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Wiki Medical | S³_angular | 42 | 20 | [
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[
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]
] | 19.901227 | all-mpnet-base-v2 | 1 | -0.177734 | 0.391057 | 0.40338 |
Wiki Medical | S³_angular | 42 | 30 | [
[
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[
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]
] | 20.715152 | all-mpnet-base-v2 | 1 | -0.184592 | 0.32433 | 0.358192 |
Wiki Medical | S³_angular | 42 | 40 | [
[
"echocardiograms",
"echocardiographic",
"cardiomyopathy",
"echocardiogram",
"electrocardiography",
"cardiomyopathies",
"echocardiography",
"electrocardiographic",
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[
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] | 19.955409 | all-mpnet-base-v2 | 0.9975 | -0.172906 | 0.335006 | 0.345084 |
Wiki Medical | S³_angular | 42 | 50 | [
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Wiki Medical | S³_combined | 42 | 10 | [
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Wiki Medical | S³_combined | 42 | 20 | [
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Wiki Medical | S³_combined | 42 | 30 | [
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Wiki Medical | S³_combined | 42 | 40 | [
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]
] | 555.486446 | all-mpnet-base-v2 | 0.838 | -0.043935 | 0.206016 | 0.284965 |
StackExchange | ECRTM | 42 | 20 | [
[
"isequal",
"az",
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]
] | 8,960.960903 | all-mpnet-base-v2 | 1 | -0.163205 | 0.124266 | 0.461328 |
StackExchange | ECRTM | 42 | 30 | [
[
"directives",
"wolfenstein",
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]
] | 10,378.886409 | all-mpnet-base-v2 | 1 | -0.116751 | 0.132616 | 0.514271 |
StackExchange | ECRTM | 42 | 40 | [
[
"dpi",
"640x480",
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]
] | 14,023.435481 | all-mpnet-base-v2 | 1 | -0.111776 | 0.217445 | 0.487457 |
StackExchange | ECRTM | 42 | 50 | [
[
"shootdirection",
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[
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[
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[
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"set_mode",
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"k_s",
"set_caption"
]
] | 12,844.104385 | all-mpnet-base-v2 | 1 | -0.086581 | 0.191245 | 0.481126 |
Wiki Medical | ECRTM | 42 | 10 | [
[
"mutation",
"microcephaly",
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]
] | 780.314439 | all-mpnet-base-v2 | 1 | -0.062704 | 0.273154 | 0.449112 |
Wiki Medical | ECRTM | 42 | 20 | [
[
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] | 951.790904 | all-mpnet-base-v2 | 1 | -0.100893 | 0.315049 | 0.433094 |
Wiki Medical | ECRTM | 42 | 30 | [
[
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] | 1,158.772788 | all-mpnet-base-v2 | 0.986667 | -0.087556 | 0.303028 | 0.449596 |
Wiki Medical | ECRTM | 42 | 40 | [
[
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] | 1,305.774816 | all-mpnet-base-v2 | 0.99 | -0.143079 | 0.304399 | 0.425804 |
Wiki Medical | ECRTM | 42 | 50 | [
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] | 1,488.272352 | all-mpnet-base-v2 | 0.978 | -0.124226 | 0.322639 | 0.458112 |
StackExchange | ECRTM | 42 | 10 | [
[
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] | 52,309.362111 | all-mpnet-base-v2 | 1 | 0.110472 | 0.143086 | 0.439922 |
BBC News | S³ | 42 | 10 | [
[
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]
] | 2.158765 | all-mpnet-base-v2 | 0.92 | -0.274553 | 0.272117 | 0.927894 |
BBC News | S³ | 42 | 20 | [
[
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[
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[
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[
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[
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"iraq",
"khodorkovsky"
]
] | 2.168818 | all-mpnet-base-v2 | 0.94 | -0.24746 | 0.244045 | 0.932891 |
BBC News | S³ | 42 | 30 | [
[
"rbs",
"activists",
"lions",
"producers",
"organisations",
"disney",
"democrats",
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[
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]
] | 2.302735 | all-mpnet-base-v2 | 0.906667 | -0.315866 | 0.221602 | 0.917345 |
BBC News | S³ | 42 | 40 | [
[
"lending",
"house",
"homes",
"inflation",
"mortgage",
"recession",
"rates",
"bath",
"interest",
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]
] | 2.412319 | all-mpnet-base-v2 | 0.9175 | -0.310836 | 0.208152 | 0.922316 |
BBC News | S³ | 42 | 50 | [
[
"appeal",
"blamed",
"blame",
"offence",
"affect",
"communicate",
"face",
"comedy",
"mood",
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"her",
"wife",
"winslet"
],
[
"auction",
"broke",
"prize",
"prizes",
"maria",
"exploit",
"theft",
"contest",
"arrest",
"prisoner"
]
] | 2.582093 | all-mpnet-base-v2 | 0.908 | -0.304832 | 0.219215 | 0.917064 |
ArXiv ML Papers | S³ | 42 | 10 | [
[
"regularization",
"segmentation",
"next",
"tasks",
"mcmc",
"planning",
"overview",
"clustering",
"task",
"optimizing"
],
[
"english",
"language",
"library",
"linguistic",
"corpus",
"corpora",
"bidirectional",
"translate",
"proposing",
"encoding"
],
[
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"optimality",
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],
[
"optimizer",
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"nonlinear",
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"optimality",
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],
[
"phenomena",
"observational",
"inference",
"simulation",
"generating",
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],
[
"conversion",
"textit",
"voice",
"preprocessing",
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"english",
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],
[
"gans",
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],
[
"annotations",
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],
[
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"monitoring",
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"reinforcement",
"driving",
"classifier"
],
[
"biology",
"interpretability",
"heuristics",
"experimentally",
"rigorously",
"neurons",
"abstraction",
"arbitrary",
"comparisons",
"computationally"
]
] | 2.003873 | all-mpnet-base-v2 | 0.91 | -0.370503 | 0.191167 | 0.875676 |
ArXiv ML Papers | S³ | 42 | 20 | [
[
"adversarially",
"adversarial",
"robustness",
"attack",
"attacks",
"anomalies",
"intrusion",
"exploits",
"malicious",
"attacker"
],
[
"graphs",
"vertex",
"graph",
"vertices",
"nodes",
"paths",
"path",
"node",
"walk",
"network"
],
[
"lasso",
"denoising",
"nonconvex",
"reconstructed",
"sparsity",
"sparse",
"compressed",
"containing",
"linear",
"alleviate"
],
[
"bandit",
"bandits",
"optimality",
"strategy",
"choose",
"optimal",
"choosing",
"advantage",
"tradeoff",
"choices"
],
[
"autoencoders",
"laplacian",
"convexity",
"neural",
"convolutions",
"nonlinear",
"convolution",
"backpropagation",
"autoencoder",
"gradients"
],
[
"clustering",
"population",
"distribution",
"densities",
"bandwidth",
"distributions",
"compressed",
"aggregation",
"heterogeneity",
"frequencies"
],
[
"look",
"visual",
"nonparametric",
"shape",
"geometric",
"extreme",
"perceptual",
"highlights",
"parametric",
"sparsity"
],
[
"recommendations",
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"suggest",
"preferences",
"profiles",
"personal",
"profile",
"personalized",
"embeddings"
],
[
"optimizer",
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],
[
"lstm",
"rnn",
"autoencoder",
"mcmc",
"encoder",
"sequences",
"clustering",
"autoencoders",
"rnns",
"svm"
],
[
"metric",
"latency",
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"sensor",
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"classifiers",
