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int64 42
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ArXiv ML Papers | CombinedTM | 44 | 10 | [
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ArXiv ML Papers | CombinedTM | 45 | 10 | [
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] | 996.905842 | all-MiniLM-L6-v2 | 0.81 | -0.072244 | 0.138617 | 0.678641 |
ArXiv ML Papers | CombinedTM | 46 | 10 | [
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ArXiv ML Papers | CombinedTM | 43 | 20 | [
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] | 1,193.747479 | all-MiniLM-L6-v2 | 0.85 | -0.048328 | 0.146138 | 0.742329 |
ArXiv ML Papers | CombinedTM | 44 | 20 | [
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] | 1,197.092792 | all-MiniLM-L6-v2 | 0.8 | -0.04121 | 0.147587 | 0.711697 |
ArXiv ML Papers | CombinedTM | 45 | 20 | [
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ArXiv ML Papers | CombinedTM | 46 | 20 | [
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ArXiv ML Papers | CombinedTM | 43 | 30 | [
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] | 1,134.648329 | all-MiniLM-L6-v2 | 0.766667 | -0.072291 | 0.135358 | 0.743058 |
ArXiv ML Papers | CombinedTM | 44 | 30 | [
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] | 1,126.578353 | all-MiniLM-L6-v2 | 0.763333 | -0.053987 | 0.124083 | 0.72492 |
ArXiv ML Papers | CombinedTM | 45 | 30 | [
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ArXiv ML Papers | CombinedTM | 46 | 30 | [
[
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ArXiv ML Papers | CombinedTM | 43 | 40 | [
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] | 1,160.406196 | all-MiniLM-L6-v2 | 0.7575 | -0.075834 | 0.131865 | 0.759935 |
ArXiv ML Papers | CombinedTM | 44 | 40 | [
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ArXiv ML Papers | CombinedTM | 45 | 40 | [
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ArXiv ML Papers | CombinedTM | 46 | 40 | [
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ArXiv ML Papers | CombinedTM | 43 | 50 | [
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ArXiv ML Papers | CombinedTM | 44 | 50 | [
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ArXiv ML Papers | CombinedTM | 45 | 50 | [
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ArXiv ML Papers | CombinedTM | 46 | 50 | [
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ArXiv ML Papers | ZeroShotTM | 43 | 10 | [
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ArXiv ML Papers | ZeroShotTM | 44 | 10 | [
[
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ArXiv ML Papers | ZeroShotTM | 45 | 10 | [
[
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ArXiv ML Papers | ZeroShotTM | 46 | 10 | [
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ArXiv ML Papers | ZeroShotTM | 43 | 20 | [
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ArXiv ML Papers | ZeroShotTM | 44 | 20 | [
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ArXiv ML Papers | ZeroShotTM | 45 | 20 | [
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ArXiv ML Papers | ZeroShotTM | 46 | 20 | [
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ArXiv ML Papers | ZeroShotTM | 43 | 30 | [
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ArXiv ML Papers | ZeroShotTM | 44 | 30 | [
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ArXiv ML Papers | ZeroShotTM | 45 | 30 | [
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] | 1,156.536222 | all-MiniLM-L6-v2 | 0.786667 | -0.048736 | 0.135058 | 0.759984 |
ArXiv ML Papers | ZeroShotTM | 46 | 30 | [
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] | 1,155.78876 | all-MiniLM-L6-v2 | 0.773333 | -0.055739 | 0.130825 | 0.760351 |
