{"id":"W4286436784","doi":"10.18280/isi.270315","title":"A Stacked Autoencoder and Multilayer Perceptrons for mmWave Beamforming Prediction","year":2022,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Beamforming; Autoencoder; Computer science; Benchmark (surveying); Multilayer perceptron; Electronic engineering; Perceptron; Artificial neural network; Artificial intelligence; Computer engineering; Engineering; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005923677,0.001254699,0.0006210009,0.0004344332,0.0002535387,0.0004861231,0.0008698514,0.0008937491,0.001283255],"category_scores_gemma":[0.001339902,0.0005879012,0.0007667,0.0004417786,0.0003004897,0.0007380191,0.0005679217,0.001628515,0.0006178783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005035714,"about_ca_system_score_gemma":0.0007082836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01122097,"about_ca_topic_score_gemma":0.01117191,"domain_scores_codex":[0.9997483,0.00004960935,0.00001804739,0.00007986314,0.00005899318,0.00004527532],"domain_scores_gemma":[0.9996217,0.0001477415,0.00003264809,0.00003986076,0.0001368814,0.00002100731],"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":[0.0001025313,0.00007953396,0.001139697,0.00004398478,0.00008251615,0.00006060834,0.00002514149,0.8604349,0.004996667,0.001081989,0.001696172,0.1302562],"study_design_scores_gemma":[0.00000144672,0.00001340454,0.0001272601,0.000002703861,0.000004624717,0.000003993369,0.000001694814,0.9986406,0.0008239137,0.0002680472,0.0001098511,0.00000247309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05322277,0.0008205561,0.9408464,0.0003406,0.0001531239,0.00004590402,0.0003781513,0.002367929,0.001824484],"genre_scores_gemma":[0.7665952,0.0006295339,0.2253309,0.0002643347,0.0001149274,0.0001549543,0.001336143,0.00009646779,0.005477472],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01122097,"threshold_uncertainty_score":0.02231127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01735707930152057,"score_gpt":0.2144175634223871,"score_spread":0.1970604841208665,"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."}}