{"meta":{"query_hash":"22fa33f7ea39","filters":{"venue":"neural information processing systems"},"cohort_total":5,"direct_labels_cover":0,"predictions_cover":5,"exported":5,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/22fa33f7ea39","api":"https://metacan.xera.ac/api/v1/cohort?venue=neural+information+processing+systems"},"results":[{"id":"W2107741520","doi":"","title":"Weighted importance sampling for off-policy learning with linear function approximation","year":2014,"lang":"en","type":"article","venue":"neural information processing systems","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":100,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Reinforcement learning; Weighting; Computer science; Importance sampling; Sampling (signal processing); Convergence (economics); Function (biology); Bridging (networking); Artificial intelligence; Mathematical optimization; Machine learning; Function approximation; Mathematics; Statistics; Artificial neural network","score_opus":0.020501250815071665,"score_gpt":0.2586979979424325,"score_spread":0.2381967471273608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107741520","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005933413,0.00014032076,0.9926208,0.00008012487,0.000029598914,0.000039601793,0.000011245715,0.00016012562,0.0009846879],"genre_scores_gemma":[0.69602567,0.00037897384,0.29873568,0.00026356356,0.00010210901,0.00041985002,0.0001569149,0.00018493328,0.00373229],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99833566,0.0007954943,0.00007223223,0.00020131363,0.00046619,0.00012920714],"domain_scores_gemma":[0.99480045,0.003931496,0.0002417963,0.00039393132,0.00048698587,0.00014535371],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003871777,0.0010368589,0.0015191423,0.00077235524,0.00046546655,0.0009972792,0.0015825137,0.0012385136,0.0024752496],"category_scores_gemma":[0.015067214,0.0006548598,0.00060754066,0.0007096122,0.0014912977,0.0015599575,0.001529916,0.0022186823,0.0004343321],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013791186,0.00011346711,0.0007565156,0.000093433555,0.000041269996,0.000071502574,0.00005880834,0.89446115,0.0011414099,0.044649154,0.0008608595,0.057614535],"study_design_scores_gemma":[0.000006589455,0.0000134382835,0.000024246418,0.000003260468,0.0000021753274,0.000005213354,0.000001674112,0.99230933,0.00018102884,0.0072921207,0.00015900865,0.0000017942385],"about_ca_topic_score_codex":0.003799199,"about_ca_topic_score_gemma":0.002948301,"teacher_disagreement_score":0.003871777,"about_ca_system_score_codex":0.0014544493,"about_ca_system_score_gemma":0.0017211516,"threshold_uncertainty_score":0.020476222},"labels":[],"label_agreement":null},{"id":"W2136836265","doi":"","title":"Adaptive dropout for training deep neural networks","year":2013,"lang":"en","type":"article","venue":"neural information processing systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":280,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Dropout (neural networks); MNIST database; Computer science; Artificial intelligence; Artificial neural network; Feature (linguistics); Boltzmann machine; Deep belief network; Restricted Boltzmann machine; Stochastic gradient descent; Pattern recognition (psychology); Convolutional neural network; Machine learning; Gradient descent; Deep learning; Backpropagation","score_opus":0.031988761766423526,"score_gpt":0.2623541483614051,"score_spread":0.23036538659498157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136836265","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0075898482,0.0007353236,0.9880284,0.0002754466,0.000058827114,0.00005295054,0.00017040691,0.0023993144,0.0006894648],"genre_scores_gemma":[0.43667817,0.001072215,0.5525709,0.000482965,0.00015725565,0.0005980167,0.0015403776,0.0006039467,0.0062961653],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902654,0.00030352184,0.00007445418,0.00019076