{"id":"W6983049894","doi":"","title":"Learning to Segment Unseen Tasks In-Context","year":2024,"lang":"en","type":"dissertation","venue":"DSpace@MIT (Massachusetts Institute of Technology)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Segmentation; Image segmentation; Inference; Process (computing); Set (abstract data type); Deep learning; Segmentation-based object categorization","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":[0.001577901,0.001873687,0.001069969,0.0007359897,0.0008090702,0.001669589,0.002042633,0.002529146,0.003671641],"category_scores_gemma":[0.004889664,0.0006516475,0.001322024,0.0007609697,0.001135069,0.003349572,0.002257576,0.003853551,0.001485024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001343908,"about_ca_system_score_gemma":0.001572566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004046334,"about_ca_topic_score_gemma":0.01125655,"domain_scores_codex":[0.9989045,0.0002380805,0.00003779355,0.0005767706,0.0001305324,0.0001124163],"domain_scores_gemma":[0.9987902,0.000435169,0.0001110744,0.0004216681,0.0001390014,0.0001029354],"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":[0.0005027907,0.0005725162,0.006500815,0.0005217472,0.0002131977,0.0003958466,0.0008123047,0.2213381,0.03294564,0.04717579,0.02728874,0.6617326],"study_design_scores_gemma":[0.00002413592,0.0001406795,0.001736924,0.00008109885,0.0000511015,0.0001571198,0.0001658533,0.9048855,0.01596452,0.06582241,0.01093607,0.00003451875],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07448763,0.001649362,0.90824,0.001711976,0.0003143517,0.0002109945,0.0009469402,0.003591804,0.008846894],"genre_scores_gemma":[0.4521165,0.001017303,0.5325558,0.001319276,0.0003781117,0.0002661142,0.003239365,0.0006555522,0.008451894],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004046334,"threshold_uncertainty_score":0.01228291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01121333134141892,"score_gpt":0.2720792840870578,"score_spread":0.2608659527456389,"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."}}