{"id":"W4393161733","doi":"10.1609/aaai.v38i21.30419","title":"Incorporating Serverless Computing into P2P Networks for ML Training: In-Database Tasks and Their Scalability Implications (Student Abstract)","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Scalability; Computer science; Training (meteorology); Database; Distributed computing; Artificial intelligence","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":[0.001738331,0.0004247374,0.0004458729,0.0002572406,0.0008219167,0.001979329,0.001834895,0.0007697212,0.003976417],"category_scores_gemma":[0.004386874,0.000217007,0.0002169566,0.0006444111,0.0006254987,0.004695245,0.001404043,0.001150373,0.0007772534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005506995,"about_ca_system_score_gemma":0.0007082091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002427822,"about_ca_topic_score_gemma":0.002372177,"domain_scores_codex":[0.9992906,0.000264028,0.00004422904,0.0001213516,0.000184249,0.00009556251],"domain_scores_gemma":[0.9969364,0.001385476,0.00008922351,0.0007243347,0.0006450325,0.000219456],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001113538,0.001525089,0.01240467,0.0004376112,0.00009764332,0.0009316356,0.001030427,0.2213312,0.08111858,0.07114129,0.01659203,0.5922763],"study_design_scores_gemma":[0.00005994372,0.0002693674,0.001028959,0.00002621781,0.00003131303,0.0002168703,0.0002867076,0.9199567,0.04483835,0.02417392,0.009087195,0.00002446315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1815312,0.001030484,0.7930467,0.003913354,0.0003412434,0.000199859,0.0001179031,0.003652766,0.0161665],"genre_scores_gemma":[0.8691096,0.0004653913,0.1247817,0.0002305243,0.0001299238,0.00008198452,0.00009925749,0.0001086473,0.004993127],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003976417,"threshold_uncertainty_score":0.01330239,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08806732443012481,"score_gpt":0.3301135409375643,"score_spread":0.2420462165074395,"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."}}