{"id":"W2946958331","doi":"10.1109/cvprw53098.2021.00268","title":"DeepShift: Towards Multiplication-Less Neural Networks","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Convolutional neural network; Multiplication (music); Inference; Computation; Latency (audio); Convolution (computer science); Parallel computing; Code (set theory); Algorithm; Artificial intelligence; Artificial neural network; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001489864,0.0004506491,0.0004378951,0.00009434708,0.0002168151,0.0006216478,0.003174053,0.0003645797,0.00003310779],"category_scores_gemma":[0.00003761098,0.0004513818,0.0002468482,0.0006861411,0.00007562374,0.0004043774,0.005179494,0.001068143,0.00002290755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001158765,"about_ca_system_score_gemma":0.0001484533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008199567,"about_ca_topic_score_gemma":0.000102902,"domain_scores_codex":[0.9968609,0.0001104554,0.0005487772,0.001547555,0.0003849553,0.0005473801],"domain_scores_gemma":[0.9958465,0.0001789083,0.0003081692,0.003138287,0.0002853147,0.0002428134],"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":[0.000002247551,0.0001040413,0.0002169099,0.00002970878,0.00003920056,0.00001924192,0.0001907708,0.6764438,0.00003772733,0.03524496,0.001150082,0.2865213],"study_design_scores_gemma":[0.0001228676,0.000008012389,0.002806828,0.00002652445,0.00001236161,0.00001830908,0.00002041141,0.9908003,0.0002251859,0.004477889,0.0009866316,0.0004946881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004059096,0.0008500572,0.9858679,0.004326219,0.001092314,0.0006757284,0.000004081721,0.0009512596,0.002173402],"genre_scores_gemma":[0.7211444,0.0002146013,0.2754865,0.001544979,0.0004362392,0.0005915746,0.0001295193,0.00003955615,0.0004126732],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7170852,"threshold_uncertainty_score":0.9997938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03497310263196076,"score_gpt":0.2897568323736205,"score_spread":0.2547837297416597,"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."}}