{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:ULERJJFHPYGHTKFPEZ5AC4AWEU","short_pith_number":"pith:ULERJJFH","schema_version":"1.0","canonical_sha256":"a2c914a4a77e0c79a8af267a0170162520874299ebff75bce12c5058baa1f339","source":{"kind":"arxiv","id":"1609.08417","version":3},"attestation_state":"computed","paper":{"title":"Learning convolutional neural network to maximize Pos@Top performance measure","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chenhao Xu, Gaoyuan Liang, Jingbin Wang, Jing-Yan Wang, Ru-Ze Liang, Weizhi Li, Yanyan Geng","submitted_at":"2016-09-27T13:27:40Z","abstract_excerpt":"In the machine learning problems, the performance measure is used to evaluate the machine learning models. Recently, the number positive data points ranked at the top positions (Pos@Top) has been a popular performance measure in the machine learning community. In this paper, we propose to learn a convolutional neural network (CNN) model to maximize the Pos@Top performance measure. The CNN model is used to represent the multi-instance data point, and a classifier function is used to predict the label from the its CNN representation. We propose to minimize the loss function of Pos@Top over a tra"},"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":"1609.08417","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-09-27T13:27:40Z","cross_cats_sorted":[],"title_canon_sha256":"e42a158a396b65726b7109a96bbf634b8910f4edc51af8c8be01befba246c100","abstract_canon_sha256":"9224151662c3955ff898ab0a980bf05365d8461239d9cceabc5b9e7ac751e7f3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:49:46.822602Z","signature_b64":"e240XrWOrHTQXuoxTnPeYoLAeTj8kfY+BW3T2LfPfckDAZY2kWjcuO8xZBsVnBwRtH3GIjuV/04RJMJY1SkGCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a2c914a4a77e0c79a8af267a0170162520874299ebff75bce12c5058baa1f339","last_reissued_at":"2026-05-18T00:49:46.821968Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:49:46.821968Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning convolutional neural network to maximize Pos@Top performance measure","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chenhao Xu, Gaoyuan Liang, Jingbin Wang, Jing-Yan Wang, Ru-Ze Liang, Weizhi Li, Yanyan Geng","submitted_at":"2016-09-27T13:27:40Z","abstract_excerpt":"In the machine learning problems, the performance measure is used to evaluate the machine learning models. Recently, the number positive data points ranked at the top positions (Pos@Top) has been a popular performance measure in the machine learning community. In this paper, we propose to learn a convolutional neural network (CNN) model to maximize the Pos@Top performance measure. The CNN model is used to represent the multi-instance data point, and a classifier function is used to predict the label from the its CNN representation. We propose to minimize the loss function of Pos@Top over a tra"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1609.08417","kind":"arxiv","version":3},"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":"1609.08417","created_at":"2026-05-18T00:49:46.822072+00:00"},{"alias_kind":"arxiv_version","alias_value":"1609.08417v3","created_at":"2026-05-18T00:49:46.822072+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1609.08417","created_at":"2026-05-18T00:49:46.822072+00:00"},{"alias_kind":"pith_short_12","alias_value":"ULERJJFHPYGH","created_at":"2026-05-18T12:30:46.583412+00:00"},{"alias_kind":"pith_short_16","alias_value":"ULERJJFHPYGHTKFP","created_at":"2026-05-18T12:30:46.583412+00:00"},{"alias_kind":"pith_short_8","alias_value":"ULERJJFH","created_at":"2026-05-18T12:30:46.583412+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/ULERJJFHPYGHTKFPEZ5AC4AWEU","json":"https://pith.science/pith/ULERJJFHPYGHTKFPEZ5AC4AWEU.json","graph_json":"https://pith.science/api/pith-number/ULERJJFHPYGHTKFPEZ5AC4AWEU/graph.json","events_json":"https://pith.science/api/pith-number/ULERJJFHPYGHTKFPEZ5AC4AWEU/events.json","paper":"https://pith.science/paper/ULERJJFH"},"agent_actions":{"view_html":"https://pith.science/pith/ULERJJFHPYGHTKFPEZ5AC4AWEU","download_json":"https://pith.science/pith/ULERJJFHPYGHTKFPEZ5AC4AWEU.json","view_paper":"https://pith.science/paper/ULERJJFH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1609.08417&json=true","fetch_graph":"https://pith.science/api/pith-number/ULERJJFHPYGHTKFPEZ5AC4AWEU/graph.json","fetch_events":"https://pith.science/api/pith-number/ULERJJFHPYGHTKFPEZ5AC4AWEU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ULERJJFHPYGHTKFPEZ5AC4AWEU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ULERJJFHPYGHTKFPEZ5AC4AWEU/action/storage_attestation","attest_author":"https://pith.science/pith/ULERJJFHPYGHTKFPEZ5AC4AWEU/action/author_attestation","sign_citation":"https://pith.science/pith/ULERJJFHPYGHTKFPEZ5AC4AWEU/action/citation_signature","submit_replication":"https://pith.science/pith/ULERJJFHPYGHTKFPEZ5AC4AWEU/action/replication_record"}},"created_at":"2026-05-18T00:49:46.822072+00:00","updated_at":"2026-05-18T00:49:46.822072+00:00"}