{"id":"W3034408737","doi":"10.48550/arxiv.1911.08019","title":"Online Learned Continual Compression with Adaptive Quantization Modules","year":2019,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Encoder; Quantization (signal processing); Data compression; USable; Artificial intelligence; Reinforcement learning; Machine learning; Algorithm; Multimedia","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":[0.0001030447,0.0001266535,0.0001456455,0.0001226493,0.0001154114,0.00005883858,0.0004414663,0.00005616825,0.00005420636],"category_scores_gemma":[0.00001231035,0.0001194245,0.00004131489,0.0005289842,0.00005259458,0.0006958941,0.0001327886,0.000154924,0.0001895925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003971898,"about_ca_system_score_gemma":0.00004000408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003292075,"about_ca_topic_score_gemma":0.00002719112,"domain_scores_codex":[0.999032,0.0001126468,0.00009643904,0.000463334,0.00009793982,0.0001976148],"domain_scores_gemma":[0.9992213,0.00007861004,0.000128559,0.0003525795,0.0001301208,0.00008885188],"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":[0.0001833449,0.0001775741,0.01381536,0.000009564732,0.00003846766,0.00006694251,0.0006158233,0.4058019,0.0007881984,0.5736873,0.00008142667,0.004734138],"study_design_scores_gemma":[0.001193798,0.0002134184,0.01556596,0.0000423112,0.000009069099,0.000003812135,0.0006529083,0.9791408,0.0001343555,0.00114472,0.001691526,0.000207375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4463815,0.000007974537,0.5512165,0.00005007763,0.0000671852,0.00009641017,0.000001380035,0.0001249169,0.002053957],"genre_scores_gemma":[0.9883345,0.00001322785,0.007678122,0.000114562,0.00001624085,1.369736e-7,0.00001223248,0.000009515526,0.00382146],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5733388,"threshold_uncertainty_score":0.4869989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06811625650054115,"score_gpt":0.1896039867010142,"score_spread":0.1214877302004731,"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."}}