{"id":"W4309166643","doi":"10.1109/tpami.2022.3222104","title":"Geometry Regularized Autoencoders","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données; Compute Canada; National Institute of General Medical Sciences; Canadian Institute for Advanced Research; National Institutes of Health; National Science Foundation","keywords":"Autoencoder; Artificial intelligence; Nonlinear dimensionality reduction; Feature learning; External Data Representation; Computer science; Representation (politics); Regularization (linguistics); Embedding; Kernel (algebra); Pattern recognition (psychology); Manifold alignment; Manifold (fluid mechanics); Kernel method; Deep learning; Invertible matrix; Machine learning; Mathematics; Dimensionality reduction; Support vector machine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001132681,0.001025174,0.0009724133,0.0004888675,0.0002603964,0.0007688016,0.001189442,0.001186933,0.001954527],"category_scores_gemma":[0.003653811,0.0005647654,0.0008827636,0.0004961142,0.000966275,0.001366723,0.00100214,0.001827195,0.001007874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007470058,"about_ca_system_score_gemma":0.0008311927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003738067,"about_ca_topic_score_gemma":0.005058232,"domain_scores_codex":[0.9994665,0.0001360073,0.00003080257,0.0001430897,0.0001651516,0.00005838381],"domain_scores_gemma":[0.9985034,0.0007567828,0.0001054316,0.0002679194,0.0003297933,0.00003659253],"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":[0.00005039602,0.00003498979,0.0004631548,0.0000587303,0.00005596587,0.00004743397,0.00005011239,0.8808969,0.005076574,0.02107284,0.001892488,0.09030047],"study_design_scores_gemma":[0.000001854434,0.000006886893,0.00006266209,0.000003132973,0.000002904199,0.000009070453,0.000002772033,0.9953403,0.0005660795,0.003675598,0.0003259312,0.000002835933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01068234,0.0002289117,0.9868985,0.0001352024,0.00004464398,0.00002126763,0.00008167401,0.0005289713,0.001378433],"genre_scores_gemma":[0.5626521,0.0007841274,0.4243729,0.0004144309,0.0001586401,0.0002047872,0.0008596228,0.0003012462,0.01025215],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003738067,"threshold_uncertainty_score":0.00743264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009991200244593062,"score_gpt":0.2626437980890731,"score_spread":0.2526525978444801,"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."}}