"classifier",
"sensors",
"sensing",
"nonparametric"
],
[
"classifiers",
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],
[
"bayes",
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"priors",
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],
[
"convergence",
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"gradually",
"diffusion",
"sgd",
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],
[
"dimensionality",
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"generalizability",
"compositional",
"transformations",
"embedding",
"similarities",
"embeddings",
"parameterized",
"transforms"
],
[
"audio",
"mismatch",
"recorded",
"voice",
"speaker",
"sound",
"acoustic",
"asynchronous",
"asr",
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],
[
"map",
"coordinates",
"traffic",
"transportation",
"driving",
"vehicles",
"gps",
"vehicle",
"maps",
"car"
],
[
"pruning",
"lasso",
"cnns",
"trainable",
"boosting",
"overfitting",
"datasets",
"hyperparameters",
"partial",
"imagenet"
],
[
"svm",
"autoencoder",
"unlabeled",
"classification",
"clustering",
"classifier",
"classifying",
"supervised",
"classifiers",
"supervision"
],
[
"predict",
"forecasting",
"autoregressive",
"predicting",
"forecasts",
"forecast",
"prediction",
"predicts",
"predictors",
"weather"
]
] | 2.134 | all-mpnet-base-v2 | 0.91 | -0.316691 | 0.211777 | 0.903862 |
ArXiv ML Papers | S³ | 42 | 30 | [
[
"correlation",
"brain",
"hypothesis",
"studies",
"findings",
"infrastructure",
"correlations",
"cognitive",
"statistics",
"architectures"
],
[
"clusters",
"segment",
"segmentation",
"cluster",
"subset",
"summarize",
"clustering",
"regions",
"partition",
"split"
],
[
"ensemble",
"ensembles",
"embeddings",
"autoencoders",
"hyperparameters",
"neural",
"embedding",
"mnist",
"autoencoder",
"decoder"
],
[
"xgboost",
"combined",
"solver",
"targets",
"optimum",
"opposed",
"alternatives",
"suite",
"alternative",
"boost"
],
[
"adversarially",
"adversarial",
"defend",
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"attack",
"robustness",
"attacker",
"attacks",
"malicious",
"vulnerable"
],
[
"classification",
"rnns",
"heuristics",
"heterogeneous",
"probabilistic",
"markov",
"classifying",
"rnn",
"bayes",
"bayesian"
],
[
"programming",
"benchmarks",
"implementations",
"developers",
"github",
"runtime",
"scalability",
"implementation",
"testing",
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],
[
"unlabeled",
"classifier",
"label",
"supervision",
"bayesian",
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"annotation",
"annotations",
"mcmc",
"covariance"
],
[
"entropy",
"molecular",
"particle",
"quantization",
"quantized",
"quantum",
"theoretical",
"physics",
"databases",
"database"
],
[
"dimensionality",
"geometry",
"distances",
"manifold",
"spatially",
"dimensional",
"similarity",
"shape",
"spatial",
"3d"
],
[
"computational",
"iteratively",
"neurons",
"granularity",
"categorical",
"distribution",
"instances",
"images",
"knowledge",
"computationally"
],
[
"posterior",
"bayes",
"separately",
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"thresholding",
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"processing",
"extraction",
"classifiers",
"brain"
],
[
"recommender",
"ranking",
"preferences",
"recommendations",
"suggest",
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"profiles",
"suggesting",
"google",
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],
[
"tensors",
"transformations",
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"tensor",
"matrix",
"matrices",
"facial",
"transformation",
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],
[
"preprocessing",
"normalizing",
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"normalization",
"autoencoder",
"frameworks",
"dataset",
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],
[
"thresholding",
"robots",
"detection",
"inspection",
"robotic",
"robot",
"automated",
"programming",
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],
[
"engineering",
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"diffusion",
"numerically",
"nonlinear",
"perturbation",
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],
[
"nearest",
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"approximation",
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"nn",
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],
[
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],
[
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"covariates",
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],
[
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"encode",
"communicate",
"bandwidth",
"iot",
"channels",
"distribution",
"security",
"case",
"distributions"
],
[
"discriminate",
"fairness",
"biases",
"discrimination",
"equally",
"diversity",
"bias",
"imbalance",
"heterogeneity",
"inequality"
],
[
"hyperparameter",
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"bayes",
"precision",
"hessian",
"margin",
"significant",
"focused",
"stability",
"sensitive"
],
[
"boosting",
"bias",
"biased",
"complementary",
"consistently",
"sentiment",
"contrast",
"svm",
"reliability",
"robust"
],
[
"clinical",
"medical",
"mri",
"diagnosis",
"patients",
"healthcare",
"cancer",
"medicine",
"diseases",
"patient"
],
[
"motion",
"behaviors",
"biological",
"visual",
"activity",
"video",
"behavioral",
"eeg",
"sensing",
"multimodal"
],
[
"linguistic",
"corpus",
"nlp",
"corpora",
"text",
"words",
"textual",
"semantically",
"sentences",
"subjects"
],
[
"accuracy",
"reliability",
"numerical",
"root",
"nodes",
"node",
"incomplete",
"tree",
"accuracies",
"trees"
],
[
"controller",
"planning",
"exploring",
"robot",
"practice",
"explore",
"robotics",
"reinforcement",
"control",
"exploration"
],
[
"recurrent",
"anomaly",
"anomalies",
"sequences",
"outliers",
"sequential",
"monitoring",
"occurrence",
"outlier",
"sequence"
]
] | 2.203227 | all-mpnet-base-v2 | 0.923333 | -0.335405 | 0.211016 | 0.906661 |
ArXiv ML Papers | S³ | 42 | 40 | [
[
"generating",
"gan",
"generate",
"gans",
"generates",
"synthesis",
"generator",
"generated",
"generative",
"creating"
],
[
"transform",
"mri",
"processing",
"embeddings",
"transforms",
"transformers",
"tensors",
"tensor",
"matrices",
"reconstructed"
],
[
"supervised",
"learning",
"datasets",
"dataset",
"data",
"imputation",
"estimating",
"annotation",
"annotations",
"observing"
],
[
"denoising",
"compress",
"sparse",
"sparsity",
"compressed",
"lacks",
"compression",
"contain",
"reduced",
"alleviate"
],
[
"normalization",
"separable",
"formalize",
"normalizing",
"grounded",
"differentially",
"differential",
"separation",
"healthy",
"derive"
],
[
"share",
"publicly",
"sharing",
"discrete",
"reveals",
"privacy",
"reveal",
"private",
"hidden",
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],
[
"degradation",
"denoising",
"bandwidth",
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"distillation",
"process",
"reducing",
"diffusion",
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],
[
"combination",
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],
[
"hyperparameters",
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"hyperparameter",
"xgboost",
"rank",
"predictor",
"autoencoder",
"predictors"
],
[
"combinatorial",
"filters",
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"classifier",
"bayes",
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"reconstructing",
"pooling",
"imputation",
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],
[
"outliers",
"outlier",
"anomaly",
"deviation",
"deviations",
"occurrence",
"observe",
"monitoring",
"anomalies",
"investigate"
],
[
"covariance",
"autoencoders",
"unlabeled",
"svm",
"autoencoder",
"classifiers",
"predicting",
"supervised",
"predictors",
"supervision"
],
[
"autoencoders",
"autoencoder",
"encode",
"variational",
"embeddings",
"encoders",
"embedding",
"latent",
"encoder",
