{"id":"W3122623456","doi":"10.1016/j.neunet.2020.12.009","title":"Words as a window: Using word embeddings to explore the learned representations of Convolutional Neural Networks","year":2021,"lang":"en","type":"article","venue":"Neural Networks","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta; University of Victoria","funders":"Western Canada Research Grid; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Window (computing); Hierarchy; Word (group theory); Artificial neural network; Machine learning; Deep learning; Natural language processing","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006711102,0.0008989041,0.0007015968,0.0006880171,0.0002020023,0.0007510791,0.0008382027,0.0009068366,0.001904194],"category_scores_gemma":[0.003454098,0.0004549879,0.0005352017,0.0007985107,0.000659509,0.00347774,0.001593179,0.001486746,0.0004949676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003108549,"about_ca_system_score_gemma":0.00038675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001959411,"about_ca_topic_score_gemma":0.00251485,"domain_scores_codex":[0.9997732,0.00007805206,0.00001279674,0.00007376027,0.00003426915,0.00002793562],"domain_scores_gemma":[0.9990252,0.0006098705,0.0000842119,0.0001575377,0.00007433263,0.00004885305],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001217966,0.0003045341,0.00464465,0.0003160607,0.0001968969,0.000283217,0.0005343838,0.4088718,0.03769173,0.03506203,0.008079269,0.5027975],"study_design_scores_gemma":[0.0000149977,0.00005245256,0.0002445304,0.00001274984,0.00001908828,0.00002043338,0.00003995536,0.9751326,0.002396767,0.0213111,0.0007451231,0.00001012328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1603539,0.001265254,0.8344195,0.0004449428,0.0001472645,0.00005637817,0.000390444,0.00149066,0.001431621],"genre_scores_gemma":[0.8515296,0.0007847833,0.1421682,0.0002432388,0.0001000038,0.0001063926,0.0009911805,0.00040054,0.003676102],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001959411,"threshold_uncertainty_score":0.006370127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05232488598892367,"score_gpt":0.332143349806977,"score_spread":0.2798184638180534,"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."}}