{"id":"W2560977758","doi":"","title":"Learning Deep Parsimonious Representations","year":2016,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":90,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; University of Toronto","funders":"","keywords":"Interpretability; Cluster analysis; Conceptual clustering; Computer science; Artificial intelligence; Categorization; Machine learning; Generalization; Deep learning; Regularization (linguistics); Correlation clustering; Canopy clustering algorithm; Mathematics","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":[0.0009781884,0.001172544,0.0009653833,0.0007011771,0.0004331194,0.001093474,0.00177033,0.001685373,0.002168514],"category_scores_gemma":[0.004189286,0.0005603942,0.0007250333,0.0008227144,0.001167264,0.003566892,0.002200895,0.003569293,0.000679724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008893555,"about_ca_system_score_gemma":0.0006797973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001157846,"about_ca_topic_score_gemma":0.002951539,"domain_scores_codex":[0.9994311,0.0001681111,0.00002151824,0.0001993049,0.0001066036,0.00007324876],"domain_scores_gemma":[0.9988074,0.0005649392,0.0001237447,0.0003416342,0.00008808426,0.00007420834],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000220045,0.0002333644,0.001727002,0.0002834492,0.0001665869,0.0002855475,0.0003667932,0.564895,0.0238469,0.1280596,0.006059898,0.2738557],"study_design_scores_gemma":[0.000008910661,0.00004644378,0.0001304562,0.00001520136,0.00001269959,0.00004069737,0.0000232723,0.9180493,0.001664284,0.07910993,0.0008909972,0.000007818681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04978753,0.0003044044,0.9469465,0.00049483,0.00003889693,0.00003335889,0.0001631658,0.0005729409,0.001658401],"genre_scores_gemma":[0.7629579,0.0004941889,0.2286166,0.0006550744,0.00009633833,0.0001351603,0.0007656937,0.0002351506,0.006043991],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002168514,"threshold_uncertainty_score":0.007254362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0174282915545141,"score_gpt":0.2555935385113803,"score_spread":0.2381652469568663,"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."}}