{"id":"W2529958663","doi":"10.1109/tcad.2017.2681075","title":"FALCON: Feature Driven Selective Classification for Energy-Efficient Image Recognition","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Defense Advanced Research Projects Agency; Microelectronics Advanced Research Corporation; Canadian Institute for Advanced Research; Intel Corporation; Semiconductor Research Corporation; National Science Foundation","keywords":"Computer science; Classifier (UML); Artificial intelligence; Scalability; Pattern recognition (psychology); Machine learning; Contextual image classification; Modular design; AdaBoost; Data mining; Image (mathematics); Database","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":[0.0002921576,0.0005604524,0.0003974741,0.0004028685,0.0002642862,0.0005707086,0.001936032,0.0005514465,0.003196994],"category_scores_gemma":[0.0007988704,0.0002194082,0.0003486847,0.0005547868,0.0002871789,0.001257727,0.0005727734,0.0006517302,0.001030279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006578596,"about_ca_system_score_gemma":0.0006942823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002877326,"about_ca_topic_score_gemma":0.006187181,"domain_scores_codex":[0.9997821,0.000022792,0.00001016908,0.00005361357,0.00009298848,0.00003833669],"domain_scores_gemma":[0.9997287,0.00007745908,0.00002665708,0.00006428121,0.00008513295,0.00001773446],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004447619,0.0004485389,0.00344141,0.0003602129,0.0001524343,0.0003384035,0.0001253517,0.1345603,0.1386008,0.01619048,0.04775931,0.6575781],"study_design_scores_gemma":[0.00003553664,0.0001536544,0.0008182588,0.00001285851,0.00002746287,0.0001496604,0.00002535636,0.9316602,0.04870686,0.007386313,0.01100127,0.00002260836],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08861797,0.001037571,0.8813393,0.0004873954,0.0002267438,0.0001894046,0.0006343533,0.01863003,0.00883727],"genre_scores_gemma":[0.6715219,0.0004342538,0.3166202,0.0006749346,0.00006778421,0.0004005771,0.001736914,0.0007407928,0.007802529],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003196994,"threshold_uncertainty_score":0.01069498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05555985660566954,"score_gpt":0.2549462936519125,"score_spread":0.199386437046243,"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."}}