{"id":"W3035695586","doi":"10.1109/cvpr42600.2020.00162","title":"ReSprop: Reuse Sparsified Backpropagation","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Speedup; Computer science; Computation; Backpropagation; Convolutional neural network; Convolution (computer science); Reuse; Parallel computing; Reduction (mathematics); Sparse matrix; Computational science; Artificial neural network; Artificial intelligence; Algorithm; Computer engineering; 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":[],"consensus_categories":[],"category_scores_codex":[0.00003882076,0.00006357742,0.00006107445,0.00001557252,0.00006822495,0.00004734144,0.0009254233,0.0000213932,0.00002805576],"category_scores_gemma":[0.00007157348,0.000055988,0.00002135365,0.0005012142,0.00001615372,0.0003459148,0.0003238601,0.00007535404,0.0005912624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001122745,"about_ca_system_score_gemma":0.00001548035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001302318,"about_ca_topic_score_gemma":8.529474e-7,"domain_scores_codex":[0.9993033,0.00002008207,0.0001230984,0.0002978513,0.0001259919,0.0001297091],"domain_scores_gemma":[0.9991398,0.00004127964,0.0000410555,0.0006330646,0.0000389938,0.0001058393],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001697774,0.0000375709,0.0001542192,0.000009195179,0.000007157726,0.00001069656,0.0006020461,0.002471762,0.04532658,0.8035423,0.0765575,0.07126404],"study_design_scores_gemma":[0.0005727613,0.0001864481,0.001644208,0.000007787112,0.000005302165,0.00001013324,0.00002422133,0.605667,0.08604302,0.06864778,0.2367079,0.0004835179],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002384868,0.00001603661,0.9438133,0.04456492,0.00004139529,0.0001641581,3.282618e-7,0.0004658934,0.008549125],"genre_scores_gemma":[0.622417,0.00001038037,0.3713958,0.005589705,0.0001139044,0.00002338181,0.000002181206,0.00000711267,0.0004405697],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7348945,"threshold_uncertainty_score":0.7599679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05164835959100823,"score_gpt":0.2591902171441513,"score_spread":0.2075418575531431,"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."}}