{"id":"W2915581179","doi":"10.48550/arxiv.1902.07249","title":"Discovery of Natural Language Concepts in Individual Units of CNNs","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Topic Modeling","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Morpheme; Natural language processing; Artificial intelligence; Natural language; Natural (archaeology); Translation (biology); Machine translation; Linguistics; History","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":[0.0008871678,0.0005628845,0.0004074789,0.000989346,0.0003080971,0.001073503,0.0007290089,0.0006771184,0.001393799],"category_scores_gemma":[0.005046709,0.0004005168,0.0007474344,0.0009792816,0.0008292201,0.002559575,0.0008870271,0.001401271,0.0004088267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001009402,"about_ca_system_score_gemma":0.0006057748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003189851,"about_ca_topic_score_gemma":0.003331144,"domain_scores_codex":[0.9994875,0.0001220052,0.00002318568,0.0002185485,0.00006308204,0.00008562278],"domain_scores_gemma":[0.9987265,0.0006670611,0.0001685987,0.000187693,0.000180433,0.00006958775],"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":[0.001032135,0.0003966134,0.06088881,0.0008104691,0.0004699304,0.0006520225,0.002326867,0.138689,0.1450477,0.1687414,0.01256379,0.4683813],"study_design_scores_gemma":[0.0000309248,0.00008671223,0.01329969,0.00005041996,0.0000730746,0.0001420302,0.0002643839,0.85315,0.01758794,0.1116895,0.003593961,0.00003141231],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.586745,0.001097297,0.4033621,0.001060088,0.0001096905,0.0001063128,0.001206754,0.0008549305,0.005457758],"genre_scores_gemma":[0.9546807,0.0002520584,0.0422403,0.0001290616,0.00003641969,0.00008578071,0.0009489691,0.00006747899,0.001559283],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003189851,"threshold_uncertainty_score":0.007323802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07753615855421307,"score_gpt":0.2152653111743964,"score_spread":0.1377291526201834,"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."}}