{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:VYDBYYVL6554QNOBKZKLZEFANH","short_pith_number":"pith:VYDBYYVL","schema_version":"1.0","canonical_sha256":"ae061c62abf77bc835c15654bc90a069edff8fa6e0ec38f6ff2834e837d4f366","source":{"kind":"arxiv","id":"1609.09444","version":2},"attestation_state":"computed","paper":{"title":"Contextual RNN-GANs for Abstract Reasoning Diagram Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Amitabha Mukerjee, Arnab Ghosh, Mohit Bansal, Vinay Namboodiri, Viveka Kulharia","submitted_at":"2016-09-29T17:56:32Z","abstract_excerpt":"Understanding, predicting, and generating object motions and transformations is a core problem in artificial intelligence. Modeling sequences of evolving images may provide better representations and models of motion and may ultimately be used for forecasting, simulation, or video generation. Diagrammatic Abstract Reasoning is an avenue in which diagrams evolve in complex patterns and one needs to infer the underlying pattern sequence and generate the next image in the sequence. For this, we develop a novel Contextual Generative Adversarial Network based on Recurrent Neural Networks (Context-R"},"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":"1609.09444","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-09-29T17:56:32Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"8719be9ac8fe0d4bafa7dd369eb74d70a276893e44ffc744623feabe0cc39d13","abstract_canon_sha256":"cd6fd34f7a0aa9b9ba4c766f421d495088cebdf4e8463c0b75c6d5d6ef854dcf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:55:51.700649Z","signature_b64":"ud8/1VQGZnaCSGybOmpsQ+j28C4zMCjrYzEjfZuF+qZUhcQ5FPXVhloubkYWQoK8shPVmtPTldvq1slrXBwADQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ae061c62abf77bc835c15654bc90a069edff8fa6e0ec38f6ff2834e837d4f366","last_reissued_at":"2026-05-18T00:55:51.700163Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:55:51.700163Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Contextual RNN-GANs for Abstract Reasoning Diagram Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Amitabha Mukerjee, Arnab Ghosh, Mohit Bansal, Vinay Namboodiri, Viveka Kulharia","submitted_at":"2016-09-29T17:56:32Z","abstract_excerpt":"Understanding, predicting, and generating object motions and transformations is a core problem in artificial intelligence. Modeling sequences of evolving images may provide better representations and models of motion and may ultimately be used for forecasting, simulation, or video generation. Diagrammatic Abstract Reasoning is an avenue in which diagrams evolve in complex patterns and one needs to infer the underlying pattern sequence and generate the next image in the sequence. For this, we develop a novel Contextual Generative Adversarial Network based on Recurrent Neural Networks (Context-R"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1609.09444","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":"1609.09444","created_at":"2026-05-18T00:55:51.700236+00:00"},{"alias_kind":"arxiv_version","alias_value":"1609.09444v2","created_at":"2026-05-18T00:55:51.700236+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1609.09444","created_at":"2026-05-18T00:55:51.700236+00:00"},{"alias_kind":"pith_short_12","alias_value":"VYDBYYVL6554","created_at":"2026-05-18T12:30:48.956258+00:00"},{"alias_kind":"pith_short_16","alias_value":"VYDBYYVL6554QNOB","created_at":"2026-05-18T12:30:48.956258+00:00"},{"alias_kind":"pith_short_8","alias_value":"VYDBYYVL","created_at":"2026-05-18T12:30:48.956258+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/VYDBYYVL6554QNOBKZKLZEFANH","json":"https://pith.science/pith/VYDBYYVL6554QNOBKZKLZEFANH.json","graph_json":"https://pith.science/api/pith-number/VYDBYYVL6554QNOBKZKLZEFANH/graph.json","events_json":"https://pith.science/api/pith-number/VYDBYYVL6554QNOBKZKLZEFANH/events.json","paper":"https://pith.science/paper/VYDBYYVL"},"agent_actions":{"view_html":"https://pith.science/pith/VYDBYYVL6554QNOBKZKLZEFANH","download_json":"https://pith.science/pith/VYDBYYVL6554QNOBKZKLZEFANH.json","view_paper":"https://pith.science/paper/VYDBYYVL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1609.09444&json=true","fetch_graph":"https://pith.science/api/pith-number/VYDBYYVL6554QNOBKZKLZEFANH/graph.json","fetch_events":"https://pith.science/api/pith-number/VYDBYYVL6554QNOBKZKLZEFANH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VYDBYYVL6554QNOBKZKLZEFANH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VYDBYYVL6554QNOBKZKLZEFANH/action/storage_attestation","attest_author":"https://pith.science/pith/VYDBYYVL6554QNOBKZKLZEFANH/action/author_attestation","sign_citation":"https://pith.science/pith/VYDBYYVL6554QNOBKZKLZEFANH/action/citation_signature","submit_replication":"https://pith.science/pith/VYDBYYVL6554QNOBKZKLZEFANH/action/replication_record"}},"created_at":"2026-05-18T00:55:51.700236+00:00","updated_at":"2026-05-18T00:55:51.700236+00:00"}