{"id":"W2345668077","doi":"10.48550/arxiv.1605.00064","title":"Higher Order Recurrent Neural Networks","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Recurrent neural network; Computer science; Treebank; Dependency (UML); Long short term memory; Artificial intelligence; Sequence (biology); Language model; Artificial neural network; Task (project management); Speech recognition; Natural language processing; Machine learning","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009098875,0.0003094804,0.0002560842,0.00009453849,0.0001769839,0.0001376353,0.002033978,0.0002434629,0.00007500542],"category_scores_gemma":[0.000004241996,0.00027824,0.000179607,0.0005935006,0.00009746803,0.0002622924,0.002245828,0.00055983,0.000092634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009724737,"about_ca_system_score_gemma":0.00005466849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001749381,"about_ca_topic_score_gemma":0.000008588285,"domain_scores_codex":[0.9981005,0.0000915756,0.0001826041,0.001119222,0.0000709896,0.0004351294],"domain_scores_gemma":[0.9979386,0.00009196756,0.0002090426,0.001412136,0.0001481894,0.0002000618],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001189964,0.00008765997,0.0007865289,0.00001637188,0.00005346919,0.0001068249,0.00001696607,0.3354819,0.000007011199,0.6413658,0.009742423,0.01232321],"study_design_scores_gemma":[0.0002585725,0.00002532282,0.0007979497,0.00005405941,0.00002864323,0.000002535563,0.000001440232,0.9433414,0.000005724095,0.04541991,0.009623249,0.0004412082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01234476,0.0001456473,0.9795846,0.001436837,0.002076152,0.0003133932,0.000009519114,0.0003834156,0.003705665],"genre_scores_gemma":[0.9944217,0.0002145911,0.0005691818,0.0002971372,0.0004280594,0.000003359004,0.00001124533,0.0000189402,0.004035832],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9820769,"threshold_uncertainty_score":0.999967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06515286175752022,"score_gpt":0.1961121686690946,"score_spread":0.1309593069115744,"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."}}