{"id":"W4400248355","doi":"10.1007/978-3-031-58181-6_38","title":"ConvMTL: Multi-task Learning via Self-supervised Learning for Simultaneous Dense Predictions","year":2024,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Athabasca University","funders":"","keywords":"Computer science; Task (project management); Artificial intelligence; Supervised learning; Machine learning; Engineering; Artificial neural network","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":[0.002078722,0.003094459,0.002465369,0.001261079,0.0008014475,0.00209553,0.006482432,0.003557539,0.01271668],"category_scores_gemma":[0.004372582,0.001589662,0.002099262,0.001901823,0.001001587,0.003933182,0.004095076,0.004664644,0.009347404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001241279,"about_ca_system_score_gemma":0.001485967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01031349,"about_ca_topic_score_gemma":0.01649755,"domain_scores_codex":[0.998647,0.0002843901,0.00007080305,0.0005419706,0.0003163609,0.0001394629],"domain_scores_gemma":[0.9984246,0.0007174485,0.00007124653,0.0004363626,0.0002603884,0.00008986458],"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":[0.0002887939,0.0002927201,0.0003068179,0.0003460131,0.0002586829,0.0001757147,0.00007665732,0.09943429,0.006086364,0.008786468,0.08051413,0.8034332],"study_design_scores_gemma":[0.0000224676,0.00004271371,0.00009303445,0.00001908061,0.00002103731,0.00004008703,0.00001240344,0.9794298,0.003048339,0.01293622,0.004318449,0.00001627089],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003310147,0.001287381,0.968272,0.0003277262,0.0003590677,0.000134444,0.001041488,0.02281676,0.002450926],"genre_scores_gemma":[0.1073487,0.001058615,0.8542914,0.001049989,0.0004485069,0.0007424409,0.009086801,0.003776321,0.02219714],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01271668,"threshold_uncertainty_score":0.04254156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03268561146353205,"score_gpt":0.2862654446636109,"score_spread":0.2535798332000788,"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."}}