"encodes"
],
[
"brain",
"eeg",
"neural",
"neurons",
"cognitive",
"memory",
"randomly",
"neuron",
"perception",
"thought"
],
[
"estimators",
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],
[
"nn",
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],
[
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[
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[
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[
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],
[
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"bidirectional",
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],
[
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"predicting",
"predict",
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[
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],
[
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"cancer",
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],
[
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"lasso",
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"us",
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"objects",
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[
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"automated",
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[
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"discovering",
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"learning",
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"textit"
],
[
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"face",
"identify",
"faces",
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"verify",
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],
[
"transport",
"task",
"transfer",
"transferred",
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"transform",
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],
[
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"grows",
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[
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"map",
"driving",
"transportation",
"gps",
"drive",
"traffic",
"vehicles",
"driven",
"car"
],
[
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"numerically",
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"nonlinear",
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"dynamics",
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],
[
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"focus",
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"focused",
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"focusing",
"offers",
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],
[
"variational",
"bayesian",
"mcmc",
"likelihood",
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"probabilistic",
"marginal",
"priors",
"densities"
],
[
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"constraint",
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[
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],
[
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"convolution",
"throughput",
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"delay",
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],
[
"biology",
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],
[
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[
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"malware",
"adversarially",
"robustness",
"targeted",
"attacks",
"attack",
"adversarial",
"cancer",
"exploits"
]
] | 2.139117 | all-mpnet-base-v2 | 0.91 | -0.337613 | 0.218006 | 0.903734 |
ArXiv ML Papers | S³ | 42 | 50 | [
[
"sampling",
"sample",
"simulation",
"simulated",
"simulations",
"streams",
"streaming",
"algorithms",
"easily",
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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20 Newsgroups Raw | S³ | 42 | 10 | [
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20 Newsgroups Raw | S³ | 42 | 20 | [
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20 Newsgroups Raw | S³ | 42 | 30 | [
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20 Newsgroups Raw | S³ | 42 | 40 | [
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[
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[
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[
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[
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[
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] | 15.77352 | all-mpnet-base-v2 | 0.9825 | -0.236302 | 0.239284 | 0.400104 |
20 Newsgroups Raw | S³ | 42 | 50 | [
[
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"parc",
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[
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[
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[
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[
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[
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[
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[
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"xterms"
]
] | 15.952653 | all-mpnet-base-v2 | 0.974 | -0.220603 | 0.264422 | 0.420743 |
ArXiv ML Papers | S³_angular | 42 | 10 | [
[
"mcmc",
"regularization",
"overview",
"tasks",
"intensive",
"start",
"next",
"task",
"regularized",
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"implications",
"cells"
]
] | 2.056319 | all-mpnet-base-v2 | 0.98 | -0.373587 | 0.153722 | 0.870637 |
ArXiv ML Papers | S³_angular | 42 | 20 | [
[
"attacker",
"defend",
"robustness",
"adversarial",
"attacks",
"malicious",
"adversarially",
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[
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"predicts",
"predicted",
"predictor"
]
] | 2.128645 | all-mpnet-base-v2 | 0.91 | -0.301999 | 0.201509 | 0.901483 |
ArXiv ML Papers | S³_angular | 42 | 30 | [
[
"throughout",
"among",
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"correlation",
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"numerous",
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"sequences"
]
] | 2.013742 | all-mpnet-base-v2 | 0.946667 | -0.328002 | 0.193325 | 0.889986 |
ArXiv ML Papers | S³_angular | 42 | 40 | [
[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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],
[
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"diagnosis",
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"medicine",
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],
[
"objects",
"lasso",
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"like",
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],
[
"programming",
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"automated",
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],
[
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"training",
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"discovering",
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],
[
"identify",
"recognize",
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"face",
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"recognizing",
"identifies",
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],
[
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],
[
"vast",
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"larger",
"mixture",
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],
[
"vehicle",
"traffic",
"drive",
"driven",
"transportation",
"driving",
"vehicles",
"car",
"gps",
"map"
],
[
"modelling",
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[
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"manually",
"supervised",
"focused",
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],
[
"bayesian",
"mcmc",
"posterior",
"bayes",
"variational",
"priors",
"probabilistic",
"likelihood",
"densities",
"markov"
],
[
"solving",
"conditional",
