD37ZZK36LE2Z3/action/citation_signature","submit_replication":"https://pith.science/pith/JKPIPQ3RMSHUDD37ZZK36LE2Z3/action/replication_record"}},"created_at":"2026-07-05T00:50:47.091514+00:00","updated_at":"2026-07-05T00:50:47.091514+00:00"}