{"id":"W2968726863","doi":"10.48550/arxiv.1908.04396","title":"Challenge of Spatial Cognition for Deep Learning","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Abstraction; Cognition; Construct (python library); GRASP; Spatial cognition; Deep learning; Set (abstract data type); Visual reasoning; Machine learning; Psychology","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":[],"consensus_categories":[],"category_scores_codex":[0.0001693493,0.0001883623,0.0002945746,0.0001759026,0.0000682231,0.00002889443,0.000826836,0.0002189415,0.000009400444],"category_scores_gemma":[0.00007876148,0.000224457,0.0001980692,0.0001702041,0.00005431945,0.000332924,0.0008923283,0.0003677892,0.00001465424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006220389,"about_ca_system_score_gemma":0.00006586577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003097085,"about_ca_topic_score_gemma":0.000008145965,"domain_scores_codex":[0.9988475,0.00006270593,0.0001639785,0.0006488909,0.00006684933,0.0002100594],"domain_scores_gemma":[0.9986352,0.0001509009,0.0003255381,0.0005423661,0.0002921288,0.00005388711],"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":[0.0005839172,0.0006696498,0.0023732,0.002485156,0.0004862938,0.000204543,0.001421795,0.188127,0.001387672,0.4778806,0.0001730726,0.3242072],"study_design_scores_gemma":[0.0008850691,0.00061441,0.0002884636,0.0002722768,0.0001021932,0.000001603607,0.00005894657,0.7522073,0.01263893,0.2301065,0.002227021,0.0005973766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004128846,0.0001227107,0.9923335,0.00002974372,0.0002199577,0.0004908384,0.000009094185,0.0002327513,0.002432595],"genre_scores_gemma":[0.9879757,0.0006134213,0.01085502,0.00001916316,0.00005308085,0.000001759759,0.00002413737,0.00001485743,0.0004429182],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9838468,"threshold_uncertainty_score":0.915309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07262870344296712,"score_gpt":0.2214478920384956,"score_spread":0.1488191885955285,"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."}}