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"relational",
"databases",
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"constraints",
"constraint"
],
[
"corpora",
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"characterizing",
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],
[
"convolution",
"convolutions",
"forward",
"apply",
"throughput",
"estimates",
"response",
"applying",
"probabilities",
"delay"
],
[
"encoded",
"combinations",
"protein",
"biology",
"molecular",
"label",
"opposed",
"same",
"package",
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],
[
"quantization",
"quantum",
"quantized",
"particle",
"88",
"assessing",
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],
[
"attacks",
"attack",
"adversarial",
"adversarially",
"malicious",
"malware",
"mnist",
"attacker",
"robustness",
"targeted"
]
] | 2.077501 | all-mpnet-base-v2 | 0.9175 | -0.330906 | 0.200536 | 0.892013 |
ArXiv ML Papers | S³_angular | 42 | 50 | [
[
"rapid",
"sample",
"records",
"algorithms",
"simulated",
"simulation",
"simulations",
"sampling",
"easily",
"dataset"
],
[
"consistently",
"boosting",
"ai",
"svm",
"cnns",
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"advance",
"markov",
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],
[
"horizon",
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"ml",
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"into",
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],
[
"adaptive",
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],
[
"likelihood",
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],
[
"calibrated",
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],
[
"autoencoder",
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"vae",
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"encodes",
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],
[
"predictor",
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],
[
"programs",
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"runtime",
"github",
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"coding",
"programming",
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],
[
"solver",
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"assume",
"algebra",
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"answer",
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],
[
"boosting",
"hybrid",
"forest",
"boost",
"branch",
"ucb",
"classifier",
"naive",
"logistic",
"xgboost"
],
[
"domain",
"combining",
"combination",
"paradigm",
"ideas",
"scheme",
"separately",
"domains",
"categories",
"schemes"
],
[
"stages",
"possible",
"follow",
"phases",
"aim",
"vast",
"large",
"google",
"lot",
"navigation"
],
[
"medical",
"patients",
"health",
"mri",
"medicine",
"healthcare",
"clinical",
"diagnosis",
"diseases",
"disease"
],
[
"recognition",
"cnns",
"analyzed",
"detector",
"identified",
"canonical",
"viewed",
"system",
"equivalent",
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],
[
"bandits",
"bandit",
"regret",
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"greedy",
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],
[
"events",
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"jointly",
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],
[
"possible",
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"correlations",
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"cnns",
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"coupled",
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],
[
"sentences",
"coherent",
"growing",
"sentence",
"recognizing",
"euclidean",
"turns",
"turn",
"landscape",
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],
[
"coupling",
"quantum",
"tuning",
"quantization",
"particle",
"quantized",
"iot",
"hilbert",
"sensing",
"wise"
],
[
"partition",
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],
[
"short",
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],
[
"rnn",
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"backpropagation",
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],
[
"signal",
"nlp",
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],
[
"forecasts",
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"grid",
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],
[
"learner",
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[
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[
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[
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[
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[
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[
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],
[
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],
[
"outlier",
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"intrusion",
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],
[
"molecular",
"biology",
"abstract",
"cells",
"cell",
"protein",
"same",
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"discover",
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],
[
"audio",
"acoustic",
"voice",
"sound",
"resonance",
"stage",
"recorded",
"speaker",
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"tune"
],
[
"subspace",
"covariance",
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"channels",
"squares",
"subspaces",
"combinatorial",
"channel",
"coverage"
],
[
"tensor",
"matrix",
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"matrices",
"decomposition",
"fold",
"vectors",
"vector",
"compute",
"decompose"
],
[
"sparse",
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"reconstructed",
"compress",
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"denoising",
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],
[
"explanations",
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"interpretability",
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"explanation",
"visualization",
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"simply",
"intuitive"
],
[
"densities",
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"availability",
"projections",
"industry",
"expected",
"expectation",
"expect",
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],
[
"f1",
"gps",
"vehicles",
"car",
"traffic",
"vehicle",
"transportation",
"driving",
"driven",
"drive"
],
[
"devise",
"malware",
"regular",
"basic",
"regularization",
"formalize",
"regularized",
"iot",
"free",
"membership"
],
[
"industrial",
"oracle",
"characterize",
"architectures",
"reading",
"distinguishing",
"distinguish",
"architectural",
"characterization",
"characterizing"
],
[
"distances",
"similarity",
"distance",
"far",
"nearest",
"metric",
"metrics",
"near",
"compared",
"compare"
],
[
"labeled",
"label",
"annotation",
"noisy",
"labels",
"annotated",
"labeling",
"annotations",
"unlabeled",
"costly"
],
[
"pose",
"robot",
"robotic",
"robotics",
"newton",
"body",
"hand",
"arms",
"force",
"arm"
],
[
"robot",
"svm",
"lstm",
"robots",
"entropy",
"optical",
"probability",
"quantization",
"spectral",
"pytorch"
],
[
"restricted",
"outside",
"specify",
"trains",
"captures",
"limited",
"specified",
"spaces",
"unique",
"transient"
],
[
"predictive",
"sequential",
"incomplete",
"probabilistic",
"variational",
"might",
"based",
"sequences",
"entries",
"deterministic"
]
] | 2.2125 | all-mpnet-base-v2 | 0.902 | -0.33961 | 0.190738 | 0.911794 |
ArXiv ML Papers | S³_combined | 42 | 10 | [
[
"overview",
"mcmc",
"segmentation",
"next",
"regularization",
"planning",
"tasks",
"clustering",
"task",
"start"
],
[
"translate",
"ell_2",
"linguistic",
"bidirectional",
"proposing",
"mini",
"library",
"pair",
"corpus",
"language"
],
[
"clustering",
"metrics",
"algorithms",
"heuristics",
"bayes",
"preferences",
"maximization",
"optimality",
"optimum",
"convexity"
],
[
"backpropagation",
"optimizer",
"heuristics",
"neural",
"nonlinear",
"optimality",
"approximations",
"hessian",