ArXiv ML Papers | ZeroShotTM | 43 | 40 | [
[
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] | 1,199.097349 | all-MiniLM-L6-v2 | 0.7375 | -0.062373 | 0.129414 | 0.766295 |
ArXiv ML Papers | ZeroShotTM | 44 | 40 | [
[
"capture",
"representation",
"visual",
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"attention",
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] | 1,407.553352 | all-MiniLM-L6-v2 | 0.7075 | -0.051788 | 0.132117 | 0.761656 |
ArXiv ML Papers | ZeroShotTM | 45 | 40 | [
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ArXiv ML Papers | ZeroShotTM | 46 | 40 | [
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ArXiv ML Papers | ZeroShotTM | 43 | 50 | [
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ArXiv ML Papers | ZeroShotTM | 44 | 50 | [
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[
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"mobile"
]
] | 1,174.040942 | all-MiniLM-L6-v2 | 0.698 | -0.066644 | 0.130783 | 0.775953 |
ArXiv ML Papers | ZeroShotTM | 45 | 50 | [
[
"of",
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"for",
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[
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"speech",
"net",
"performance"
]
] | 1,066.117967 | all-MiniLM-L6-v2 | 0.706 | -0.065101 | 0.127012 | 0.783174 |
ArXiv ML Papers | ZeroShotTM | 46 | 50 | [
[
"networks",
"neural",
"training",
"activation",
"depth",
"adversarial",
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"relu",
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] | 1,220.778715 | all-MiniLM-L6-v2 | 0.708 | -0.066256 | 0.129866 | 0.77281 |
ArXiv ML Papers | ECRTM | 43 | 10 | [
[
"attack",
"perturbation",
"perturbations",
"adversarial",
"defense",
"against",
"gan",
"attacks",
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"transformer",
"speech",
"pruning",
"convolutional",
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],
[
"research",
"social",
"health",
"questions",
"human",
"ml",
"researchers",
"software",
"survey",
"practices"
],
[
"relations",
"probabilistic",
"media",
"pipelines",
"supervised",
"label",
"games",
"margin",
"hierarchical",
"boosting"
],
[
"posterior",
"bayesian",
"carlo",
"variables",
"flows",
"forecasting",
"variational",
"monte",
"likelihood",
"density"
],
[
"covid",
"vehicle",
"patients",
"detection",
"cancer",
"traffic",
"ct",
"iot",
"day",
"driving"
],
[
"quantum",
"gradient",
"descent",
"convergence",
"convex",
"tensor",
"accelerated",
"rank",
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],
[
"mathbb",
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"bandit",
"estimator",
"sqrt",
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"bound",
"bounds",
"delta"
],
[
"embedding",
"node",
"clustering",
"graphs",
"embeddings",
"nodes",
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"metric",
"link",
"vertex"
],
[
"policy",
"agent",
"reinforcement",
"rl",
"agents",
"reward",
"environment",
"action",
"actions",
"robot"
]
] | 4,127.300286 | all-MiniLM-L6-v2 | 1 | -0.063325 | 0.131136 | 0.902187 |
ArXiv ML Papers | ECRTM | 44 | 10 | [
[
"adversarial",
"perturbations",
"attacks",
"defense",
"gan",
"perturbation",
"relu",
"gans",
"robustness",
"pooling"
],
[
"bandit",
"sqrt",
"epsilon",
"regret",
"bounds",
"convex",
"delta",
"bound",
"log",
"varepsilon"
],
[
"graph",
"graphs",
"node",
"nodes",
"gnns",
"clustering",
"embeddings",
"embedding",
"link",
"matrix"
],
[
"density",
"variational",
"carlo",
"forecasting",
"dynamics",
"flows",
"monte",
"posterior",
"variables",
"autoregressive"
],
[
"media",
"research",
"researchers",
"questions",
"human",
