{"id":"W2744816398","doi":"10.1145/3063593","title":"A Tucker Deep Computation Model for Mobile Multimedia Feature Learning","year":2017,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Computation; Speedup; Computer science; Tucker decomposition; Deep learning; Artificial intelligence; Feature (linguistics); Server; Machine learning; Parallel computing; Algorithm; Tensor (intrinsic definition); Tensor decomposition; Mathematics; Computer network","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":[0.000349733,0.0007877193,0.0006393382,0.0005066782,0.0003535318,0.0007022819,0.001452087,0.0007910568,0.003032681],"category_scores_gemma":[0.001210742,0.0002884243,0.0006794161,0.0007567338,0.000475877,0.001948415,0.0008790419,0.001638148,0.0009082659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001160606,"about_ca_system_score_gemma":0.001348924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01228137,"about_ca_topic_score_gemma":0.01474538,"domain_scores_codex":[0.9997547,0.00003318793,0.00001552119,0.0000741642,0.00007403955,0.00004835705],"domain_scores_gemma":[0.999777,0.00005303003,0.00001989256,0.00004666603,0.00008097165,0.00002251072],"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":[0.0002459932,0.0001552376,0.00115149,0.0001110244,0.00006776112,0.0001694916,0.00008012334,0.6286575,0.01966866,0.03646176,0.01159817,0.3016329],"study_design_scores_gemma":[0.000002669742,0.00001266154,0.00004473738,0.000002086945,0.000003952145,0.00001052087,0.000002556359,0.9959834,0.001150979,0.002201153,0.0005824608,0.000002886679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01954766,0.0004293445,0.9750128,0.0004237049,0.00009435593,0.00005703134,0.000189005,0.001603604,0.002642499],"genre_scores_gemma":[0.5642455,0.001116605,0.4203466,0.0005670887,0.0001251464,0.0002960246,0.001266552,0.0002399208,0.01179651],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01228137,"threshold_uncertainty_score":0.02441972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06013025682110174,"score_gpt":0.3723619049408898,"score_spread":0.3122316481197881,"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."}}