{"id":"W4416873489","doi":"10.1109/aann66429.2025.11257731","title":"Cutting FLOPs Overhead with SNT and LReSuMe: A Noise-Tolerant Spiking Classifier","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"FLOPS; Robustness (evolution); Inference; Spiking neural network; Spurious relationship; Flicker; Salient; Artificial neural 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002493193,0.0005649322,0.0005801928,0.0002135028,0.0005332006,0.0002020863,0.0001893543,0.0001840786,0.00008702592],"category_scores_gemma":[0.00006296813,0.0005011702,0.00009298836,0.0006170868,0.0001136504,0.0004123168,0.0002413861,0.000819152,0.00001279406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001245383,"about_ca_system_score_gemma":0.00007426713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001813745,"about_ca_topic_score_gemma":0.00002768548,"domain_scores_codex":[0.9975027,0.00005404214,0.0006167573,0.0007375757,0.0002402943,0.0008486244],"domain_scores_gemma":[0.99889,0.0003366457,0.00009935282,0.0004108245,0.00007286827,0.0001902533],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006754217,0.0001678317,0.009549968,0.004092488,0.0007401575,0.0005489851,0.002588398,0.3018965,0.1007119,0.01584995,0.0006916192,0.5624867],"study_design_scores_gemma":[0.00440889,0.0003318914,0.01102317,0.00735516,0.0003739311,0.0001873843,0.001712768,0.8660399,0.08787437,0.001702552,0.01697483,0.002015113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7961386,0.003245908,0.1376433,0.000443632,0.001015619,0.0005197775,0.000003804559,0.0004605853,0.06052869],"genre_scores_gemma":[0.9884608,0.0001871452,0.008095468,0.0004753005,0.0002178234,0.00001124387,0.000001907841,0.00006264084,0.002487683],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5641434,"threshold_uncertainty_score":0.999744,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008318569796166844,"score_gpt":0.2311779525767949,"score_spread":0.222859382780628,"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."}}