"social",
"software",
"ml",
"health",
"survey"
],
[
"environment",
"reinforcement",
"iot",
"attacks",
"agent",
"rl",
"policy",
"attack",
"robot",
"driving"
],
[
"patients",
"cancer",
"day",
"detection",
"19",
"ct",
"forest",
"covid",
"segmentation",
"patient"
],
[
"boosting",
"correlated",
"explanations",
"pac",
"bayesian",
"concepts",
"probabilistic",
"sum",
"bounds",
"margin"
],
[
"hardware",
"pruning",
"accelerators",
"quantization",
"scaling",
"devices",
"bit",
"memory",
"convolution",
"parallel"
],
[
"language",
"encoder",
"visual",
"audio",
"transformer",
"pre",
"speech",
"text",
"style",
"decoder"
]
] | 4,688.849451 | all-MiniLM-L6-v2 | 0.98 | -0.099419 | 0.139801 | 0.918459 |
ArXiv ML Papers | ECRTM | 45 | 10 | [
[
"media",
"games",
"library",
"pipelines",
"gpu",
"implementations",
"tensor",
"sum",
"solving",
"solvers"
],
[
"decoder",
"encoder",
"speech",
"language",
"transformer",
"audio",
"languages",
"word",
"text",
"speaker"
],
[
"node",
"graph",
"link",
"nodes",
"graphs",
"gnns",
"embedding",
"embeddings",
"matrix",
"spectral"
],
[
"bounds",
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"convex",
"regret",
"bound",
"estimator",
"delta",
"mathbb",
"varepsilon",
"sqrt"
],
[
"policy",
"agent",
"rl",
"agents",
"reward",
"reinforcement",
"actions",
"environment",
"action",
"robot"
],
[
"variational",
"carlo",
"posterior",
"flows",
"density",
"monte",
"approximate",
"variables",
"forecasting",
"distributions"
],
[
"researchers",
"questions",
"ml",
"social",
"software",
"human",
"research",
"health",
"practices",
"ai"
],
[
"covid",
"cancer",
"ct",
"patients",
"patient",
"day",
"detection",
"traffic",
"svm",
"driving"
],
[
"gan",
"perturbations",
"adversarial",
"contrastive",
"image",
"attacks",
"unlabeled",
"gans",
"supervised",
"labeled"
],
[
"attacks",
"attack",
"iot",
"devices",
"federated",
"privacy",
"server",
"hardware",
"security",
"pruning"
]
] | 4,159.961052 | all-MiniLM-L6-v2 | 0.99 | -0.074917 | 0.139443 | 0.911483 |
ArXiv ML Papers | ECRTM | 46 | 10 | [
[
"monte",
"distributions",
"variables",
"posterior",
"variational",
"carlo",
"density",
"bayesian",
"likelihood",
"uncertainty"
],
[
"subspace",
"norm",
"subspaces",
"kernels",
"relu",
"equations",
"convex",
"approximation",
"dimension",
"matrix"
],
[
"attacks",
"adversarial",
"perturbations",
"attack",
"defense",
"perturbation",
"against",
"robustness",
"attacker",
"gan"
],
[
"patient",
"cancer",
"traffic",
"patients",
"covid",
"day",
"vehicle",
"ct",
"forest",
"sensors"
],
[
"quantization",
"hardware",
"accelerators",
"pruning",
"memory",
"convolution",
"nas",
"module",
"net",
"layer"
],
[
"bayesian",
"pac",
"cross",
"probabilistic",
"supervised",
"margin",
"boosting",
"entropy",
"fair",
"games"
],
[
"research",
"engineering",
"ml",
"researchers",
"robot",
"rl",
"development",
"social",
"media",
"physics"
],
[
"bandit",
"regret",
"policy",
"agent",
"sqrt",
"agents",
"reward",
"epsilon",
"communication",
"rl"
],
[
"contrastive",
"nodes",
"node",
"embeddings",
"vertex",
"graph",
"graphs",
"metric",
"embedding",
"protein"
],
[
"audio",
"language",
"word",
"words",
"speech",
"gender",
"text",
"visual",
"languages",
"bias"
]
] | 4,646.624485 | all-MiniLM-L6-v2 | 0.98 | -0.13126 | 0.150766 | 0.923685 |
ArXiv ML Papers | ECRTM | 43 | 20 | [
[
"gan",
"adversarial",
"generator",
"gans",
"contrastive",
"perturbations",
"images",
"augmentation",
"image",
"camera"
],
[
"3d",
"net",
"enhancement",
"demand",
"videos",
"video",
"resolution",
"speech",
"module",
"spatial"
],
[
"sqrt",
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"entries",
"tensor",
"epsilon",
"matrix",
"completion",
"regret",
"rank",
"series"
],
[
"classifiers",
"mathbb",
"calibration",
