{"id":"W2963420686","doi":"10.1109/tpami.2019.2913372","title":"Squeeze-and-Excitation Networks","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Force Microscopy Techniques and Applications","field":"Physics and Astronomy","cited_by":12510,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; Engineering and Physical Sciences Research Council; Canadian Institute for Advanced Research; Chinese Academy of Sciences; Universidade de Macau; National Natural Science Foundation of China; University of Manchester","keywords":"Computer science; Artificial intelligence","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":[0.0007783719,0.001600426,0.001101088,0.0004898725,0.000451743,0.0009983216,0.002183612,0.001445318,0.008831217],"category_scores_gemma":[0.003105957,0.0007599566,0.001026419,0.0005499112,0.001103401,0.003442608,0.002755399,0.002400953,0.002345877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007967319,"about_ca_system_score_gemma":0.0009094251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002592351,"about_ca_topic_score_gemma":0.0076508,"domain_scores_codex":[0.9995599,0.00007390146,0.00002182909,0.0001476304,0.0001219955,0.00007465866],"domain_scores_gemma":[0.9994129,0.000242165,0.00005187773,0.0001553702,0.00009097821,0.00004678572],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001024955,0.0002051509,0.001986135,0.0003951104,0.0002543289,0.0004789997,0.0001998129,0.4482591,0.05078336,0.06856023,0.0216033,0.4062495],"study_design_scores_gemma":[0.00003622296,0.0001112804,0.0003596495,0.00003570442,0.00005464608,0.0001372354,0.00002819449,0.9477742,0.01461175,0.02882206,0.007993943,0.00003515305],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0650686,0.001834294,0.9075657,0.001173491,0.0003673941,0.0001355021,0.001099359,0.008757467,0.01399833],"genre_scores_gemma":[0.6795006,0.001234558,0.2839606,0.001464579,0.0001900651,0.0003050201,0.002832015,0.000853483,0.02965908],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008831217,"threshold_uncertainty_score":0.02954334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008529432180569783,"score_gpt":0.2667022275782162,"score_spread":0.2581727953976464,"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."}}