{"id":"W4392906087","doi":"10.32920/25412848.v1","title":"Understanding, Interpreting and Learning Representations in Deep Neural Networks","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Manitoba; York University","funders":"","keywords":"Interpretability; Computer science; Pooling; ENCODE; Artificial intelligence; Position (finance); Convolutional neural network; Representation (politics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001439921,0.0009308725,0.0004163875,0.0006277445,0.0003097682,0.002065529,0.001221562,0.001038642,0.001119814],"category_scores_gemma":[0.008200132,0.0006497694,0.000542215,0.0007028888,0.001556217,0.004392894,0.00119285,0.00229667,0.000329863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001365964,"about_ca_system_score_gemma":0.0007148434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004572165,"about_ca_topic_score_gemma":0.004988853,"domain_scores_codex":[0.9993162,0.000255632,0.00004563545,0.0001807292,0.000145643,0.0000561248],"domain_scores_gemma":[0.9980206,0.001169357,0.0002711765,0.0002961988,0.0002013661,0.00004128897],"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":[0.0001243157,0.0000761788,0.002490873,0.0003522912,0.0000822886,0.0002183031,0.0005761715,0.5689023,0.02149005,0.1572434,0.002866962,0.2455768],"study_design_scores_gemma":[0.000004350048,0.00001939803,0.0002776149,0.00003087717,0.000009825803,0.00002063,0.00005172315,0.9067098,0.004545265,0.08720372,0.001117433,0.000009284325],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04575233,0.0007034068,0.9496782,0.0008965995,0.00006340806,0.00003961479,0.0002729362,0.0006213685,0.00197208],"genre_scores_gemma":[0.6595258,0.001706008,0.3343,0.0003875538,0.0000786432,0.0001135936,0.0006586689,0.000159048,0.003070742],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004572165,"threshold_uncertainty_score":0.009910762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05810391097385165,"score_gpt":0.3168739290633024,"score_spread":0.2587700180894507,"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."}}