{"id":"W4224272151","doi":"10.1109/vrw55335.2022.00201","title":"Splitting Large Convolutional Neural Network Layers to Run Real-Time Applications on Mixed-Reality Hardware: Extended Abstract","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Convolutional neural network; Frame rate; Convolution (computer science); Frame (networking); Overhead (engineering); Graph; Convolutional code; Parallel computing; Algorithm; Real-time computing; Computer engineering; Artificial neural network; Artificial intelligence; Theoretical computer science; Decoding methods; Operating system; Computer network","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007736488,0.0004568923,0.0004707379,0.0001118096,0.0007300556,0.0002830038,0.0003339715,0.000140078,0.0005393796],"category_scores_gemma":[0.00004580949,0.0004729947,0.00008865552,0.0002839075,0.0001289609,0.0002011632,0.0001778124,0.001009591,0.00005149841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001432773,"about_ca_system_score_gemma":0.00005250775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000246846,"about_ca_topic_score_gemma":0.0002008449,"domain_scores_codex":[0.9972771,0.0001190111,0.0006373064,0.000750074,0.0004942696,0.0007222267],"domain_scores_gemma":[0.9985288,0.0003906147,0.0001531521,0.0004709433,0.00007597458,0.0003805076],"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":[0.000735322,0.0006547066,0.0005353938,0.0001699299,0.0003126944,0.00006092307,0.00207683,0.8206716,0.00835994,0.01490591,0.05881782,0.09269897],"study_design_scores_gemma":[0.005835999,0.002647989,0.3532823,0.001313235,0.0004392354,0.0001888494,0.01578138,0.3555275,0.004891729,0.004085882,0.2495189,0.006486949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9890388,0.0001477933,0.0004643303,0.001463239,0.0007062241,0.000600098,0.0009012833,0.0003753629,0.006302848],"genre_scores_gemma":[0.9976699,0.0002851854,0.00007390752,0.000464874,0.0002640641,0.0001492132,0.0001727557,0.00005234955,0.0008677959],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.465144,"threshold_uncertainty_score":0.9997722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02638272970402388,"score_gpt":0.2702927637904058,"score_spread":0.2439100340863819,"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."}}