{"id":"W4207006620","doi":"10.1145/3491223","title":"TT-TSVD: A Multi-modal Tensor Train Decomposition with Its Application in Convolutional Neural Networks for Smart Healthcare","year":2022,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"National Natural Science Foundation of China","keywords":"Singular value decomposition; Tensor (intrinsic definition); Convolutional neural network; Computer science; Reduction (mathematics); Modalities; Decomposition; Data mining; Tensor decomposition; Artificial intelligence; Big data; Pattern recognition (psychology); Algorithm; Mathematics; Chemistry","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":[0.001074355,0.001124866,0.0007075762,0.001075374,0.0004509232,0.0009147279,0.0009526705,0.000712481,0.002394801],"category_scores_gemma":[0.002216998,0.0004025455,0.001116894,0.001352421,0.0006654749,0.00147987,0.001275273,0.001722776,0.0007969357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007632952,"about_ca_system_score_gemma":0.001691628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009109388,"about_ca_topic_score_gemma":0.01280126,"domain_scores_codex":[0.9995477,0.00008833519,0.0000434369,0.00008737884,0.0001839893,0.00004911916],"domain_scores_gemma":[0.9993906,0.0001535887,0.00006304302,0.0001067963,0.0002352453,0.00005070846],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002238734,0.0001569659,0.001881245,0.0002114791,0.0001637666,0.0001417024,0.0001341318,0.1942033,0.02590459,0.0166553,0.01113639,0.7491872],"study_design_scores_gemma":[0.000007525276,0.00004455078,0.0002714008,0.00001122272,0.00001645425,0.00005627827,0.00001806015,0.9846374,0.007552324,0.004459015,0.002912051,0.00001367416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007484844,0.0003432424,0.9901904,0.0002005614,0.00008290251,0.00004716116,0.000147319,0.0009171143,0.0005865035],"genre_scores_gemma":[0.163059,0.001153331,0.8278621,0.0002230234,0.00009501012,0.0001639334,0.00162358,0.0003577843,0.005462074],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009109388,"threshold_uncertainty_score":0.01811272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05554688708277145,"score_gpt":0.3520555960012822,"score_spread":0.2965087089185107,"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."}}