{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:TJ2ZBRCNWFB7KPRT46OXBHSIXI","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":"083c124a853fac9dcd2991acd886c16fad955eeeb5914f5d8980ada8e367f212","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2019-07-18T11:14:01Z","title_canon_sha256":"a64a7a41e039f9cee273afd5d36cb54231d85890b148e8912b191e461920b0ff"},"schema_version":"1.0","source":{"id":"1907.08194","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.08194","created_at":"2026-07-05T00:07:17Z"},{"alias_kind":"arxiv_version","alias_value":"1907.08194v2","created_at":"2026-07-05T00:07:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.08194","created_at":"2026-07-05T00:07:17Z"},{"alias_kind":"pith_short_12","alias_value":"TJ2ZBRCNWFB7","created_at":"2026-07-05T00:07:17Z"},{"alias_kind":"pith_short_16","alias_value":"TJ2ZBRCNWFB7KPRT","created_at":"2026-07-05T00:07:17Z"},{"alias_kind":"pith_short_8","alias_value":"TJ2ZBRCN","created_at":"2026-07-05T00:07:17Z"}],"graph_snapshots":[{"event_id":"sha256:39d6032d295b0f532a6ad9e6943047847abc01a3c9e577afd6bae730bfa1917f","target":"graph","created_at":"2026-07-05T00:07:17Z","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/1907.08194/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We introduce DeepProbLog, a neural probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learning techniques of the underlying probabilistic logic programming language ProbLog can be adapted for the new language. We theoretically and experimentally demonstrate that DeepProbLog supports (i) both symbolic and subsymbolic representations and inference, (ii) program induction, (iii) probabilistic (logic) programming, and (iv) (deep) learning from examples. To the best of our knowledge, this work is the first to pr","authors_text":"Angelika Kimmig, Luc De Raedt, Robin Manhaeve, Sebastijan Duman\\v{c}i\\'c, Thomas Demeester","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2019-07-18T11:14:01Z","title":"Neural Probabilistic Logic Programming in DeepProbLog"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.08194","kind":"arxiv","version":2},"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":"sha256:0173ecf3d6ab3faa682bebb72b338d163b138b2f66bf2155375c95dd837554c0","target":"record","created_at":"2026-07-05T00:07:17Z","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":"083c124a853fac9dcd2991acd886c16fad955eeeb5914f5d8980ada8e367f212","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2019-07-18T11:14:01Z","title_canon_sha256":"a64a7a41e039f9cee273afd5d36cb54231d85890b148e8912b191e461920b0ff"},"schema_version":"1.0","source":{"id":"1907.08194","kind":"arxiv","version":2}},"canonical_sha256":"9a7590c44db143f53e33e79d709e48ba12c23f963b98bd585ba1bf829dfad311","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9a7590c44db143f53e33e79d709e48ba12c23f963b98bd585ba1bf829dfad311","first_computed_at":"2026-07-05T00:07:17.572571Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:07:17.572571Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HWnHCHcH9oUVA6fEl1MemrQz+S4D7NgMUTLdLP9LkaSTZgsuz+n6w0RQR/vmofR+PtTYPC89u2OJRs0sULyFAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T00:07:17.573046Z","signed_message":"canonical_sha256_bytes"},"source_id":"1907.08194","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0173ecf3d6ab3faa682bebb72b338d163b138b2f66bf2155375c95dd837554c0","sha256:39d6032d295b0f532a6ad9e6943047847abc01a3c9e577afd6bae730bfa1917f"],"state_sha256":"e5883926c2cea0fb1961d00c44964607819729e6051465970566d1e0e992c1ec"}