{"id":"W4401023952","doi":"10.24963/ijcai.2024/347","title":"Heterogeneous Temporal Hypergraph Neural Network","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Institute of Automation, Chinese Academy of Sciences; Chinese Academy of Sciences","keywords":"Spiking neural network; Computer science; Neuromorphic engineering; Boosting (machine learning); Artificial intelligence; Machine learning; Artificial neural network; Computer architecture","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":[0.0004021107,0.000754634,0.00065281,0.0009469597,0.0003654043,0.0008089361,0.001583634,0.001034709,0.002060014],"category_scores_gemma":[0.001928699,0.0003526648,0.0007586097,0.001524551,0.0005204384,0.00208633,0.0008050718,0.001253899,0.0004160679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001272233,"about_ca_system_score_gemma":0.0007374384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01353158,"about_ca_topic_score_gemma":0.01634841,"domain_scores_codex":[0.999657,0.00006782497,0.00001689906,0.0001463651,0.00006169265,0.00005011331],"domain_scores_gemma":[0.9995577,0.0001813522,0.00006496484,0.00006894964,0.00009963635,0.00002747402],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001187175,0.00008712388,0.001609973,0.0001263791,0.000107622,0.0001684335,0.0001037259,0.7177478,0.003952763,0.02561775,0.006243061,0.2441165],"study_design_scores_gemma":[0.000002939126,0.000008175911,0.0001564002,0.000004962589,0.000009062069,0.00001582854,0.000007832155,0.9908884,0.0003791222,0.007978474,0.0005446502,0.000004262413],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0388276,0.001303561,0.9533,0.0005259756,0.0001209552,0.0000590893,0.0005775201,0.001333911,0.00395136],"genre_scores_gemma":[0.8005525,0.001478466,0.1836997,0.0007499402,0.0001428808,0.000202412,0.002145042,0.0001835785,0.01084548],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01353158,"threshold_uncertainty_score":0.0269056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01235967630577013,"score_gpt":0.2238876526732776,"score_spread":0.2115279763675074,"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."}}