{"id":"W2256558689","doi":"10.48550/arxiv.1511.05616","title":"Learning Structured Inference Neural Networks with Label Relations","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Categorization; Abstraction; Exploit; Artificial intelligence; Benchmark (surveying); Semantics (computer science); Inference; Set (abstract data type); Multi-label classification; Encoding (memory); Image (mathematics); Machine learning; Artificial neural network; Pattern recognition (psychology); Deep learning; Contextual image classification","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001067478,0.001317847,0.0009380276,0.0007752277,0.0004845698,0.001047524,0.002101234,0.001903626,0.001688968],"category_scores_gemma":[0.004904986,0.0006195862,0.000782668,0.001010153,0.0008622338,0.003090679,0.001233786,0.002858859,0.0006243513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001377381,"about_ca_system_score_gemma":0.0009279348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007346897,"about_ca_topic_score_gemma":0.0137297,"domain_scores_codex":[0.9994411,0.0001466528,0.00002354998,0.0002343202,0.00009769017,0.00005665305],"domain_scores_gemma":[0.9980428,0.001047598,0.0002524977,0.0003046929,0.0002760342,0.00007638748],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002686998,0.0003818805,0.003153925,0.0001397327,0.0001135784,0.0001402913,0.0001865699,0.7134805,0.004663994,0.02270293,0.007869371,0.2468985],"study_design_scores_gemma":[0.000006496041,0.00001043827,0.00006285018,0.000003946923,0.000005169838,0.000003747221,0.000004908906,0.9883453,0.0002965085,0.01114015,0.0001184662,0.000002082955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08489721,0.000666974,0.9072621,0.0009464125,0.0001021702,0.00007740975,0.0005285158,0.002321423,0.003197752],"genre_scores_gemma":[0.7703479,0.0003374788,0.2195213,0.0006712534,0.0002121207,0.0001810695,0.002581944,0.0001530252,0.005993862],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007346897,"threshold_uncertainty_score":0.01460826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07096644628058629,"score_gpt":0.2247982694540282,"score_spread":0.1538318231734419,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}