{"id":"W4283797178","doi":"10.1609/aaai.v36i2.20061","title":"SpikeConverter: An Efficient Conversion Framework Zipping the Gap between Artificial Neural Networks and Spiking Neural Networks","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; Shanghai Jiao Tong University; National Natural Science Foundation of China; Institute for Catastrophic Loss Reduction","keywords":"Spiking neural network; Computer science; Artificial neural network; Artificial intelligence; Spike (software development); Inference; Pattern recognition (psychology); Machine learning","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006731834,0.0003593958,0.0003698222,0.0001000865,0.001134392,0.0002043224,0.0009936884,0.0001112289,0.00007698248],"category_scores_gemma":[0.0001138603,0.0002788449,0.0001327472,0.000600022,0.0003045093,0.0002293899,0.0005413785,0.001459472,0.000002856483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007440434,"about_ca_system_score_gemma":0.00001271164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001106108,"about_ca_topic_score_gemma":0.000002371755,"domain_scores_codex":[0.9977255,0.00006197103,0.0006532588,0.0005075728,0.0004258812,0.0006258001],"domain_scores_gemma":[0.998857,0.0003135192,0.0002845634,0.0002760063,0.0001316148,0.000137249],"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":[0.00009985534,0.00003825232,0.000540724,0.00003733248,0.00001905069,0.000001802703,0.0009409986,0.9039295,0.003676355,0.01528932,0.00001612766,0.07541063],"study_design_scores_gemma":[0.00002874702,0.000217484,0.0002314118,0.00008871831,0.00003646129,0.00001312928,0.001619578,0.9711525,0.02104383,0.005234474,0.00002345387,0.0003101665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.961408,0.0001492466,0.03548764,0.0005648154,0.001470955,0.000516129,0.000006402115,0.0002026567,0.0001941537],"genre_scores_gemma":[0.9990101,0.00002119986,0.0001072227,0.0002716555,0.0005084223,0.00002687081,0.000002726124,0.00004364106,0.000008095572],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07510047,"threshold_uncertainty_score":0.9999664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06553404433713134,"score_gpt":0.2765627105121829,"score_spread":0.2110286661750516,"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."}}