{"id":"W2173996720","doi":"","title":"Semi-supervised Learning with Encoder-Decoder Recurrent Neural Networks: Experiments with Motion Capture Sequences","year":2015,"lang":"en","type":"preprint","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Discriminative model; Encoder; Recurrent neural network; Artificial intelligence; Encoding (memory); Machine learning; Autoencoder; Regularization (linguistics); Sequence (biology); Feature learning; Pattern recognition (psychology); Supervised learning; Artificial neural network; Motion capture; Motion (physics); Sequence 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","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0006627155,0.0008495213,0.0006889814,0.000536463,0.0004414838,0.001124094,0.001344024,0.0006644106,0.00003017286],"category_scores_gemma":[0.00004469305,0.0006998914,0.0001836649,0.0005903124,0.0001307391,0.001119035,0.0008020486,0.002161114,0.00001373168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007556904,"about_ca_system_score_gemma":0.0005149749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002416617,"about_ca_topic_score_gemma":0.001921872,"domain_scores_codex":[0.9956353,0.000462577,0.0006222021,0.00138471,0.0009408127,0.0009543836],"domain_scores_gemma":[0.9969484,0.00006844255,0.0006902656,0.001233753,0.0005354914,0.0005236415],"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":[0.0002995492,0.0006009988,0.01648029,0.0001900418,0.0003171415,0.0003848001,0.003872287,0.897158,0.0004934921,0.001425732,0.002865515,0.07591221],"study_design_scores_gemma":[0.0007543527,0.0004016832,0.001316685,0.0004503868,0.00006958731,0.0002520773,0.0003403503,0.9926602,0.001455901,0.0008865549,0.0004004705,0.001011733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08902011,0.001526022,0.9044213,0.0012838,0.0004539946,0.001211122,0.00001653515,0.001549037,0.0005180472],"genre_scores_gemma":[0.9296371,0.0002290831,0.06697932,0.0009325395,0.0003566025,0.001094408,0.0002644364,0.0001031885,0.0004033674],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8406169,"threshold_uncertainty_score":0.9999129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02352876789836961,"score_gpt":0.2500587755150337,"score_spread":0.2265300076166641,"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."}}