{"id":"W2248206634","doi":"","title":"Position Paper: Representation Search through Generate and Test","year":2013,"lang":"en","type":"article","venue":"Symposium on Abstraction, Reformulation and Approximation","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Representation (politics); Artificial intelligence; Machine learning; Feature learning; Search problem; Simple (philosophy); Artificial neural network; Element (criminal law); Theoretical computer science; Algorithm","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":[0.004881307,0.001100586,0.001266032,0.0009628958,0.000859407,0.00272419,0.003029348,0.003122641,0.01509842],"category_scores_gemma":[0.02839754,0.0005347329,0.0008767353,0.001139665,0.002066751,0.007429593,0.001900445,0.003272427,0.004638122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001510054,"about_ca_system_score_gemma":0.001791664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001948378,"about_ca_topic_score_gemma":0.001518063,"domain_scores_codex":[0.9972658,0.001211628,0.0001050433,0.000644381,0.0006214345,0.0001516741],"domain_scores_gemma":[0.9875416,0.008439149,0.0004286664,0.001980929,0.001198093,0.0004115447],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000494512,0.0004364566,0.001795145,0.0002828797,0.0001181939,0.0001163973,0.0001966753,0.1156893,0.003117246,0.1788008,0.08833967,0.6106128],"study_design_scores_gemma":[0.0001467755,0.0003397799,0.0004222451,0.00009128913,0.00006440333,0.0002193558,0.00005491546,0.79935,0.008358265,0.153681,0.03720992,0.00006215139],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"commentary","genre_scores_codex":[0.01141408,0.00141657,0.9677923,0.004585307,0.001126055,0.0001863816,0.0002789828,0.002594312,0.01060609],"genre_scores_gemma":[0.2759663,0.00151001,0.6810084,0.003276294,0.002226846,0.0004277054,0.002250452,0.002058638,0.03127537],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.01509842,"threshold_uncertainty_score":0.05050927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02071115116405118,"score_gpt":0.2661946017112756,"score_spread":0.2454834505472244,"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."}}