{"id":"W2069885283","doi":"10.1109/globalsip.2013.6737140","title":"Selective decoding in associative memories based on Sparse-Clustered Networks","year":2013,"lang":"en","type":"article","venue":"","topic":"Network Packet Processing and Optimization","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Decoding methods; Associative property; Field-programmable gate array; Content-addressable memory; Latency (audio); Parallel computing; Low latency (capital markets); Implementation; Computer engineering; Computer hardware; Computer architecture; Artificial neural network; Algorithm; Artificial intelligence; 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":[0.000116787,0.0001661394,0.0001971002,0.0002805275,0.0001980212,0.0003272762,0.0006281598,0.0002318026,0.001298377],"category_scores_gemma":[0.00052086,0.0001100212,0.0001123476,0.0003847233,0.0002583653,0.0006795621,0.0002778396,0.0002074154,0.0002731247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003324233,"about_ca_system_score_gemma":0.0003815726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00144716,"about_ca_topic_score_gemma":0.003520383,"domain_scores_codex":[0.9999187,0.0000136927,0.000005730536,0.0000144557,0.00003165827,0.00001575028],"domain_scores_gemma":[0.999783,0.00007218065,0.00002769014,0.00003984591,0.00006360147,0.00001370304],"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":[0.0006006063,0.000177581,0.002067416,0.0002750897,0.00006642965,0.0004573638,0.00016147,0.384295,0.1898199,0.09301095,0.004080512,0.3249877],"study_design_scores_gemma":[0.00004498584,0.0002147899,0.000632078,0.00001417455,0.00002227258,0.0002336925,0.00004355589,0.8872126,0.08685453,0.02038523,0.004323413,0.00001858369],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.474228,0.0007608247,0.5114597,0.0002358834,0.00006613063,0.0000600933,0.0001401744,0.001423838,0.01162531],"genre_scores_gemma":[0.9155572,0.0002736269,0.08047935,0.00006954809,0.00002106769,0.00004041074,0.0001045332,0.00002921823,0.003425005],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00144716,"threshold_uncertainty_score":0.00434345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01090045500599036,"score_gpt":0.2219681780495709,"score_spread":0.2110677230435805,"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."}}