{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:NKITESZW25RFWJ754VOIV74MQD","short_pith_number":"pith:NKITESZW","schema_version":"1.0","canonical_sha256":"6a91324b36d7625b27fde55c8aff8c80e5ed91d2efa7d2d406252c0dedd5c196","source":{"kind":"arxiv","id":"1906.08834","version":2},"attestation_state":"computed","paper":{"title":"Deep Learning in the Automotive Industry: Recent Advances and Application Examples","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO","eess.SP","stat.ML"],"primary_cat":"cs.LG","authors_text":"Kanwar Bharat Singh, Mustafa Ali Arat","submitted_at":"2019-06-20T20:30:39Z","abstract_excerpt":"One of the most exciting technology breakthroughs in the last few years has been the rise of deep learning. State-of-the-art deep learning models are being widely deployed in academia and industry, across a variety of areas, from image analysis to natural language processing. These models have grown from fledgling research subjects to mature techniques in real-world use. The increasing scale of data, computational power and the associated algorithmic innovations are the main drivers for the progress we see in this field. These developments also have a huge potential for the automotive industry"},"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":"1906.08834","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-20T20:30:39Z","cross_cats_sorted":["cs.RO","eess.SP","stat.ML"],"title_canon_sha256":"535612592af6f0a164bfe03ab798d63c70630e1b46718359c9fbaffdeedd50ce","abstract_canon_sha256":"9ae21d2199bf5c95d166bb3dd85e14630c5a176d6f9ed26d0746a96b07e1300c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:42:41.229782Z","signature_b64":"lfmpY2YKHxsZiSmbsjKTeKQ20ztNmjLrOJQPWMws9DzwMGvQzhuJNUYH+lGV0EfIbe/cPhvPpFVU0tKRwghsBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6a91324b36d7625b27fde55c8aff8c80e5ed91d2efa7d2d406252c0dedd5c196","last_reissued_at":"2026-05-17T23:42:41.229019Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:42:41.229019Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Learning in the Automotive Industry: Recent Advances and Application Examples","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO","eess.SP","stat.ML"],"primary_cat":"cs.LG","authors_text":"Kanwar Bharat Singh, Mustafa Ali Arat","submitted_at":"2019-06-20T20:30:39Z","abstract_excerpt":"One of the most exciting technology breakthroughs in the last few years has been the rise of deep learning. State-of-the-art deep learning models are being widely deployed in academia and industry, across a variety of areas, from image analysis to natural language processing. These models have grown from fledgling research subjects to mature techniques in real-world use. The increasing scale of data, computational power and the associated algorithmic innovations are the main drivers for the progress we see in this field. These developments also have a huge potential for the automotive industry"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.08834","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1906.08834","created_at":"2026-05-17T23:42:41.229146+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.08834v2","created_at":"2026-05-17T23:42:41.229146+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.08834","created_at":"2026-05-17T23:42:41.229146+00:00"},{"alias_kind":"pith_short_12","alias_value":"NKITESZW25RF","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_16","alias_value":"NKITESZW25RFWJ75","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_8","alias_value":"NKITESZW","created_at":"2026-05-18T12:33:24.271573+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/NKITESZW25RFWJ754VOIV74MQD","json":"https://pith.science/pith/NKITESZW25RFWJ754VOIV74MQD.json","graph_json":"https://pith.science/api/pith-number/NKITESZW25RFWJ754VOIV74MQD/graph.json","events_json":"https://pith.science/api/pith-number/NKITESZW25RFWJ754VOIV74MQD/events.json","paper":"https://pith.science/paper/NKITESZW"},"agent_actions":{"view_html":"https://pith.science/pith/NKITESZW25RFWJ754VOIV74MQD","download_json":"https://pith.science/pith/NKITESZW25RFWJ754VOIV74MQD.json","view_paper":"https://pith.science/paper/NKITESZW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.08834&json=true","fetch_graph":"https://pith.science/api/pith-number/NKITESZW25RFWJ754VOIV74MQD/graph.json","fetch_events":"https://pith.science/api/pith-number/NKITESZW25RFWJ754VOIV74MQD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NKITESZW25RFWJ754VOIV74MQD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NKITESZW25RFWJ754VOIV74MQD/action/storage_attestation","attest_author":"https://pith.science/pith/NKITESZW25RFWJ754VOIV74MQD/action/author_attestation","sign_citation":"https://pith.science/pith/NKITESZW25RFWJ754VOIV74MQD/action/citation_signature","submit_replication":"https://pith.science/pith/NKITESZW25RFWJ754VOIV74MQD/action/replication_record"}},"created_at":"2026-05-17T23:42:41.229146+00:00","updated_at":"2026-05-17T23:42:41.229146+00:00"}