{"id":"W4388509231","doi":"10.36227/techrxiv.24495202.v1","title":"Subspace Rotation Algorithm for Training Restricted Hopfield Network","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Subspace topology; Content-addressable memory; Rotation (mathematics); Bidirectional associative memory; Associative property; Algorithm; Hopfield network; Computer science; Arithmetic; Artificial intelligence; Artificial neural network; Mathematics; Pure mathematics","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":[0.0006315606,0.0007730953,0.0006090077,0.0005490897,0.00039532,0.0004716511,0.0008566407,0.0006321805,0.00346099],"category_scores_gemma":[0.001876976,0.000308779,0.00056536,0.0006324044,0.0004326173,0.0009443419,0.0005449675,0.0008581546,0.000856526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004290383,"about_ca_system_score_gemma":0.001056214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008461703,"about_ca_topic_score_gemma":0.006704717,"domain_scores_codex":[0.9997297,0.00007883684,0.00002164258,0.00006824679,0.000065072,0.00003650675],"domain_scores_gemma":[0.9996191,0.0001237591,0.00003204115,0.00005823768,0.0001487958,0.00001799593],"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":[0.0000813701,0.00004526683,0.0008307623,0.00009669365,0.00005923399,0.0000498862,0.00008367412,0.6453601,0.007817631,0.008532131,0.0019867,0.3350566],"study_design_scores_gemma":[0.000006521444,0.00002408515,0.00008766984,0.000005056963,0.000004864361,0.00001513357,0.000006113116,0.9953719,0.001639799,0.002065323,0.0007680879,0.000005465942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01271366,0.0003229942,0.9841022,0.00006397313,0.00004172374,0.00005665619,0.00005831568,0.0007779069,0.001862707],"genre_scores_gemma":[0.4354971,0.0005492602,0.5574508,0.0001149877,0.00004704581,0.000387528,0.0005695951,0.0001900423,0.005193726],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008461703,"threshold_uncertainty_score":0.0168249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09104006936496757,"score_gpt":0.309122514165711,"score_spread":0.2180824448007434,"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."}}