{"id":"W2963935808","doi":"","title":"Focused Hierarchical RNNs for Conditional Sequence Processing","year":2018,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Topic Modeling","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal; McGill University; Université de Montréal; Polytechnique Montréal","funders":"","keywords":"Computer science; Security token; Recurrent neural network; Encoder; Sequence (biology); Generalization; Context (archaeology); Artificial intelligence; Embedding; Dependency (UML); Machine learning; Artificial neural network","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":[0.0006383296,0.0002659979,0.0002650972,0.000282162,0.0005115281,0.0003784038,0.001297264,0.0002109123,0.00001790116],"category_scores_gemma":[0.0002309551,0.0002681698,0.0001146318,0.0004602002,0.0002055871,0.0008081283,0.0003169532,0.0002998987,0.00002031277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002873717,"about_ca_system_score_gemma":0.0005284439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004036048,"about_ca_topic_score_gemma":0.0003101461,"domain_scores_codex":[0.997591,0.00007481571,0.0004326419,0.0007028827,0.0004172555,0.0007814565],"domain_scores_gemma":[0.9982414,0.0001439822,0.000173019,0.0008541521,0.0002988036,0.0002886651],"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":[0.00005954423,0.0001726296,0.002073556,0.00006459287,0.00003047916,0.00003146533,0.0007519959,0.0008156435,0.02128733,0.7530458,0.003023644,0.2186433],"study_design_scores_gemma":[0.0004563754,0.0002097805,0.001703026,0.00003904059,0.000008844371,0.0000947055,0.00001017591,0.8877911,0.01056633,0.0936512,0.005124389,0.000345046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01488501,0.0001553853,0.9751123,0.007604971,0.0001436295,0.0006358479,0.00003209234,0.0009795107,0.0004512506],"genre_scores_gemma":[0.5181442,0.00000379938,0.4787579,0.002189286,0.0003150273,0.0003394994,0.00001388296,0.00002238093,0.0002140012],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8869755,"threshold_uncertainty_score":0.9999771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02754741548236892,"score_gpt":0.2714953414140823,"score_spread":0.2439479259317134,"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."}}