{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:3P6AKCZCL6UZ5ICFOEDSUYYDIP","short_pith_number":"pith:3P6AKCZC","schema_version":"1.0","canonical_sha256":"dbfc050b225fa99ea04571072a630343d5713635586c3673a986bddcb5f75b49","source":{"kind":"arxiv","id":"1806.02934","version":1},"attestation_state":"computed","paper":{"title":"Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.CV","cs.LG"],"primary_cat":"stat.ML","authors_text":"Anitha Kannan, Ashwin Kalyan, Dhruv Batra, Stefan Lee","submitted_at":"2018-06-08T01:18:10Z","abstract_excerpt":"Many structured prediction problems (particularly in vision and language domains) are ambiguous, with multiple outputs being correct for an input - e.g. there are many ways of describing an image, multiple ways of translating a sentence; however, exhaustively annotating the applicability of all possible outputs is intractable due to exponentially large output spaces (e.g. all English sentences). In practice, these problems are cast as multi-class prediction, with the likelihood of only a sparse set of annotations being maximized - unfortunately penalizing for placing beliefs on plausible but u"},"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":"1806.02934","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-06-08T01:18:10Z","cross_cats_sorted":["cs.CL","cs.CV","cs.LG"],"title_canon_sha256":"1c111e12df715c40b9c597622b5248e111a9bc96b73998c59738da9e649fd073","abstract_canon_sha256":"194841bf6a27ce9064e734f16866a18662dd47c64bcd5847936d7c357244b468"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:13:54.247582Z","signature_b64":"m8vUbWIectcowmXic33VdKL0tFy3VWHdK+9NeAuBSmO60JOxRDbnlfyXLUwFyT20A1N9bEGzrUwLkvoKtMg/BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dbfc050b225fa99ea04571072a630343d5713635586c3673a986bddcb5f75b49","last_reissued_at":"2026-05-18T00:13:54.246944Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:13:54.246944Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.CV","cs.LG"],"primary_cat":"stat.ML","authors_text":"Anitha Kannan, Ashwin Kalyan, Dhruv Batra, Stefan Lee","submitted_at":"2018-06-08T01:18:10Z","abstract_excerpt":"Many structured prediction problems (particularly in vision and language domains) are ambiguous, with multiple outputs being correct for an input - e.g. there are many ways of describing an image, multiple ways of translating a sentence; however, exhaustively annotating the applicability of all possible outputs is intractable due to exponentially large output spaces (e.g. all English sentences). In practice, these problems are cast as multi-class prediction, with the likelihood of only a sparse set of annotations being maximized - unfortunately penalizing for placing beliefs on plausible but u"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.02934","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":"1806.02934","created_at":"2026-05-18T00:13:54.247058+00:00"},{"alias_kind":"arxiv_version","alias_value":"1806.02934v1","created_at":"2026-05-18T00:13:54.247058+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.02934","created_at":"2026-05-18T00:13:54.247058+00:00"},{"alias_kind":"pith_short_12","alias_value":"3P6AKCZCL6UZ","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_16","alias_value":"3P6AKCZCL6UZ5ICF","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_8","alias_value":"3P6AKCZC","created_at":"2026-05-18T12:32:02.567920+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/3P6AKCZCL6UZ5ICFOEDSUYYDIP","json":"https://pith.science/pith/3P6AKCZCL6UZ5ICFOEDSUYYDIP.json","graph_json":"https://pith.science/api/pith-number/3P6AKCZCL6UZ5ICFOEDSUYYDIP/graph.json","events_json":"https://pith.science/api/pith-number/3P6AKCZCL6UZ5ICFOEDSUYYDIP/events.json","paper":"https://pith.science/paper/3P6AKCZC"},"agent_actions":{"view_html":"https://pith.science/pith/3P6AKCZCL6UZ5ICFOEDSUYYDIP","download_json":"https://pith.science/pith/3P6AKCZCL6UZ5ICFOEDSUYYDIP.json","view_paper":"https://pith.science/paper/3P6AKCZC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1806.02934&json=true","fetch_graph":"https://pith.science/api/pith-number/3P6AKCZCL6UZ5ICFOEDSUYYDIP/graph.json","fetch_events":"https://pith.science/api/pith-number/3P6AKCZCL6UZ5ICFOEDSUYYDIP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3P6AKCZCL6UZ5ICFOEDSUYYDIP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3P6AKCZCL6UZ5ICFOEDSUYYDIP/action/storage_attestation","attest_author":"https://pith.science/pith/3P6AKCZCL6UZ5ICFOEDSUYYDIP/action/author_attestation","sign_citation":"https://pith.science/pith/3P6AKCZCL6UZ5ICFOEDSUYYDIP/action/citation_signature","submit_replication":"https://pith.science/pith/3P6AKCZCL6UZ5ICFOEDSUYYDIP/action/replication_record"}},"created_at":"2026-05-18T00:13:54.247058+00:00","updated_at":"2026-05-18T00:13:54.247058+00:00"}