{"id":"W7027829294","doi":"","title":"DenseHMM: Learning Hidden Markov Models by Learning Dense Representations: Paper presented at Learning Meaningful Representations of Life Workshop at the 34th Conference on Neural Information Processing Systems, NeurIPS 2020, December 6, 2020, Online, Vancouver, Canada","year":2020,"lang":"en","type":"article","venue":"Fraunhofer-Publica (Fraunhofer-Gesellschaft)","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hidden Markov model; Scalability; Artificial neural network; Markov model; Deep learning; Markov process; Kernelization","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001378318,0.001230252,0.001401611,0.0004087139,0.003000628,0.001240948,0.003036531,0.0005952073,0.0005517482],"category_scores_gemma":[0.008061867,0.001127474,0.0003457724,0.003089551,0.0003278102,0.004206207,0.002483423,0.004835904,0.00013164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009400806,"about_ca_system_score_gemma":0.002233777,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05167841,"about_ca_topic_score_gemma":0.05613496,"domain_scores_codex":[0.9858966,0.003569974,0.002950326,0.002186158,0.003492208,0.001904777],"domain_scores_gemma":[0.9883624,0.003120743,0.00273589,0.001909406,0.002451252,0.001420278],"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":[0.0005179322,0.0001652757,0.025038,0.0008525553,0.0002258416,0.00006559472,0.0120618,0.6284546,0.0003722952,0.0006119173,0.3212035,0.01043075],"study_design_scores_gemma":[0.001455612,0.000320122,0.002093761,0.0003087345,0.00008377968,0.00006639389,0.00663813,0.8702629,0.000100734,0.0000586227,0.117575,0.00103626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6981117,0.008410636,0.08070146,0.1720343,0.007852846,0.009570999,0.0006127272,0.004611635,0.01809366],"genre_scores_gemma":[0.9744048,0.0006058617,0.002366927,0.004548089,0.0005573955,0.0003577266,0.001404847,0.0002159234,0.01553842],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2762931,"threshold_uncertainty_score":0.9997959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02633551808500826,"score_gpt":0.2686804470798033,"score_spread":0.242344928994795,"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."}}