{"id":"W2996680032","doi":"","title":"Efficient and Information-Preserving Future Frame Prediction and Beyond","year":2020,"lang":"en","type":"article","venue":"International Conference on Learning Representations","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; MNIST database; Autoencoder; Bottleneck; Artificial intelligence; Frame (networking); Machine learning; Feature extraction; Information bottleneck method; Margin (machine learning); Feature (linguistics); High memory; Key (lock); Deep learning; State (computer science); Algorithm; Mutual information","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004364609,0.0006155384,0.0004701113,0.0003559703,0.0002926301,0.0005597883,0.0014387,0.0006192001,0.002792217],"category_scores_gemma":[0.001694115,0.000328672,0.0004620827,0.0003641137,0.0005211448,0.001414769,0.0008543179,0.001104884,0.0009530531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003927263,"about_ca_system_score_gemma":0.0007492669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004173055,"about_ca_topic_score_gemma":0.006247553,"domain_scores_codex":[0.999831,0.00002391641,0.000007893635,0.0000634475,0.00004498732,0.00002880864],"domain_scores_gemma":[0.9996386,0.0001192657,0.00004646286,0.0001168147,0.00005817242,0.00002071294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002452284,0.0001180721,0.001444844,0.0001204301,0.00005680004,0.0002469602,0.0001413501,0.3487969,0.0465334,0.03127112,0.004929008,0.5660959],"study_design_scores_gemma":[0.000004610367,0.0000382484,0.0002551408,0.00001325915,0.00001084259,0.00007512786,0.000009671484,0.9720666,0.0169565,0.008574023,0.001986512,0.000009549167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0333202,0.0003160225,0.9609013,0.0001738954,0.00004928459,0.00003611196,0.00018065,0.00168788,0.003334651],"genre_scores_gemma":[0.7616254,0.0004358691,0.2297244,0.000152756,0.00005905692,0.00007671223,0.0007210146,0.0002816113,0.00692316],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004173055,"threshold_uncertainty_score":0.009340882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01964610070994115,"score_gpt":0.2979189959929102,"score_spread":0.2782728952829691,"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."}}