"iterative",
"optimization"
],
[
"generative",
"inference",
"stochastic",
"observational",
"scenarios",
"simulation",
"causal",
"generating",
"influence",
"simulations"
],
[
"conversion",
"audio",
"textit",
"acoustic",
"evaluations",
"rapid",
"voice",
"asr",
"corpora",
"quality"
],
[
"unsupervised",
"sparse",
"gan",
"variational",
"yielding",
"gans",
"denoising",
"reconstructed",
"sampling",
"obtaining"
],
[
"predicting",
"predictors",
"priors",
"hyperparameters",
"predicts",
"predictive",
"annotations",
"annotated",
"leveraged",
"imbalanced"
],
[
"assist",
"supervision",
"suggest",
"training",
"suggests",
"boosting",
"train",
"supervised",
"driving",
"monitoring"
],
[
"interpretability",
"experimentally",
"heuristics",
"neurons",
"comparisons",
"rigorously",
"arbitrarily",
"abstraction",
"biology",
"implications"
]
] | 2.229787 | all-mpnet-base-v2 | 0.96 | -0.373657 | 0.166714 | 0.881961 |
ArXiv ML Papers | S³_combined | 42 | 20 | [
[
"adversarially",
"robustness",
"adversarial",
"attacks",
"malicious",
"intrusion",
"exploits",
"attack",
"anomalies",
"attacker"
],
[
"node",
"graphs",
"graph",
"vertex",
"vertices",
"nodes",
"path",
"paths",
"walk",
"network"
],
[
"lasso",
"sparsity",
"sparse",
"nonconvex",
"containing",
"denoising",
"reconstructed",
"linear",
"compressed",
"minimize"
],
[
"advantage",
"optimality",
"strategy",
"bandits",
"bandit",
"optimal",
"choosing",
"tradeoff",
"choose",
"choices"
],
[
"autoencoders",
"convolutions",
"neural",
"nonlinear",
"propagation",
"convolution",
"convexity",
"laplacian",
"backpropagation",
"convolutional"
],
[
"compressed",
"heterogeneity",
"distributions",
"distribution",
"aggregation",
"densities",
"heterogeneous",
"distributional",
"bandwidth",
"clustering"
],
[
"shape",
"highlights",
"perceptual",
"extreme",
"visual",
"characterize",
"geometric",
"nonparametric",
"look",
"characterizing"
],
[
"recommender",
"suggest",
"recommendations",
"preferences",
"recommendation",
"personalized",
"personal",
"profile",
"profiles",
"embeddings"
],
[
"optimum",
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"hyperparameter",
"numerically",
"optimisation",
"heuristics",
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],
[
"lstm",
"autoencoder",
"clustering",
"sequences",
"rnn",
"mcmc",
"encoder",
"svm",
"rnns",
"autoencoders"
],
[
"classifier",
"sensing",
"sensor",
"sensors",
"classifiers",
"recognize",
"metrics",
"music",
"metric",
"nonparametric"
],
[
"asymptotically",
"kernel",
"svm",
"detecting",
"asymptotic",
"precision",
"classifiers",
"kernels",
"track",
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],
[
"bayes",
"bayesian",
"mcmc",
"likelihood",
"posterior",
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"probabilistic",
"stochastic",
"estimating",
"bernoulli"
],
[
"converge",
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"convergence",
"gradually",
"hessian",
"gradient",
"gradients",
"sgd",
"diffusion",
"smoothness"
],
[
"similarity",
"dimensionality",
"embeddings",
"embedding",
"parameterized",
"transforms",
"similarities",
"transformations",
"generalizability",
"compositional"
],
[
"voice",
"asr",
"audio",
"speaker",
"recorded",
"sound",
"acoustic",
"mismatch",
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],
[
"vehicles",
"vehicle",
"map",
"driving",
"transportation",
"gps",
"car",
"coordinates",
"traffic",
"maps"
],
[
"trainable",
"boosting",
"cnns",
"imagenet",
"lasso",
"pruning",
"hyperparameters",
"datasets",
"overfitting",
"partial"
],
[
"clustering",
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"autoencoder",
"supervision",
"classification",
"svm",
"boosting",
"classifier",
"classifying",
"unlabeled"
],
[
"autoregressive",
"predicting",
"predicts",
"predictors",
"forecast",
"forecasting",
"forecasts",
"prediction",
"predict",
"weather"
]
] | 1.925228 | all-mpnet-base-v2 | 0.925 | -0.310877 | 0.208582 | 0.900374 |
ArXiv ML Papers | S³_combined | 42 | 30 | [
[
"studies",
"hypothesis",
"brain",
"findings",
"correlations",
"correlation",
"infrastructure",
"architectures",
"cognitive",
"statistics"
],
[
"cluster",
"segmentation",
"clusters",
"clustering",
"segment",
"subset",
"partition",
"summarize",
"unsupervised",
"split"
],
[
"embeddings",
"ensemble",
"ensembles",
"autoencoders",
"hyperparameters",
"mnist",
"embedding",
"autoencoder",
"neural",
"rnns"
],
[
"xgboost",
"alternatives",
"boost",
"armed",
"combined",
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"suite",
"mathbb",
"alternative",
"fusion"
],
[
"adversarial",
"attack",
"robustness",
"vulnerable",
"defend",
"adversarially",
"attacker",
"malicious",
"attacks",
"adversaries"
],
[
"classify",
"classifying",
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"classification",
"markov",
"probabilistic",
"rnn",
"heterogeneous",
"bayes"
],
[
"frameworks",
"github",
"testing",
"developers",
"programming",
"implementations",
"benchmarks",
"runtime",
"implementation",
"implementing"
],
[
"supervision",
"bayes",
"annotation",
"unlabeled",
"annotations",
"covariance",
"bayesian",
"label",
"classifier",
"supervised"
],
[
"class",
"molecular",
"quantization",
"quantized",
"quantum",
"entropy",
"theoretical",
"particle",
"database",
"databases"
],
[
"dimensionality",
"manifold",
"spatially",
"spatial",
"distances",
"dimensional",
"similarity",
"geometry",
"space",
"euclidean"
],
[
"computational",
"instances",
"knowledge",
"categorical",
"granularity",
"images",
"iteratively",
"neurons",
"computationally",
"categories"
],
[
"separate",
"bayes",
"posterior",
"extraction",
"processing",
"separately",
"classifiers",
"mixture",
"separable",
"thresholding"
],
[
"recommender",
"recommendations",
"suggesting",
"profiles",
"recommendation",
"ranking",
"suggest",
"google",
"rank",
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],
[
"tensor",
"tensors",
"matrices",
"matrix",
"processing",
"transform",
"transformations",
"facial",
"parallel",
"transformation"
],
[
"datasets",
"formalize",
"preprocessing",
"normalizing",
"embeddings",
"normalization",
"trained",
"pretrained",
"dataset",
"autoencoder"
],
[
"machine",
"thresholding",
"robots",
"robotic",
"robot",
"detection",
"inspection",
"discriminator",
"identifies",
"automated"
],
[
"perturbations",
"numerically",
"engineering",
"perturbation",
"diffusion",
"hessian",
"dynamics",
"nonlinear",
"differentially",
"analytically"
],
[
"tested",
"nearest",
"near",
"benchmark",
"nn",
"approximated",
"test",
"approximation",
"testing",
"imputation"
],
[
"inductive",
"optimizer",
"solver",
"reconstruct",
"linearly",
"retrieval",
"algorithm",
"reconstructing",
"oracle",
"solvers"
],
[
"particular",
"hyperparameters",
"covariates",
"hyperparameter",
"nonparametric",
"limitations",
"criteria",
"parameters",
"exhaustive",
"characterizing"
],
[
"case",
"iot",
"bandwidth",
"encode",
"channels",
"communicate",
"communications",
"defense",
"security",
"distribution"
],
[
"discriminate",
"discrimination",
"biases",
"fairness",
"diversity",
"equally",
"bias",
"imbalance",
"diverse",
"biased"
],
[
"bayes",
"hyperparameters",
"focused",
"margin",
"significant",
"hyperparameter",
"handling",
"hessian",
"precision",
"stability"
],
[
"boosting",
"biased",
"complementary",
"consistently",
"bias",
"sentiment",
"svm",
"robust",
"usually",
"outperform"
],
[
"healthcare",
"medical",
"mri",
"clinical",
"diagnosis",
"cancer",
"patients",
"medicine",
"diseases",
"patient"
],
[
"behavioral",
"eeg",
"visual",
"video",
"activity",
"biological",
"motion",
"human",
"behaviors",
"movement"
],
[
"linguistic",
"corpora",
"nlp",
"corpus",
"text",
"semantically",
"textual",
"words",
"word",
"sentences"
],
[
"root",
"node",
"nodes",
"incomplete",
"tree",
"reliability",
"accuracy",
"numerical",
"accuracies",
"trees"
],
[
"explore",
"robot",
"robotics",
"practice",
"autonomous",
"reinforcement",
"exploring",
"planning",
"control",
"controller"
],
[
"monitoring",
"anomalies",
"outliers",
