{"id":"W1853900790","doi":"10.48550/arxiv.1312.5663","title":"k-Sparse Autoencoders","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":148,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Autoencoder; MNIST database; Dropout (neural networks); Pattern recognition (psychology); Neural coding; Computer science; Artificial intelligence; Encoding (memory); Noise reduction; Sparse approximation; Machine learning; Algorithm; Deep learning","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":[0.0009252566,0.0007307919,0.001068728,0.0005951155,0.0002863117,0.0006147047,0.0009678731,0.0009631861,0.002124679],"category_scores_gemma":[0.004121462,0.0004863924,0.0006794293,0.000768287,0.0006907096,0.001405042,0.0008656406,0.001359747,0.00124346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004552616,"about_ca_system_score_gemma":0.0006050233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002622939,"about_ca_topic_score_gemma":0.004460242,"domain_scores_codex":[0.9994776,0.0001683397,0.00003417492,0.000143375,0.0001277413,0.00004878725],"domain_scores_gemma":[0.9984499,0.0008212784,0.0001071899,0.0003110371,0.0002709405,0.00003964966],"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":[0.0001714234,0.0001388352,0.001314356,0.0002639317,0.0001774751,0.0001162153,0.0001595097,0.5902551,0.009776404,0.04850798,0.01021098,0.3389078],"study_design_scores_gemma":[0.000005487563,0.00001173668,0.0001743534,0.000008305586,0.000007117306,0.00002312215,0.000008131669,0.9877009,0.001065477,0.0100508,0.0009398102,0.000004821964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01362766,0.0005333334,0.9832367,0.0001813495,0.0000508904,0.00003111871,0.0001433227,0.0006153468,0.001580212],"genre_scores_gemma":[0.5004562,0.001450547,0.4866527,0.0004495196,0.0001967399,0.0002036444,0.001692437,0.0002222013,0.008675918],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002622939,"threshold_uncertainty_score":0.007107735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09595176305524669,"score_gpt":0.1851467634226563,"score_spread":0.08919500036740957,"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."}}