{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003055381,0.00009973106,0.00007717695,0.00002725344,0.00004140704,0.00003668862,0.00006174484,0.00002775153,0.00004944375],"category_scores_gemma":[0.000001550606,0.00008554133,0.00005904549,0.0001698964,0.000009445112,0.00006378532,0.00001922218,0.0001285635,0.00005446477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009186261,"about_ca_system_score_gemma":0.000002119315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.282537e-7,"about_ca_topic_score_gemma":0.000004693282,"domain_scores_codex":[0.9995146,0.000006517228,0.0001005919,0.0001202494,0.00004081309,0.0002172334],"domain_scores_gemma":[0.9998194,0.00003672322,0.00000289244,0.00008934005,0.000003872217,0.00004777627],"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":[0.000001699604,0.000001350371,0.00008641618,0.0000402303,0.00001527126,0.000132895,0.00001651308,0.964296,0.002597386,0.0004152904,0.001328564,0.03106837],"study_design_scores_gemma":[0.00006463416,0.000035856,0.00005303877,0.00004281643,0.000009666606,0.0001962014,0.000006467639,0.9460046,0.0143326,0.00157007,0.03740984,0.0002741676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9603027,0.005175679,0.02309327,0.00005231722,0.002361291,0.00008401806,0.000001188394,0.003108572,0.005821016],"genre_scores_gemma":[0.9978632,0.00002138141,0.00120964,0.00009063827,0.0004504449,0.000002713474,0.000002435053,0.00002754927,0.0003320356],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03756052,"threshold_uncertainty_score":0.3488274,"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."}}