"recurrent",
"occurrence",
"lstm",
"outlier",
"sequences",
"sequential",
"anomaly"
]
] | 2.098237 | all-mpnet-base-v2 | 0.943333 | -0.329545 | 0.208498 | 0.898761 |
ArXiv ML Papers | S³_combined | 42 | 40 | [
[
"gans",
"gan",
"generate",
"generating",
"generates",
"generated",
"generative",
"generator",
"synthesis",
"creating"
],
[
"mri",
"matrices",
"tensors",
"tensor",
"transform",
"processing",
"transformers",
"embeddings",
"transforms",
"reconstructed"
],
[
"dataset",
"datasets",
"annotation",
"supervised",
"learning",
"annotations",
"data",
"estimating",
"imputation",
"annotated"
],
[
"compress",
"compressed",
"sparse",
"lacks",
"sparsity",
"denoising",
"contain",
"compression",
"contained",
"lack"
],
[
"differentially",
"grounded",
"normalization",
"formalize",
"separation",
"normalizing",
"separable",
"differential",
"healthy",
"normalized"
],
[
"reveals",
"sharing",
"reveal",
"public",
"publicly",
"private",
"discrete",
"hidden",
"privacy",
"share"
],
[
"reducing",
"processes",
"distillation",
"process",
"degradation",
"denoising",
"preprocessing",
"bandwidth",
"reduction",
"regularization"
],
[
"combine",
"separately",
"combination",
"mixture",
"combining",
"separate",
"combined",
"momentum",
"package",
"modalities"
],
[
"hyperparameters",
"hyperparameter",
"boosting",
"svm",
"xgboost",
"nonparametric",
"rank",
"predictor",
"predictors",
"lasso"
],
[
"reconstructing",
"pooling",
"reconstruct",
"combinatorial",
"classifiers",
"learns",
"transformer",
"filters",
"classifier",
"bayes"
],
[
"outlier",
"monitoring",
"outliers",
"anomaly",
"anomalies",
"deviations",
"deviation",
"observe",
"occurrence",
"investigate"
],
[
"covariance",
"svm",
"autoencoders",
"supervised",
"classifiers",
"predictors",
"supervision",
"unlabeled",
"autoencoder",
"boosting"
],
[
"autoencoders",
"embeddings",
"encoders",
"autoencoder",
"latent",
"unsupervised",
"encode",
"encoder",
"embedding",
"variational"
],
[
"brain",
"neurons",
"neural",
"neuron",
"memory",
"cognitive",
"eeg",
"randomly",
"thought",
"perception"
],
[
"covariates",
"estimators",
"correlation",
"prevalence",
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"affected",
"efficacy",
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"correlations",
"interventions"
],
[
"nn",
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],
[
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"accuracy"
],
[
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"sound",
"music",
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"videos",
"audio",
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],
[
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],
[
"ensemble",
"assisted",
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"unified",
"boost",
"cooperative",
"federated",
"ensembles",
"aggregated",
"boosting"
],
[
"streams",
"computationally",
"cnns",
"convolutions",
"bidirectional",
"aggregation",
"tracking",
"matching",
"inferring",
"classifiers"
],
[
"forecast",
"forecasts",
"forecasting",
"predicted",
"weather",
"predicting",
"predict",
"prediction",
"predictions",
"autoregressive"
],
[
"flow",
"recurrent",
"videos",
"video",
"sequences",
"flows",
"markov",
"stream",
"spatiotemporal",
"streams"
],
[
"cancer",
"diagnosis",
"patients",
"mri",
"medical",
"clinical",
"medicine",
"healthcare",
"patient",
"diseases"
],
[
"lasso",
"generalizing",
"granularity",
"generalize",
"generalization",
"objects",
"us",
"activity",
"distribution",
"like"
],
[
"benchmarks",
"workflow",
"programming",
"processes",
"automated",
"computing",
"preprocessing",
"developers",
"expect",
"testing"
],
[
"lstm",
"vast",
"reconstructing",
"google",
"studying",
"discovering",
"learning",
"possible",
"lasso",
"imaging"
],
[
"faces",
"face",
"identifying",
"recognize",
"identification",
"identify",
"verify",
"identified",
"recognizing",
"facial"
],
[
"shifts",
"shift",
"transferred",
"transport",
"transformation",
"transferring",
"transfer",
"task",
"transform",
"tasks"
],
[
"batch",
"grows",
"extensive",
"kernels",
"larger",
"largest",
"mixture",
"vast",
"respectively",
"imagenet"
],
[
"gps",
"drive",
"transportation",
"vehicle",
"traffic",
"car",
"driven",
"vehicles",
"driving",
"map"
],
[
"regularization",
"physics",
"numerically",
"simulations",
"nonlinear",
"acceleration",
"dynamics",
"interpolation",
"modelling",
"trajectories"
],
[
"solely",
"alone",
"focus",
"focused",
"focusing",
"interested",
"offers",
"manually",
"automatically",
"controlled"
],
[
"posterior",
"bayesian",
"mcmc",
"bayes",
"variational",
"probabilistic",
"likelihood",
"densities",
"priors",
"marginal"
],
[
"constraints",
"solvers",
"solver",
"solving",
"constraint",
"inference",
"algorithms",
"conditional",
"constrained",
"solves"
],
[
"distinguishing",
"proving",
"difficult",
"interpretable",
"prove",
"corpora",
"easier",
"interpretability",
"distinguish",
"characterizing"
],
[
"throughput",
"estimate",
"forward",
"applying",
"delay",
"probabilities",
"convolutions",
"estimates",
"convolution",
"apply"
],
[
"package",
"label",
"molecular",
"combinations",
"synthetic",
"biology",
"encoded",
"covid",
"encodes",
"protein"
],
[
"assessing",
"particle",
"quantization",
"quantum",
"calibration",
"computations",
"pairwise",
"quantized",
"measurement",
"iot"
],
[
"attacks",
"malicious",
"adversarial",
"adversarially",
"malware",
"attack",
"robustness",
"targeted",
"attacker",
"cancer"
]
] | 2.017185 | all-mpnet-base-v2 | 0.9175 | -0.331989 | 0.209317 | 0.901723 |
ArXiv ML Papers | S³_combined | 42 | 50 | [
[
"algorithms",
"simulation",
"sample",
"sampling",
"simulations",
"simulated",
"streams",
"easily",
"rapid",
"streaming"
],
[
"boosting",
"consistently",
"cnns",
"multimodal",
"classifiers",
"accelerators",
"generalization",
"markov",
"correlation",
"correlations"
],
[
"stability",
"bayes",
"hessian",
"nonlinear",
"stable",
"primary",
"hidden",
"into",
"laplacian",
"handling"
],
[
"adaptive",
"embeddings",
"classifiers",
"sensor",
"applicability",
"observations",
"optimization",
"sensors",
"datasets",
"mini"
],
[
"rates",
"capacity",
"effort",
"individuals",
"collection",
"rate",
"hierarchy",
"likelihood",
"amounts",
"tasks"
],
[
"uncertainty",
"uncertain",
"averaged",
"consensus",
"calibrated",
"calibration",
"uncertainties",
"reliability",
"reliable",
"reliably"
],
[
"autoencoders",
"vae",
"autoencoder",
"encode",
"encoder",
"encodes",
"encoding",
"encoded",
"encoders",
"decoder"
],
[
"classifier",
"svm",
"supervised",
"predicting",
"classifiers",
"supervision",
"boosting",
"markov",
"predictor",
"covariance"
],
[
"development",
"code",
"coding",
"programming",
"github",
"program",
"runtime",
"developers",
"frameworks",
"testing"
],
[
"solving",
"questions",
"assume",
"solve",
"solver",
"solves",
"solution",
"assuming",
"algebra",
"solvers"
],
[
"forest",
"branch",
"up",
"hybrid",
"boosting",
"boost",
"tree",
"imbalanced",
"logistic",
"lightweight"
],
[
"combination",
"domains",
"combining",
"separately",
"schemes",
"modalities",
"separate",
"combine",
"momentum",
"domain"
],
[
"follow",
"phases",
"vast",
"stages",
"aim",
"possible",
"navigation",
"twitter",
"tracking",
"lot"
],
[
"medical",
"diagnosis",
"patients",
"diseases",
"clinical",
"disease",
"mri",
"healthcare",
"health",
"medicine"
],
[
"canonical",
"identifying",
"cnns",
"detector",
"identified",
"equivalent",
"detection",
"system",
"identification",
"identical"
],
[
"bandits",
"explore",
"exploration",
"optimality",
"greedy",
"fail",
"advantage",
"regret",
"bandit",
"exploring"
],
[
"action",
"severely",
"severe",
"each",
"events",
"classes",
"jointly",
"event",
"impact",
"individually"
],
[
"inferring",
"bayesian",
"coupled",
"correlations",
"statistics",
"estimators",
"correlated",
"likelihood",
"infer",
"convolutions"
],
[
"turns",
"euclidean",
"coherent",
"probabilistic",
"ubiquitous",
"favorable",
"landscape",
"sentences",
"recognizing",
"superiority"
],
[
"particle",
