{"id":"W2892043975","doi":"10.18653/v1/d18-1502","title":"Dual Fixed-Size Ordinally Forgetting Encoding (FOFE) for Competitive Neural Language Models","year":2018,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Forgetting; Dual (grammatical number); Computer science; Encoding (memory); Perplexity; Artificial neural network; Word (group theory); Language model; Artificial intelligence; Deep neural networks; Mathematics; Linguistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001612057,0.001343377,0.001181688,0.0008051885,0.0004130329,0.001442404,0.003326738,0.001574998,0.004443673],"category_scores_gemma":[0.007813981,0.0004736766,0.0009631963,0.000816435,0.0007568384,0.004377658,0.001391127,0.002915223,0.001461294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000912789,"about_ca_system_score_gemma":0.001091567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00536914,"about_ca_topic_score_gemma":0.007766153,"domain_scores_codex":[0.9991807,0.0002526617,0.00007514783,0.0002077922,0.0001871562,0.00009652155],"domain_scores_gemma":[0.9977113,0.001156262,0.0001421266,0.0005005182,0.0003888412,0.0001009339],"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":[0.0004085782,0.0002618641,0.00180538,0.0002723099,0.000168336,0.0003223012,0.0002158342,0.3240698,0.008215021,0.04694773,0.00671095,0.6106018],"study_design_scores_gemma":[0.00001260791,0.00003843226,0.00008179296,0.00001058001,0.00001621404,0.0000556732,0.000009513786,0.9840556,0.00161308,0.01313192,0.0009605913,0.00001393897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01620352,0.000651492,0.9790036,0.0002703502,0.0001277252,0.00004235693,0.0001451449,0.002203492,0.001352376],"genre_scores_gemma":[0.5716144,0.0005710866,0.4200886,0.000572048,0.000232743,0.0002013094,0.0008669202,0.0004159993,0.005436782],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00536914,"threshold_uncertainty_score":0.01486558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02834280651441331,"score_gpt":0.2765021994500761,"score_spread":0.2481593929356628,"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."}}