{"id":"W2982283130","doi":"10.48550/arxiv.1910.11577","title":"CrevNet: Conditionally Reversible Video Prediction","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Autoencoder; Computer science; Bijection; Property (philosophy); Feature (linguistics); Artificial intelligence; Conditional independence; Feature extraction; Resolution (logic); Pattern recognition (psychology); Algorithm; Theoretical computer science; Machine learning; Deep learning; Mathematics; Discrete mathematics","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":[0.0003800077,0.0004543226,0.000349339,0.0002293365,0.0002310667,0.0004403833,0.001460087,0.0005947803,0.003406008],"category_scores_gemma":[0.001333597,0.0002465194,0.0003144791,0.0002181177,0.0006007368,0.000968355,0.0008587415,0.0009544825,0.0006492853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003690071,"about_ca_system_score_gemma":0.0006510473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003405833,"about_ca_topic_score_gemma":0.006203355,"domain_scores_codex":[0.9998475,0.00002725694,0.000006222225,0.00005122504,0.00004230456,0.00002549437],"domain_scores_gemma":[0.9997256,0.0001068396,0.00003248541,0.00006125004,0.00005335999,0.00002032879],"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":[0.0004193785,0.0001295422,0.001011587,0.0002288489,0.00009645843,0.0003502903,0.0000980695,0.5212777,0.07634123,0.0845724,0.007903446,0.3075711],"study_design_scores_gemma":[0.000008413712,0.00004735329,0.0000995634,0.000009290916,0.000009365884,0.00005906248,0.000004471617,0.9723756,0.01609095,0.009199727,0.002084949,0.00001125737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02545758,0.0003457577,0.9666911,0.0001696027,0.00009391335,0.00005115923,0.0001892882,0.00225299,0.004748541],"genre_scores_gemma":[0.7401909,0.0004019346,0.2439741,0.0002063235,0.00005322732,0.0001474467,0.0006695985,0.0002794045,0.01407708],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003406008,"threshold_uncertainty_score":0.01139426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05244919599965824,"score_gpt":0.1981883633876888,"score_spread":0.1457391673880306,"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."}}