"quantum",
"quantization",
"coupling",
"quantized",
"iot",
"sensing",
"rigorously",
"tuning",
"hilbert"
],
[
"summarize",
"clusters",
"clustering",
"cluster",
"partitioning",
"subset",
"partition",
"segment",
"subsets",
"summary"
],
[
"optimality",
"extreme",
"constraints",
"threshold",
"highlights",
"exact",
"minimal",
"short",
"particular",
"optimal"
],
[
"solver",
"mismatch",
"reconstructing",
"rnn",
"rnns",
"backpropagation",
"reconstruct",
"sequential",
"forgetting",
"imputation"
],
[
"semantically",
"signals",
"nlp",
"semantic",
"signal",
"corpus",
"output",
"interpretable",
"directed",
"predictive"
],
[
"weather",
"forecasts",
"grid",
"forecasting",
"landscape",
"forecast",
"forest",
"trees",
"cloud",
"region"
],
[
"generalizing",
"approximations",
"asymptotic",
"generalizability",
"generalization",
"embeddings",
"learning",
"dimensionality",
"asymptotically",
"overfitting"
],
[
"algorithmic",
"decoding",
"structures",
"structure",
"recursive",
"theory",
"complexities",
"complexity",
"complicated",
"hierarchy"
],
[
"translate",
"translation",
"rnn",
"rnns",
"localization",
"alignment",
"focusing",
"lstm",
"decoder",
"attention"
],
[
"distinguishing",
"nlp",
"corpora",
"expressive",
"challenge",
"imagenet",
"prove",
"corpus",
"engine",
"distinguish"
],
[
"classifiers",
"stacked",
"combinatorial",
"combinations",
"allocation",
"capacity",
"factor",
"tensors",
"partitioning",
"classifier"
],
[
"bayes",
"mcmc",
"markov",
"simulations",
"simulation",
"posterior",
"probabilistic",
"sampling",
"stochastic",
"bayesian"
],
[
"autoregressive",
"financial",
"predicting",
"forecast",
"forecasting",
"forecasts",
"chain",
"process",
"market",
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],
[
"solvers",
"models",
"queries",
"filtering",
"filter",
"recommendations",
"augment",
"filters",
"constraints",
"criteria"
],
[
"outliers",
"outlier",
"deviation",
"anomalies",
"anomaly",
"deviations",
"investigate",
"phenomenon",
"intrusion",
"imbalance"
],
[
"discover",
"combinations",
"biology",
"abstract",
"cell",
"protein",
"combination",
"cells",
"molecular",
"shared"
],
[
"voice",
"audio",
"music",
"speaker",
"resonance",
"acoustic",
"sound",
"recorded",
"stage",
"tune"
],
[
"covariance",
"channels",
"combinatorial",
"pairwise",
"coverage",
"squares",
"subspace",
"subspaces",
"communications",
"channel"
],
[
"matrices",
"tensors",
"tensor",
"fold",
"vectors",
"decomposition",
"compute",
"matrix",
"vector",
"decompose"
],
[
"sparse",
"denoising",
"sparsity",
"compressed",
"reconstruct",
"compress",
"compression",
"reconstructed",
"lacks",
"imputation"
],
[
"interpretability",
"understand",
"explanations",
"interpretable",
"explainable",
"intuitive",
"explanation",
"interpret",
"visualization",
"explaining"
],
[
"capacity",
"industry",
"densities",
"estimate",
"expect",
"expected",
"storage",
"availability",
"estimates",
"expectation"
],
[
"drive",
"driving",
"driven",
"vehicles",
"traffic",
"vehicle",
"car",
"f1",
"transportation",
"gps"
],
[
"regularization",
"regularized",
"devise",
"malware",
"iot",
"regular",
"formalize",
"free",
"membership",
"basic"
],
[
"hierarchical",
"characterize",
"oracle",
"distinguishing",
"characterization",
"distinguish",
"industrial",
"architectural",
"measurement",
"architectures"
],
[
"similarity",
"metrics",
"distance",
"distances",
"nearest",
"metric",
"spatially",
"compared",
"near",
"compare"
],
[
"annotation",
"annotated",
"labeling",
"labels",
"noisy",
"label",
"labeled",
"unlabeled",
"costly",
"annotations"
],
[
"newton",
"robotic",
"hand",
"arm",
"arms",
"robotics",
"robot",
"pose",
"object",
"objects"
],
[
"quantization",
"probability",
"robots",
"entropy",
"svm",
"spectral",
"densities",
"optical",
"robot",
"robotic"
],
[
"outside",
"limited",
"trajectories",
"transient",
"inside",
"restricted",
"constrained",
"captures",
"varying",
"kinds"
],
[
"generative",
"deterministic",
"variational",
"sequences",
"interactive",
"graphical",
"sequential",
"predictive",
"incomplete",
"probabilistic"
]
] | 2.665469 | all-mpnet-base-v2 | 0.886 | -0.340064 | 0.201313 | 0.913525 |
BBC News | S³_angular | 42 | 10 | [
[
"organisers",
"spokesperson",
"spokesman",
"host",
"leader",
"mp",
"territory",
"principal",
"takes",
"tony"
],
[
"affairs",
"refuse",
"deny",
"appointed",
"dispute",
"circumstances",
"scandal",
"fairly",
"allegations",
"opposition"
],
[
"housing",
"fact",
"social",
"buildings",
"statement",
"statements",
"property",
"prime",
"meetings",
"subject"
],
[
"ourselves",
"surplus",
"employment",
"tax",
"taxes",
"waste",
"conservatives",
"taxpayers",
"understanding",
"welfare"
],
[
"breakfast",
"federer",
"showing",
"correspondent",
"tournament",
"roddick",
"maria",
"serve",
"may",
"according"
],
[
"house",
"polls",
"clive",
"wasps",
"content",
"statistics",
"bob",
"limits",
"pushing",
"tim"
],
[
"singer",
"album",
"albums",
"concert",
"singing",
"rock",
"bands",
"together",
"drake",
"performers"
],
[
"cap",
"gartner",
"product",
"package",
"quick",
"co",
"search",
"find",
"profile",
"confirm"
],
[
"arrival",
"starring",
"actor",
"actress",
"oscars",
"sequel",
"acting",
"film",
"movie",
"director"
],
[
"america",
"countries",
"china",
"india",
"representatives",
"canada",
"usa",
"australia",
"politicians",
"lists"
]
] | 2.124526 | all-mpnet-base-v2 | 1 | -0.353604 | 0.157331 | 0.894448 |
BBC News | S³_angular | 42 | 20 | [
[
"striker",
"midfield",
"mourinho",
"madrid",
"chelsea",
"arsenal",
"goalkeeper",
"midfielder",
"liverpool",
"everton"
],
[
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[
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],
[
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[
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[
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[
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[
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[
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[
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[
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[
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"46",
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[
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[
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"activists"
],
[
"earth",
"trillion",
"africa",
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],
[
"conditions",
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],
[
"jonathan",
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[
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[
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"putin",
"russian",
"russia",
"mikhail",
"yukos",
"gazprom",
"vladimir",
"oil",
"texas"
]
] | 2.305272 | all-mpnet-base-v2 | 0.995 | -0.29666 | 0.195131 | 0.915997 |
BBC News | S³_angular | 42 | 30 | [
[
"re",
"departments",
"mps",
"lions",
"reviews",
"rbs",
"officers",
"organisations",
"investigating",
"others"
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[
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"brown",
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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"upon",
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[
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"eyes",
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[
"gadgets",
"device",
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"laptops",
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"portable",
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"camera",
"mobiles",
"handheld"
],
[
"neil",
"woodward",
"gartner",
"andy",
"liam",
"3bn",
"4bn",
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"35bn",
"5bn"
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[
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"morgan",
"wasps",
"ll",
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"limit",
