{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:CS47LTS6JVRZUA7QE56GDEGA5G","short_pith_number":"pith:CS47LTS6","schema_version":"1.0","canonical_sha256":"14b9f5ce5e4d639a03f0277c6190c0e9a83849e7b53d1e0a55ad116ce917f551","source":{"kind":"arxiv","id":"2106.12574","version":1},"attestation_state":"computed","paper":{"title":"DeepStochLog: Neural Stochastic Logic Programming","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LO"],"primary_cat":"cs.AI","authors_text":"Giuseppe Marra, Luc De Raedt, Robin Manhaeve, Thomas Winters","submitted_at":"2021-06-23T17:59:04Z","abstract_excerpt":"Recent advances in neural symbolic learning, such as DeepProbLog, extend probabilistic logic programs with neural predicates. Like graphical models, these probabilistic logic programs define a probability distribution over possible worlds, for which inference is computationally hard. We propose DeepStochLog, an alternative neural symbolic framework based on stochastic definite clause grammars, a type of stochastic logic program, which defines a probability distribution over possible derivations. More specifically, we introduce neural grammar rules into stochastic definite clause grammars to cr"},"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":"2106.12574","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2021-06-23T17:59:04Z","cross_cats_sorted":["cs.LO"],"title_canon_sha256":"ce3ce94cc4c88ee3fe898743a5862ff75ca93384030abf6f7caf52884914acc3","abstract_canon_sha256":"a5bc53a1ad28557adf4c453e39a0931c4a2c5ac3f6617f544501660f3be2f504"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:51:56.786898Z","signature_b64":"TwVg7V//Px1HV26o4FFZ0q4xp4ij6iWepaaeSNfJz+ZzyA+yOUtNrSQylS/kQ2/K7CB44SASuSGCyl1l503bDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"14b9f5ce5e4d639a03f0277c6190c0e9a83849e7b53d1e0a55ad116ce917f551","last_reissued_at":"2026-07-05T02:51:56.786557Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:51:56.786557Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DeepStochLog: Neural Stochastic Logic Programming","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LO"],"primary_cat":"cs.AI","authors_text":"Giuseppe Marra, Luc De Raedt, Robin Manhaeve, Thomas Winters","submitted_at":"2021-06-23T17:59:04Z","abstract_excerpt":"Recent advances in neural symbolic learning, such as DeepProbLog, extend probabilistic logic programs with neural predicates. Like graphical models, these probabilistic logic programs define a probability distribution over possible worlds, for which inference is computationally hard. We propose DeepStochLog, an alternative neural symbolic framework based on stochastic definite clause grammars, a type of stochastic logic program, which defines a probability distribution over possible derivations. More specifically, we introduce neural grammar rules into stochastic definite clause grammars to cr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.12574","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/2106.12574/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":"2106.12574","created_at":"2026-07-05T02:51:56.786615+00:00"},{"alias_kind":"arxiv_version","alias_value":"2106.12574v1","created_at":"2026-07-05T02:51:56.786615+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.12574","created_at":"2026-07-05T02:51:56.786615+00:00"},{"alias_kind":"pith_short_12","alias_value":"CS47LTS6JVRZ","created_at":"2026-07-05T02:51:56.786615+00:00"},{"alias_kind":"pith_short_16","alias_value":"CS47LTS6JVRZUA7Q","created_at":"2026-07-05T02:51:56.786615+00:00"},{"alias_kind":"pith_short_8","alias_value":"CS47LTS6","created_at":"2026-07-05T02:51:56.786615+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.05435","citing_title":"Neuro-Symbolic AI in 2024: A Systematic Review","ref_index":75,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CS47LTS6JVRZUA7QE56GDEGA5G","json":"https://pith.science/pith/CS47LTS6JVRZUA7QE56GDEGA5G.json","graph_json":"https://pith.science/api/pith-number/CS47LTS6JVRZUA7QE56GDEGA5G/graph.json","events_json":"https://pith.science/api/pith-number/CS47LTS6JVRZUA7QE56GDEGA5G/events.json","paper":"https://pith.science/paper/CS47LTS6"},"agent_actions":{"view_html":"https://pith.science/pith/CS47LTS6JVRZUA7QE56GDEGA5G","download_json":"https://pith.science/pith/CS47LTS6JVRZUA7QE56GDEGA5G.json","view_paper":"https://pith.science/paper/CS47LTS6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2106.12574&json=true","fetch_graph":"https://pith.science/api/pith-number/CS47LTS6JVRZUA7QE56GDEGA5G/graph.json","fetch_events":"https://pith.science/api/pith-number/CS47LTS6JVRZUA7QE56GDEGA5G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CS47LTS6JVRZUA7QE56GDEGA5G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CS47LTS6JVRZUA7QE56GDEGA5G/action/storage_attestation","attest_author":"https://pith.science/pith/CS47LTS6JVRZUA7QE56GDEGA5G/action/author_attestation","sign_citation":"https://pith.science/pith/CS47LTS6JVRZUA7QE56GDEGA5G/action/citation_signature","submit_replication":"https://pith.science/pith/CS47LTS6JVRZUA7QE56GDEGA5G/action/replication_record"}},"created_at":"2026-07-05T02:51:56.786615+00:00","updated_at":"2026-07-05T02:51:56.786615+00:00"}