{"id":"W3131262006","doi":"10.24963/ijcai.2021/595","title":"Combinatorial Optimization and Reasoning with Graph Neural Networks","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Polytechnique Montréal","funders":"Deutsche Forschungsgemeinschaft; Australian Government","keywords":"Computer science; Combinatorial optimization; Key (lock); Artificial intelligence; Artificial neural network; Theoretical computer science; Graph; Block (permutation group theory); Machine learning; Algorithm; Mathematics","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":[0.0001155147,0.0001912432,0.000197958,0.00009986808,0.0001247113,0.0008247932,0.0002118438,0.0001774113,0.00004777846],"category_scores_gemma":[0.00001713582,0.0001737952,0.00004084508,0.0002594387,0.00004838933,0.0003459335,0.0005087311,0.0003419463,1.901713e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002603399,"about_ca_system_score_gemma":0.00008771492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005045595,"about_ca_topic_score_gemma":0.00002472283,"domain_scores_codex":[0.998854,0.00008050631,0.0001853785,0.0005257414,0.0001926045,0.0001617463],"domain_scores_gemma":[0.9991865,0.00003950193,0.0001373048,0.0003752013,0.0001693773,0.00009208601],"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":[0.000003828006,0.00001032033,0.001360021,0.000009208373,0.00001945685,0.000008947775,0.00009933175,0.9731855,3.144258e-7,0.01831652,0.00002377946,0.006962706],"study_design_scores_gemma":[0.0003258823,0.00002413475,0.001258929,0.00005267009,0.00001376916,0.00003719697,0.00003204841,0.9977868,0.000006347473,0.0002350775,0.000008033841,0.0002191732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001939585,0.0001122049,0.9941229,0.0003475286,0.001498789,0.000187948,5.393624e-7,0.000252055,0.001538476],"genre_scores_gemma":[0.6245389,0.0001441551,0.3749311,0.0001745187,0.00009441446,0.00001583108,0.00005773379,0.00001272953,0.00003059689],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6225994,"threshold_uncertainty_score":0.79535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007260825204586705,"score_gpt":0.2083105452298668,"score_spread":0.20104972002528,"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."}}