"spokeswoman",
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"jean",
"margaret"
],
[
"democrat",
"lib",
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"politically",
"dem",
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],
[
"standing",
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"table",
"royal",
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"afford",
"kate",
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],
[
"collins",
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],
[
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"billion",
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[
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"71",
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[
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"bankruptcy",
"tax",
"case",
"lawsuits",
"policy"
]
] | 2.250851 | all-mpnet-base-v2 | 0.953333 | -0.329348 | 0.201645 | 0.898081 |
BBC News | S³_angular | 42 | 40 | [
[
"mortgage",
"recession",
"bath",
"rate",
"january",
"house",
"lending",
"inflation",
"rates",
"homes"
],
[
"french",
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"rome",
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"increasing",
"lisbon",
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],
[
"broadcast",
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],
[
"interested",
"argentina",
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"carlos",
"spanish"
],
[
"google",
"search",
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"think",
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"seeking",
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"advertising"
],
[
"useful",
"grant",
"silk",
"clive",
"woodward",
"imf",
"staunton",
"ian",
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"via"
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[
"spokesperson",
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[
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[
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[
"windows",
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[
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],
[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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]
] | 2.69438 | all-mpnet-base-v2 | 0.9775 | -0.311388 | 0.18726 | 0.897996 |
BBC News | S³_angular | 42 | 50 | [
[
"judgement",
"blamed",
"offence",
"talk",
"blame",
"communicate",
"face",
"appeal",
"affect",
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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] | 2.602298 | all-mpnet-base-v2 | 0.97 | -0.306987 | 0.21568 | 0.909207 |
BBC News | S³_combined | 42 | 10 | [
[
"presenter",
"host",
"organisers",
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"hosted",
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] | 2.128947 | all-mpnet-base-v2 | 0.95 | -0.282857 | 0.260102 | 0.926833 |
BBC News | S³_combined | 42 | 20 | [
[
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] | 2.232358 | all-mpnet-base-v2 | 0.98 | -0.267791 | 0.227021 | 0.934955 |
BBC News | S³_combined | 42 | 30 | [
[
"rbs",
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] | 2.269293 | all-mpnet-base-v2 | 0.946667 | -0.31255 | 0.213746 | 0.918759 |
BBC News | S³_combined | 42 | 40 | [
[
"mortgage",
"bath",
"recession",
"inflation",
"rate",
"january",
"lending",
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] | 2.550105 | all-mpnet-base-v2 | 0.94 | -0.309918 | 0.204181 | 0.917429 |
BBC News | S³_combined | 42 | 50 | [
[
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20 Newsgroups Raw | S³_angular | 42 | 10 | [
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20 Newsgroups Raw | S³_angular | 42 | 20 | [
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20 Newsgroups Raw | S³_angular | 42 | 30 | [
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20 Newsgroups Raw | S³_angular | 42 | 40 | [
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20 Newsgroups Raw | S³_angular | 42 | 50 | [
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20 Newsgroups Raw | S³_combined | 42 | 10 | [
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20 Newsgroups Raw | S³_combined | 42 | 20 | [
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] | 15.289149 | all-mpnet-base-v2 | 1 | -0.2287 | 0.257648 | 0.413361 |
20 Newsgroups Raw | S³_combined | 42 | 30 | [
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20 Newsgroups Raw | S³_combined | 42 | 40 | [
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20 Newsgroups Raw | S³_combined | 42 | 50 | [
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]
] | 15.594732 | all-mpnet-base-v2 | 0.984 | -0.225168 | 0.254145 | 0.419106 |
ArXiv ML Papers | BERTopic | 42 | 10 | [
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] | 596.737655 | intfloat/e5-large-v2 | 0.36 | -0.019652 | 0.226954 | 0.556204 |
ArXiv ML Papers | BERTopic | 42 | 20 | [
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ArXiv ML Papers | LDA | 42 | 10 | [
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ArXiv ML Papers | LDA | 42 | 20 | [
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] | 19.932253 | intfloat/e5-large-v2 | 0.426667 | -0.049465 | 0.188912 | 0.651156 |
ArXiv ML Papers | LDA | 42 | 40 | [
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ArXiv ML Papers | LDA | 42 | 50 | [
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] | 812.217862 | intfloat/e5-large-v2 | 0.32 | -0.287604 | 0.31249 | 0.76937 |
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[
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[
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"xgboost"
]
] | 32.767907 | intfloat/e5-large-v2 | 0.783333 | -0.052635 | 0.127641 | 0.744987 |
ArXiv ML Papers | CombinedTM | 42 | 40 | [
[
"context",
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[
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[
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[
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[
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[
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[
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[
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[
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[
"robust",
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"overfitting",
"noisy",
"robustness"
]
] | 31.204046 | intfloat/e5-large-v2 | 0.785 | -0.067333 | 0.11963 | 0.770256 |
ArXiv ML Papers | CombinedTM | 42 | 50 | [
[
"clinical",
"researchers",
"scientific",
"and",
"research",
"in",
"challenges",
"have",
"design",
"several"
],
[
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[
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[
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[
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],
[
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],
[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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"conduct",
"self"
]
] | 33.575866 | intfloat/e5-large-v2 | 0.786 | -0.082915 | 0.122667 | 0.778237 |
ArXiv ML Papers | ZeroShotTM | 42 | 10 | [
[
"gradient",
"regret",
"algorithm",
"function",
"stochastic",
"we",
"convergence",
"optimal",
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[
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[
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[
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"representations",
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[
"ranging",
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[
"graph",
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"linear"
]
] | 26.602624 | intfloat/e5-large-v2 | 0.86 | -0.050055 | 0.135054 | 0.729548 |
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