{"id":"W4313532401","doi":"10.48550/arxiv.2301.01047","title":"A Theory of Human-Like Few-Shot Learning","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Artificial intelligence; Autoencoder; Computer science; Generative grammar; Deep learning; Machine learning; Generative model","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003365941,0.00077836,0.001503773,0.001140773,0.0009619293,0.002131638,0.003112189,0.002391645,0.004425751],"category_scores_gemma":[0.01686573,0.000705461,0.001183164,0.0009254083,0.005117037,0.007115848,0.002987015,0.003569619,0.0007265436],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001455856,"about_ca_system_score_gemma":0.001063496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002020207,"about_ca_topic_score_gemma":0.002109898,"domain_scores_codex":[0.998026,0.0007183038,0.00007232754,0.0006383367,0.0004243821,0.0001206276],"domain_scores_gemma":[0.9918651,0.00576298,0.0004640173,0.001076128,0.0005292522,0.000302514],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000052052,0.00008207052,0.001124705,0.0002799493,0.0001085167,0.0001160572,0.0003654551,0.104131,0.00195928,0.8534409,0.003152359,0.03518768],"study_design_scores_gemma":[0.00000680838,0.00004006337,0.0002577118,0.00002825021,0.00001049337,0.00008580098,0.00002901363,0.3298256,0.0006119147,0.6675705,0.001514473,0.00001945666],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.008302796,0.0003920099,0.9870122,0.0008234817,0.00005017949,0.00003183173,0.00009954963,0.0001683815,0.003119579],"genre_scores_gemma":[0.6719194,0.001306503,0.3128236,0.001777274,0.0004864722,0.0003934574,0.0006225648,0.0002668097,0.01040401],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.004425751,"threshold_uncertainty_score":0.01780099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.179193005031713,"score_gpt":0.2274187735958758,"score_spread":0.04822576856416283,"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."}}