"quantum",
"confidence",
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"label",
"bias",
"channel",
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],
[
"questions",
"compliance",
"social",
"software",
"researchers",
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"engineering",
"ml",
"human",
"health"
],
[
"cancer",
"patients",
"ct",
"covid",
"19",
"patient",
"cell",
"disease",
"curve",
"diagnosis"
],
[
"pruning",
"quantization",
"hardware",
"accelerators",
"nas",
"transformers",
"precision",
"bit",
"scaling",
"memory"
],
[
"equations",
"physics",
"geometric",
"metric",
"informed",
"cancer",
"equation",
"distance",
"shape",
"differential"
],
[
"attacks",
"attack",
"traffic",
"iot",
"security",
"defense",
"service",
"adversarial",
"malicious",
"vehicle"
],
[
"text",
"language",
"languages",
"style",
"sentence",
"speech",
"speaker",
"word",
"sentences",
"audio"
],
[
"directions",
"pipelines",
"coding",
"clinical",
"review",
"taxonomy",
"channel",
"media",
"survey",
"library"
],
[
"graph",
"nodes",
"link",
"graphs",
"node",
"protein",
"embeddings",
"embedding",
"vertex",
"item"
],
[
"posterior",
"monte",
"divergence",
"carlo",
"variational",
"likelihood",
"variables",
"bayesian",
"approximate",
"density"
],
[
"bayesian",
"cross",
"factorization",
"tensor",
"pac",
"margin",
"entropy",
"programming",
"probabilistic",
"base"
],
[
"convergence",
"convex",
"proximal",
"stochastic",
"descent",
"max",
"accelerated",
"gradient",
"min",
"epsilon"
],
[
"rl",
"reward",
"agents",
"agent",
"policy",
"action",
"actions",
"reinforcement",
"robot",
"environment"
],
[
"width",
"pooling",
"activation",
"initialization",
"layer",
"relu",
"separable",
"filters",
"gnns",
"teacher"
],
[
"forecasts",
"concepts",
"explanation",
"forecasting",
"explanations",
"forecast",
"interpretability",
"individual",
"black",
"relations"
],
[
"frac",
"graphs",
"arm",
"private",
"delta",
"left",
"fairness",
"laplacian",
"bandit",
"agents"
],
[
"federated",
"student",
"centralized",
"devices",
"quantum",
"server",
"decentralized",
"teacher",
"domain",
"central"
]
] | 21,789.126004 | all-MiniLM-L6-v2 | 0.945 | -0.132215 | 0.141669 | 0.925237 |
ArXiv ML Papers | ECRTM | 44 | 20 | [
[
"ml",
"questions",
"research",
"researchers",
"subjects",
"compliance",
"social",
"health",
"human",
"treatment"
],
[
"reward",
"agent",
"action",
"robot",
"policy",
"policies",
"reinforcement",
"rl",
"actions",
"planning"
],
[
"eeg",
"pipelines",
"channel",
"image",
"manipulation",
"extraction",
"concepts",
"media",
"kinds",
"channels"
],
[
"convex",
"proximal",
"convergence",
"descent",
"stochastic",
"accelerated",
"minimization",
"epsilon",
"gradient",
"min"
],
[
"recommendation",
"user",
"item",
"items",
"audio",
"face",
"video",
"videos",
"recommender",
"users"
],
[
"accelerators",
"hardware",
"quantization",
"consumption",
"latency",
"bit",
"device",
"memory",
"mobile",
"devices"
],
[
"activation",
"width",
"relu",
"mathbb",
"pooling",
"regime",
"calibration",
"perturbations",
"invariant",
"theory"
],
[
"link",
"nodes",
"metric",
"graph",
"embedding",
"embeddings",
"node",
"vertex",
"graphs",
"distance"
],
[
"density",
"divergence",
"posterior",
"variables",
"carlo",
"likelihood",
"variational",
"monte",
"mcmc",
"approximate"
],
[
"game",
"delta",
"recovery",
"laplacian",
"quantum",
"fairness",
"frac",
"graph",
"graphs",
"sum"
],
[
"layers",
"nas",
"convolution",
"pruning",
"net",
"gnns",
"transformers",
"layer",
"recurrent",
"architectures"
],
[
"19",
"forest",
"patients",
"cancer",
"ct",
"covid",
"day",
"svm",
"automated",
"net"
],
[
"sentences",
"corpus",
"text",
"language",
"languages",
"word",
"speech",
"sentence",
"asr",
"speaker"
],
[
"attack",
