{"id":"W4312928961","doi":"10.1109/pn56061.2022.9908251","title":"Nonlinear Dimensionality Reduction for Low Data Regimes in Photonics Design","year":2022,"lang":"en","type":"article","venue":"2022 Photonics North (PN)","topic":"Photonic and Optical Devices","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"National Research Council","keywords":"Dimensionality reduction; Principal component analysis; Curse of dimensionality; Linear subspace; Photonics; Initialization; Computer science; Nonlinear dimensionality reduction; Nonlinear system; Artificial neural network; Autoencoder; Reduction (mathematics); Subspace topology; Component (thermodynamics); Algorithm; Artificial intelligence; Mathematics; Physics; Optics","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":[0.0008009135,0.0004096942,0.0003866161,0.0003065033,0.0004226219,0.0008599419,0.0003308021,0.0005604249,0.001654147],"category_scores_gemma":[0.00299687,0.0002938409,0.0003402563,0.0003298296,0.001004059,0.001022934,0.001137681,0.00137987,0.0005661052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006406493,"about_ca_system_score_gemma":0.0005602599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008542353,"about_ca_topic_score_gemma":0.001290866,"domain_scores_codex":[0.9996245,0.0001558265,0.00001829913,0.00004941086,0.0001273707,0.00002446859],"domain_scores_gemma":[0.9992274,0.0004396923,0.00006493804,0.0001303559,0.0001143138,0.00002323444],"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":[0.000156659,0.0001162092,0.001265189,0.0003703794,0.00007368118,0.0001251582,0.0003044048,0.4860972,0.0663719,0.1969251,0.006073828,0.2421203],"study_design_scores_gemma":[0.000003882532,0.00002006198,0.0002133782,0.0000164777,0.000004067525,0.00002913091,0.0000106591,0.9554484,0.01036487,0.03190401,0.001973633,0.00001140665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03222201,0.001423127,0.9599082,0.001160477,0.00006849784,0.00003285635,0.00007009401,0.0003607638,0.00475392],"genre_scores_gemma":[0.461739,0.001850361,0.5303575,0.0002629423,0.0001445022,0.0001979226,0.0001908814,0.0001843235,0.005072622],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001654147,"threshold_uncertainty_score":0.005533695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03484564581594336,"score_gpt":0.2540906194714531,"score_spread":0.2192449736555098,"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."}}