{"id":"W4283821931","doi":"10.1609/aaai.v36i6.20642","title":"Cross-Domain Few-Shot Graph Classification","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Autodesk (Canada)","funders":"","keywords":"Computer science; Encoder; Domain adaptation; Graph; Artificial intelligence; Metric (unit); Task (project management); Machine learning; Domain (mathematical analysis); Theoretical computer science; Transfer of learning; Feature (linguistics); Pattern recognition (psychology); Mathematics; Classifier (UML)","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":[0.003790578,0.002311346,0.001707266,0.003146815,0.000974415,0.001668646,0.004011596,0.002801125,0.001628943],"category_scores_gemma":[0.01521915,0.000400404,0.00129726,0.002395318,0.001236599,0.003845582,0.002692577,0.00295646,0.001140567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001425643,"about_ca_system_score_gemma":0.0008263328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005443249,"about_ca_topic_score_gemma":0.007191987,"domain_scores_codex":[0.9969251,0.0008499315,0.000121052,0.001444006,0.0004038778,0.0002561105],"domain_scores_gemma":[0.9916942,0.00431028,0.0005265353,0.002214988,0.0007957447,0.0004583037],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001521182,0.002344985,0.03053734,0.001508169,0.001019138,0.0009293924,0.0003422975,0.4625236,0.01712869,0.007243733,0.04740116,0.4275003],"study_design_scores_gemma":[0.00005618677,0.0003253567,0.005287711,0.00004054031,0.00007237859,0.0004005221,0.0002009058,0.9639658,0.01134441,0.01448076,0.003783099,0.00004230535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6111119,0.005732844,0.3552797,0.001122304,0.0006945479,0.000494346,0.007253874,0.01149155,0.006818947],"genre_scores_gemma":[0.8693368,0.0003910734,0.1056069,0.0004243849,0.0001229716,0.000201896,0.02005124,0.0005543535,0.003310417],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005443249,"threshold_uncertainty_score":0.02004671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1337759251364533,"score_gpt":0.3296633763661114,"score_spread":0.1958874512296581,"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."}}