"adversarial",
"perturbations",
"attacks",
"defense",
"privacy",
"vulnerable",
"attacker",
"perturbation",
"defend"
],
[
"bandit",
"regret",
"sqrt",
"epsilon",
"communication",
"bandits",
"agent",
"log",
"bound",
"armed"
],
[
"images",
"image",
"camera",
"cell",
"contrastive",
"object",
"segmentation",
"supervision",
"scene",
"unlabeled"
],
[
"bounds",
"probabilistic",
"cross",
"margin",
"bayesian",
"boosting",
"pac",
"entropy",
"bound",
"generalization"
],
[
"software",
"scientific",
"survey",
"review",
"engineering",
"researchers",
"development",
"clinical",
"computer",
"directions"
],
[
"vehicle",
"equations",
"channel",
"signals",
"teacher",
"iot",
"vehicles",
"traffic",
"student",
"driving"
],
[
"forecasting",
"forecasts",
"tensor",
"forecast",
"series",
"weather",
"completion",
"rank",
"missing",
"anomaly"
]
] | 21,816.13647 | all-MiniLM-L6-v2 | 0.95 | -0.110595 | 0.144156 | 0.920937 |
ArXiv ML Papers | ECRTM | 45 | 20 | [
[
"channels",
"explanations",
"coding",
"channel",
"explanation",
"quantum",
"calibration",
"concepts",
"eeg",
"transmission"
],
[
"ml",
"social",
"fairness",
"subjects",
"questions",
"researchers",
"research",
"health",
"human",
"individuals"
],
[
"kernel",
"label",
"delta",
"fairness",
"frac",
"kernels",
"nearest",
"neighbor",
"regression",
"theoretic"
],
[
"monte",
"variational",
"mcmc",
"variables",
"autoregressive",
"posterior",
"forecasting",
"forecasts",
"forecast",
"likelihood"
],
[
"net",
"feature",
"3d",
"audio",
"cnn",
"enhancement",
"videos",
"frame",
"spatial",
"video"
],
[
"health",
"cancer",
"disease",
"covid",
"patients",
"ct",
"patient",
"19",
"diagnosis",
"day"
],
[
"sqrt",
"epsilon",
"communication",
"regret",
"bandit",
"varepsilon",
"delta",
"log",
"distributed",
"frac"
],
[
"attacks",
"platforms",
"detection",
"service",
"attack",
"malware",
"iot",
"detect",
"software",
"bias"
],
[
"graph",
"link",
"gnns",
"graphs",
"node",
"nodes",
"protein",
"embeddings",
"matrix",
"gnn"
],
[
"concepts",
"recurrent",
"pac",
"probabilistic",
"operate",
"bayesian",
"hierarchical",
"turn",
"forecast",
"relations"
],
[
"rl",
"robot",
"reinforcement",
"reward",
"action",
"agent",
"policy",
"policies",
"agents",
"actions"
],
[
"pooling",
"width",
"layer",
"relu",
"activation",
"depth",
"regime",
"initialization",
"mathbb",
"neuron"
],
[
"sentence",
"language",
"word",
"speech",
"text",
"sentences",
"audio",
"languages",
"words",
"speaker"
],
[
"subspace",
"signals",
"cluster",
"clustering",
"teacher",
"student",
"clusters",
"ranking",
"filter",
"nas"
],
[
"machines",
"quantum",
"science",
"metric",
"scientific",
"distance",
"queries",
"informed",
"dl",
"physics"
],
[
"quantization",
"bit",
"hardware",
"consumption",
"accelerators",
"pruning",
"memory",
"scaling",
"convolution",
"nas"
],
[
"convex",
"convergence",
"proximal",
"variational",
"stochastic",
"momentum",
"accelerated",
"gradient",
"descent",
"variance"
],
[
"contrastive",
"image",
"segmentation",
"images",
"supervision",
"cell",
"object",
"unlabeled",
"labeled",
"supervised"
],
[
"perturbation",
"federated",
"perturbations",
"attacks",
"defense",
"privacy",
"adversarial",
"attack",
"private",
"vulnerable"
],
[
"survey",
"media",
"games",
"pipelines",
"directions",
"gans",
"clinical",
"review",
"taxonomy",
"library"
]
] | 21,868.923991 | all-MiniLM-L6-v2 | 0.94 | -0.12843 | 0.152332 | 0.9172 |
ArXiv ML Papers | ECRTM | 46 | 20 | [
[
"clustering",
"subspace",
"subspaces",
"mathbb",
"clusters",
"kernel",
"laplacian",
"recovery",
"semi",
"regression"
],
[
"labeled",
"cell",
"unlabeled",
"contrastive",
"segmentation",
"supervised",
"supervision",
"images",
"annotation",
"augmentation"
],
[
"tilde",
"regret",
"epsilon",
"sqrt",
"varepsilon",