419,0.00030118108,0.00010359085],"domain_scores_gemma":[0.9981797,0.0010682713,0.00017245086,0.00021070645,0.00030999945,0.000058993042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025329876,0.0016303904,0.001390848,0.00078256323,0.00049315963,0.00072810764,0.0024101415,0.001743993,0.002599454],"category_scores_gemma":[0.009215246,0.0008076783,0.00088506716,0.0011637654,0.00083174335,0.0017024179,0.0011846736,0.0028908784,0.00097525655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018550166,0.00009602705,0.0012158148,0.00022790366,0.00010602396,0.00010912528,0.00007202156,0.75418526,0.0036401555,0.013718932,0.0056068143,0.22083646],"study_design_scores_gemma":[0.000008567491,0.000012515787,0.00008281985,0.000009818968,0.0000045262263,0.000009035216,0.0000023552154,0.993558,0.0010873677,0.004687907,0.00053352,0.0000035442529],"about_ca_topic_score_codex":0.0074583874,"about_ca_topic_score_gemma":0.009496566,"teacher_disagreement_score":0.0074583874,"about_ca_system_score_codex":0.0020893498,"about_ca_system_score_gemma":0.0013879298,"threshold_uncertainty_score":0.0151593685},"labels":[],"label_agreement":null},{"id":"W2162124943","doi":"","title":"Automatic Generation of Social Tags for Music Recommendation","year":2007,"lang":"en","type":"article","venue":"neural information processing systems","topic":"Music and Audio Processing","field":"Computer Science","cited_by":184,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; CMC Microsystems (Canada)","funders":"","keywords":"Recommender system; Computer science; Information retrieval; Set (abstract data type); Baseline (sea); World Wide Web; Resource (disambiguation); Space (punctuation); Social web; Component (thermodynamics); Social media","score_opus":0.06320323338863898,"score_gpt":0.29409910127234096,"score_spread":0.23089586788370198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162124943","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12957591,0.0008535698,0.84641796,0.00051577564,0.00041946518,0.000398199,0.0035900204,0.0092994105,0.008929669],"genre_scores_gemma":[0.59727556,0.00033247253,0.38487202,0.00018669816,0.00023841568,0.0002853884,0.007128368,0.00037563915,0.009305431],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894434,0.0002696712,0.0000537621,0.00028917982,0.00031747014,0.00012567393],"domain_scores_gemma":[0.99757713,0.00087289134,0.0001768653,0.00044078165,0.00082799216,0.00010439253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008098643,0.0007873744,0.00059428904,0.0024558802,0.00070196035,0.0009317625,0.0009474471,0.0010987405,0.0021341953],"category_scores_gemma":[0.0043566595,0.00036034366,0.0007173113,0.0017744665,0.00032647498,0.0012437975,0.00074831676,0.0008692308,0.0035393995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058682176,0.00050396513,0.022482522,0.00024106579,0.00022044612,0.00027686026,0.00039437608,0.025345843,0.041526604,0.0048890132,0.029225033,0.8743074],"study_design_scores_gemma":[0.000060690872,0.00014721515,0.009521491,0.000034293516,0.00009708119,0.00024868903,0.00021573326,0.92340755,0.041383777,0.010606301,0.01421578,0.000061424376],"about_ca_topic_score_codex":0.0072905547,"about_ca_topic_score_gemma":0.023837974,"teacher_disagreement_score":0.0072905547,"about_ca_system_score_codex":0.00073228875,"about_ca_system_score_gemma":0.0007542986,"threshold_uncertainty_score":0.014496207},"labels":[],"label_agreement":null},{"id":"W2163976407","doi":"","title":"Lower Bounds on Rate of Convergence of Cutting Plane Methods","year":2010,"lang":"en","type":"article","venue":"neural information processing systems","topic":"Advanced Numerical Analysis Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Support vector machine; Cutting-plane method; Hinge loss; Convergence (economics); Rate of convergence; Conjecture; Plane (geometry); Computer science; Algorithm; Function (biology); Mathematical optimization; Mathematics; Applied