{"id":"W2949803069","doi":"10.48550/arxiv.1704.02345","title":"Fast Spectral Clustering Using Autoencoders and Landmarks","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Spectral clustering; Adjacency matrix; Cluster analysis; Laplacian matrix; Autoencoder; Computer science; Computational complexity theory; Graph; Adjacency list; Matrix decomposition; Pattern recognition (psychology); Spectral graph theory; Eigenvalues and eigenvectors; Artificial intelligence; Algorithm; Matrix (chemical analysis); Theoretical computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001240881,0.0002070152,0.0002145788,0.0001542141,0.000313823,0.0003175398,0.0008832712,0.000207014,0.00001603556],"category_scores_gemma":[0.00001325703,0.000230721,0.00009115959,0.00007372477,0.00008569114,0.0005260045,0.001954234,0.0003710631,0.00001904747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007776995,"about_ca_system_score_gemma":0.00008729883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000280801,"about_ca_topic_score_gemma":0.00007912693,"domain_scores_codex":[0.9987926,0.0000541676,0.0001039087,0.0007237533,0.0000601401,0.0002653623],"domain_scores_gemma":[0.9988784,0.00002657203,0.000184444,0.0007286094,0.00004939068,0.000132637],"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":[0.00007480648,0.00009918009,0.009120156,0.0003375978,0.0001686264,0.001178608,0.001379998,0.9700941,0.0006691126,0.008907117,0.0006892855,0.007281402],"study_design_scores_gemma":[0.0002990821,0.00001588538,0.001059514,0.0002141768,0.00002797676,0.00001363772,0.00005930008,0.9883589,0.00009482585,0.00945004,0.0001148304,0.0002918258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3193611,0.00003015695,0.6771232,0.00006153524,0.0004757698,0.0001047178,0.000006158099,0.0001067562,0.002730579],"genre_scores_gemma":[0.9882962,0.0001580823,0.01078458,0.00004412925,0.00006853613,2.436456e-7,0.000006420344,0.00001016281,0.0006316099],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6689352,"threshold_uncertainty_score":0.9408528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09574453108513345,"score_gpt":0.2059066529691377,"score_spread":0.1101621218840042,"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."}}