{"id":"W3157599687","doi":"","title":"DC3: A learning method for optimization with hard constraints","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Constraint (computer-aided design); Differentiable function; Grid; Mathematical optimization; Optimization problem; Deep learning; ENCODE; Constrained optimization; Algorithm; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001968539,0.002027649,0.001328708,0.001305152,0.0007304839,0.001365474,0.002350068,0.002042048,0.01187533],"category_scores_gemma":[0.009438452,0.001012578,0.001383524,0.001528909,0.001758737,0.001464673,0.003419095,0.005325399,0.002559478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001343992,"about_ca_system_score_gemma":0.004197218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007771795,"about_ca_topic_score_gemma":0.01275264,"domain_scores_codex":[0.9987941,0.000377513,0.00005948246,0.0001977065,0.0004755124,0.00009564848],"domain_scores_gemma":[0.9968128,0.001971402,0.0001819776,0.0004216865,0.0004503485,0.0001619156],"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":[0.0001141584,0.0001122587,0.0005894228,0.0003506467,0.00009280684,0.000130028,0.00008008014,0.6900958,0.002031735,0.1056309,0.03049319,0.1702789],"study_design_scores_gemma":[0.00002654877,0.00001255351,0.00002722788,0.00002099198,0.000004216882,0.00001800024,0.000007431298,0.9670679,0.0005518574,0.02741401,0.004841741,0.000007469325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006909164,0.0001054298,0.9964942,0.0001351676,0.00004387052,0.00004967091,0.0001023083,0.0006661028,0.001712268],"genre_scores_gemma":[0.05106479,0.0002586056,0.9404204,0.0004477987,0.000122667,0.0005303054,0.0007904755,0.001518223,0.004846786],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01187533,"threshold_uncertainty_score":0.03972691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05739591779876293,"score_gpt":0.2025298560040661,"score_spread":0.1451339382053032,"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."}}