{"id":"W4312250727","doi":"10.1007/978-3-031-19775-8_3","title":"You Already Have It: A Generator-Free Low-Precision DNN Training Framework Using Stochastic Rounding","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Rounding; NIST; Random number generation; Generator (circuit theory); Artificial neural network; Application-specific integrated circuit; Stochastic computing; Field-programmable gate array; Artificial intelligence; Algorithm; Computer engineering; Speech recognition; Computer hardware","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001168686,0.0009492094,0.001025455,0.0003938435,0.0005430311,0.001312761,0.002321983,0.001769845,0.01127523],"category_scores_gemma":[0.002750318,0.0007194356,0.0007043993,0.0004633662,0.000718373,0.001616094,0.002258124,0.003693264,0.005375637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006680036,"about_ca_system_score_gemma":0.001067425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004018957,"about_ca_topic_score_gemma":0.007268443,"domain_scores_codex":[0.9995406,0.0001053375,0.00002387595,0.0001149609,0.0001680547,0.00004734453],"domain_scores_gemma":[0.9994949,0.0002108863,0.00002610532,0.0001141396,0.0001146088,0.00003927996],"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":[0.0002832925,0.00007597372,0.0001952314,0.0001454699,0.00006314681,0.0001602038,0.00006393689,0.4919057,0.01336702,0.09356842,0.0145638,0.3856077],"study_design_scores_gemma":[0.00001162792,0.00002113596,0.0000209801,0.00001456897,0.000006496834,0.00002707187,0.00000421378,0.9738764,0.002028617,0.02181765,0.002163164,0.000008112597],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001681442,0.000176183,0.9938082,0.0001154732,0.00008743142,0.00002682324,0.00008248329,0.001462402,0.002559587],"genre_scores_gemma":[0.1273487,0.0004036603,0.8564194,0.0003706627,0.0001571202,0.0001433707,0.0005723364,0.0009647526,0.01361984],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01127523,"threshold_uncertainty_score":0.03771943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04120259046185975,"score_gpt":0.2958535069173309,"score_spread":0.2546509164554711,"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."}}