{"id":"W2927780566","doi":"10.48550/arxiv.1904.00438","title":"Understanding Neural Architecture Search Techniques","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Interpretability; Computer science; Controller (irrigation); Architecture; Computation; Graph; Artificial neural network; Artificial intelligence; Similarity (geometry); Machine learning; ENCODE; Theoretical computer science; Algorithm","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":[0.001861676,0.0007731292,0.0005079638,0.00140975,0.0005158547,0.001697379,0.001492147,0.001598301,0.005426532],"category_scores_gemma":[0.01001067,0.0005490224,0.0009627619,0.0007108498,0.001756513,0.004287842,0.001584859,0.002239723,0.0006614213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001240316,"about_ca_system_score_gemma":0.0007079492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0020148,"about_ca_topic_score_gemma":0.001850159,"domain_scores_codex":[0.9990871,0.0002957936,0.00005593551,0.0002360874,0.0002473011,0.00007782173],"domain_scores_gemma":[0.9968666,0.002007092,0.000267172,0.0005288376,0.0002729395,0.00005733914],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005376413,0.00005639679,0.002371235,0.0002869324,0.00009827983,0.000139648,0.0003857187,0.4079565,0.004375346,0.4765002,0.002926353,0.1048497],"study_design_scores_gemma":[0.000009798629,0.00002275199,0.0004114663,0.00003526475,0.00001168289,0.00004746722,0.00004247466,0.6902992,0.001111583,0.3053633,0.002634852,0.00001013078],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.02545905,0.001014286,0.9616997,0.001477606,0.00003826244,0.0000633385,0.0001674745,0.0003707747,0.009709528],"genre_scores_gemma":[0.621035,0.001774718,0.3692448,0.0004114135,0.0001330409,0.0002872885,0.0005046698,0.0002332269,0.006375886],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.005426532,"threshold_uncertainty_score":0.01815355,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1685196417328178,"score_gpt":0.2283406863077185,"score_spread":0.05982104457490073,"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."}}