{"id":"W7000541850","doi":"","title":"Focused hierarchical RNNs for conditional sequence processing","year":2018,"lang":"en","type":"article","venue":"Jagiellonian University Repository (Jagiellonian University)","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Regional Development Fund; Canada Research Chairs; Compute Canada; Microsoft Research","keywords":"Security token; Generalization; Sequence (biology); Encoder; Context (archaeology); Recurrent neural network; Embedding; Key (lock)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009006565,0.001398986,0.0005864624,0.0007503554,0.0002824719,0.0006224331,0.001714519,0.001015208,0.007829779],"category_scores_gemma":[0.002842046,0.0005589441,0.0007821736,0.000758652,0.0004316749,0.001648174,0.000826488,0.001872265,0.002863236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00120953,"about_ca_system_score_gemma":0.001049239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009396349,"about_ca_topic_score_gemma":0.01661568,"domain_scores_codex":[0.999531,0.0001332866,0.00002594682,0.0001778829,0.00008600748,0.00004575876],"domain_scores_gemma":[0.9991177,0.0004466723,0.00007643671,0.0001618157,0.0001653565,0.00003200667],"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":[0.0001837994,0.0001526065,0.0007951346,0.0003337974,0.0001552604,0.0001299069,0.0001261426,0.5440724,0.02023934,0.03848831,0.01950847,0.3758148],"study_design_scores_gemma":[0.000006075388,0.00001641549,0.0001210941,0.000008453751,0.000008229036,0.00001219013,0.000004197004,0.9875773,0.002168272,0.008568424,0.001504078,0.000005308081],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008977733,0.0008634496,0.98197,0.0002331014,0.00009546539,0.00008592218,0.0006098934,0.004713167,0.002451333],"genre_scores_gemma":[0.3555045,0.001196976,0.6212957,0.0005787661,0.0002431014,0.0005093698,0.004626072,0.0008127795,0.0152327],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009396349,"threshold_uncertainty_score":0.02619326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02381451076763849,"score_gpt":0.2204741529833915,"score_spread":0.196659642215753,"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."}}