mathematics; Artificial intelligence; Combinatorics; Geometry","score_opus":0.010689782648075125,"score_gpt":0.2954642392651482,"score_spread":0.28477445661707307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163976407","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015899023,0.014749543,0.93404067,0.004728172,0.00093939545,0.00042514302,0.0005434653,0.0016259403,0.02704867],"genre_scores_gemma":[0.2608341,0.014407214,0.6849862,0.0031592026,0.0021287638,0.0028007461,0.0023608906,0.0058428985,0.023480011],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9810228,0.007462696,0.001257684,0.0023351149,0.0062996265,0.0016222241],"domain_scores_gemma":[0.82823414,0.14003648,0.00439525,0.011774699,0.013592294,0.0019671645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024375439,0.004975929,0.0036838378,0.003446759,0.002180244,0.004559478,0.0061819823,0.0062728026,0.013477225],"category_scores_gemma":[0.18317595,0.0019146967,0.003193169,0.002950609,0.005158517,0.009609666,0.0075967615,0.016518258,0.007618246],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003069826,0.00061102415,0.008216838,0.0025066065,0.0006072765,0.0007144543,0.0011398289,0.3105389,0.010722282,0.42452985,0.029546205,0.20779686],"study_design_scores_gemma":[0.00016241423,0.0004237655,0.001070561,0.00074622856,0.00011146813,0.0004257106,0.00015040887,0.7942753,0.006231549,0.1825322,0.013762527,0.00010793541],"about_ca_topic_score_codex":0.0017650519,"about_ca_topic_score_gemma":0.0012545431,"teacher_disagreement_score":0.024375439,"about_ca_system_score_codex":0.0031066786,"about_ca_system_score_gemma":0.0022569848,"threshold_uncertainty_score":0.1289112},"labels":[],"label_agreement":null},{"id":"W2770008080","doi":"","title":"Efficient Sublinear-Regret Algorithms for Online Sparse Linear Regression with Limited Observation","year":2017,"lang":"en","type":"article","venue":"neural information processing systems","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Regret; Sublinear function; Computer science; Algorithm; Linear regression; Constraint (computer-aided design); Task (project management); Exponential function; Mathematical optimization; Mathematics; Machine learning; Discrete mathematics","score_opus":0.25850632172385657,"score_gpt":0.4470429972754272,"score_spread":0.1885366755515706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2770008080","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0067636166,0.0009622475,0.9870401,0.00085430755,0.000104127794,0.00013244103,0.00021144305,0.0014779466,0.0024537628],"genre_scores_gemma":[0.26060548,0.0012524232,0.7270392,0.0012022413,0.0005542599,0.0008837296,0.0017490388,0.0007104936,0.006003232],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99668306,0.0011457772,0.00018979252,0.00069213065,0.0008159478,0.00047342977],"domain_scores_gemma":[0.9847705,0.01151758,0.0010507292,0.0013616937,0.0009540385,0.00034543645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047884085,0.002609702,0.0027247495,0.0009935412,0.000985996,0.0021764257,0.0041655283,0.0024154615,0.0061996877],"category_scores_gemma":[0.021376278,0.0010426209,0.0015574034,0.0020324432,0.0014526242,0.0041323015,0.0028696968,0.0057563265,0.0030164346],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006542719,0.0006742729,0.0012973155,0.0004103185,0.00012798575,0.00011578747,0.00016997872,0.7161039,0.0019342004,0.04373357,0.021211153,0.21356724],"study_design_scores_gemma":[0.000046295256,0.000036868423,0.00009719327,0.00001195195,0.000011464913,0.000028864544,0.000014428522,0.9830344,0.0005305242,0.015552668,0.0006278062,0.0000075371727],"about_ca_topic_score_codex":0.0046594026,"about_ca_topic_score_gemma":0.007428977,"teacher_disagreement_score":0.0061996877,"about_ca_system_score_codex":0.0024343173,"about_ca_system_score_gemma":0.0039597643,"threshold_uncertainty_score":0.025323868},"labels":[],"label_agreement":null}]}