{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:75XGQYXCH6Y6Q2THW34BWHFFVJ","short_pith_number":"pith:75XGQYXC","schema_version":"1.0","canonical_sha256":"ff6e6862e23fb1e86a67b6f81b1ca5aa4d6169f28bd7f1763a6b2603b63c07dc","source":{"kind":"arxiv","id":"1701.02284","version":1},"attestation_state":"computed","paper":{"title":"DeepDSL: A Compilation-based Domain-Specific Language for Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.PL","authors_text":"Tian Zhao, Xiaobing Huang, Yu Cao","submitted_at":"2017-01-09T18:02:13Z","abstract_excerpt":"In recent years, Deep Learning (DL) has found great success in domains such as multimedia understanding. However, the complex nature of multimedia data makes it difficult to develop DL-based software. The state-of-the art tools, such as Caffe, TensorFlow, Torch7, and CNTK, while are successful in their applicable domains, are programming libraries with fixed user interface, internal representation, and execution environment. This makes it difficult to implement portable and customized DL applications.\n  In this paper, we present DeepDSL, a domain specific language (DSL) embedded in Scala, that"},"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":"1701.02284","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.PL","submitted_at":"2017-01-09T18:02:13Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c362c4ed2ad82e1256ac57c577a867e130724de9073139b7a2d2777ac5c7533c","abstract_canon_sha256":"ceb84ad37cdbea9fe842c1d3a7681c5b14dd266b864706617f1b192a786baff5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:53:11.314251Z","signature_b64":"9fbrStgR638D5Q4yGjUpMXwYwchLgbSqXVNDswU3Z90V7qkGcZi2O8aaKbpjteA3P1RAgSLnZV5RGDaVs8WGCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ff6e6862e23fb1e86a67b6f81b1ca5aa4d6169f28bd7f1763a6b2603b63c07dc","last_reissued_at":"2026-05-18T00:53:11.313892Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:53:11.313892Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DeepDSL: A Compilation-based Domain-Specific Language for Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.PL","authors_text":"Tian Zhao, Xiaobing Huang, Yu Cao","submitted_at":"2017-01-09T18:02:13Z","abstract_excerpt":"In recent years, Deep Learning (DL) has found great success in domains such as multimedia understanding. However, the complex nature of multimedia data makes it difficult to develop DL-based software. The state-of-the art tools, such as Caffe, TensorFlow, Torch7, and CNTK, while are successful in their applicable domains, are programming libraries with fixed user interface, internal representation, and execution environment. This makes it difficult to implement portable and customized DL applications.\n  In this paper, we present DeepDSL, a domain specific language (DSL) embedded in Scala, that"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1701.02284","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":""},"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":"1701.02284","created_at":"2026-05-18T00:53:11.313946+00:00"},{"alias_kind":"arxiv_version","alias_value":"1701.02284v1","created_at":"2026-05-18T00:53:11.313946+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1701.02284","created_at":"2026-05-18T00:53:11.313946+00:00"},{"alias_kind":"pith_short_12","alias_value":"75XGQYXCH6Y6","created_at":"2026-05-18T12:31:03.183658+00:00"},{"alias_kind":"pith_short_16","alias_value":"75XGQYXCH6Y6Q2TH","created_at":"2026-05-18T12:31:03.183658+00:00"},{"alias_kind":"pith_short_8","alias_value":"75XGQYXC","created_at":"2026-05-18T12:31:03.183658+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/75XGQYXCH6Y6Q2THW34BWHFFVJ","json":"https://pith.science/pith/75XGQYXCH6Y6Q2THW34BWHFFVJ.json","graph_json":"https://pith.science/api/pith-number/75XGQYXCH6Y6Q2THW34BWHFFVJ/graph.json","events_json":"https://pith.science/api/pith-number/75XGQYXCH6Y6Q2THW34BWHFFVJ/events.json","paper":"https://pith.science/paper/75XGQYXC"},"agent_actions":{"view_html":"https://pith.science/pith/75XGQYXCH6Y6Q2THW34BWHFFVJ","download_json":"https://pith.science/pith/75XGQYXCH6Y6Q2THW34BWHFFVJ.json","view_paper":"https://pith.science/paper/75XGQYXC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1701.02284&json=true","fetch_graph":"https://pith.science/api/pith-number/75XGQYXCH6Y6Q2THW34BWHFFVJ/graph.json","fetch_events":"https://pith.science/api/pith-number/75XGQYXCH6Y6Q2THW34BWHFFVJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/75XGQYXCH6Y6Q2THW34BWHFFVJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/75XGQYXCH6Y6Q2THW34BWHFFVJ/action/storage_attestation","attest_author":"https://pith.science/pith/75XGQYXCH6Y6Q2THW34BWHFFVJ/action/author_attestation","sign_citation":"https://pith.science/pith/75XGQYXCH6Y6Q2THW34BWHFFVJ/action/citation_signature","submit_replication":"https://pith.science/pith/75XGQYXCH6Y6Q2THW34BWHFFVJ/action/replication_record"}},"created_at":"2026-05-18T00:53:11.313946+00:00","updated_at":"2026-05-18T00:53:11.313946+00:00"}