{"id":"W7132893767","doi":"","title":"BitChop: A Heuristic Approach to Memory Footprint Reduction in AI Training","year":2022,"lang":"","type":"dissertation","venue":"TSpace","topic":"Big Data and Digital Economy","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Toronto","keywords":"Memory footprint; Reduction (mathematics); Footprint; Heuristics; Artificial neural network; Training (meteorology); Heuristic; Lossy compression","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":[0.0007768985,0.0006050108,0.0007330318,0.0009587989,0.0003191165,0.0007737689,0.001665332,0.0002691374,0.0001726705],"category_scores_gemma":[0.0001685291,0.0007652804,0.0001966898,0.00182169,0.0000549675,0.000660053,0.0006221455,0.001175214,0.0001415046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005143163,"about_ca_system_score_gemma":0.0009452671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001089782,"about_ca_topic_score_gemma":0.0000991031,"domain_scores_codex":[0.9956486,0.0001895916,0.0008236828,0.00192805,0.0005665861,0.0008435408],"domain_scores_gemma":[0.9977685,0.00007821487,0.0003704362,0.001274589,0.0001029969,0.0004053035],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0003421116,0.00112516,0.00003207534,0.0007259654,0.0001018559,0.0000875391,0.3794402,0.02110926,0.0009136977,0.02629302,0.002355598,0.5674735],"study_design_scores_gemma":[0.005966347,0.002514146,0.009512885,0.001862316,0.0002701248,0.001013046,0.5975341,0.1767617,0.003338386,0.009747996,0.181576,0.009902973],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3409949,0.001543112,0.1203337,0.003140149,0.01151102,0.004365615,0.00006665994,0.0004905939,0.5175542],"genre_scores_gemma":[0.9735494,0.00004089443,0.008683687,0.000381243,0.0003185978,0.0007199355,0.0007694825,0.0000806477,0.01545615],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6325545,"threshold_uncertainty_score":0.9994798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06713135526166314,"score_gpt":0.3354844691610666,"score_spread":0.2683531138994034,"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."}}