"bandit",
"communication",
"bounds",
"log",
"frac"
],
[
"defense",
"attacks",
"adversarial",
"perturbations",
"attack",
"privacy",
"perturbation",
"attacker",
"vulnerable",
"private"
],
[
"nas",
"gnns",
"pruning",
"quantization",
"scaling",
"pruned",
"gnn",
"convolution",
"com",
"github"
],
[
"style",
"generative",
"student",
"variables",
"latent",
"generator",
"vae",
"teacher",
"auxiliary",
"autoencoder"
],
[
"width",
"equations",
"dynamical",
"boundary",
"theory",
"activation",
"physics",
"relu",
"informed",
"equation"
],
[
"metric",
"classifier",
"label",
"calibration",
"channels",
"channel",
"eeg",
"decision",
"screening",
"candidate"
],
[
"language",
"languages",
"word",
"speech",
"sentence",
"transformer",
"speaker",
"english",
"bleu",
"corpus"
],
[
"recurrent",
"solvers",
"kernels",
"correlated",
"resolution",
"games",
"compositional",
"meta",
"sum",
"principled"
],
[
"attacks",
"attack",
"iot",
"vehicle",
"service",
"traffic",
"detect",
"vehicles",
"driving",
"detection"
],
[
"patients",
"causal",
"series",
"forecast",
"treatment",
"patient",
"forecasts",
"missing",
"forecasting",
"records"
],
[
"agent",
"action",
"policies",
"policy",
"reinforcement",
"reward",
"rl",
"robot",
"actions",
"agents"
],
[
"3d",
"video",
"segmentation",
"frame",
"net",
"convolution",
"frames",
"audio",
"videos",
"cnn"
],
[
"computing",
"federated",
"quantum",
"decentralized",
"transmission",
"server",
"bandwidth",
"iteration",
"devices",
"centralized"
],
[
"survey",
"researchers",
"research",
"scientific",
"ml",
"review",
"questions",
"software",
"challenges",
"development"
],
[
"graphs",
"link",
"graph",
"nodes",
"embeddings",
"topic",
"recommendation",
"embedding",
"metric",
"vertex"
],
[
"variational",
"carlo",
"bayesian",
"posterior",
"approximations",
"monte",
"flows",
"flow",
"normalizing",
"estimators"
],
[
"cancer",
"day",
"covid",
"forest",
"19",
"patients",
"weather",
"ct",
"was",
"were"
],
[
"items",
"traffic",
"media",
"transfer",
"recommendation",
"incremental",
"taxonomy",
"pipelines",
"recommender",
"temporal"
]
] | 22,149.969739 | all-MiniLM-L6-v2 | 0.96 | -0.140105 | 0.140342 | 0.907562 |
ArXiv ML Papers | ECRTM | 43 | 30 | [
[
"attack",
"attacks",
"defense",
"perturbations",
"vulnerable",
"adversarial",
"attacker",
"perturbation",
"vulnerability",
"poisoning"
],
[
"series",
"anomaly",
"signals",
"speech",
"delay",
"music",
"audio",
"temporal",
"event",
"aggregation"
],
[
"equations",
"mutual",
"divergence",
"variables",
"logistic",
"vae",
"regression",
"kernel",
"operator",
"boundary"
],
[
"quantum",
"regime",
"exponentially",
"exponential",
"polynomial",
"relu",
"calibration",
"bounds",
"classifiers",
"confidence"
],
[
"treatment",
"social",
"practices",
"explanation",
"eye",
"interventions",
"patients",
"intelligence",
"explain",
"effects"
],
[
"language",
"alignment",
"speech",
"languages",
"speaker",
"voice",
"character",
"source",
"text",
"bert"
],
[
"contrastive",
"shot",
"reasoning",
"unlabeled",
"supervision",
"supervised",
"distillation",
"event",
"self",
"forgetting"
],
[
"diversity",
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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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"ct",
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"eeg",
"filter",
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],
[
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"gaussian",
"converges",
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],
[
"clinical",
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"software",
"engineering",
"review",
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"challenges"
],
[
"metric",
"questions",
"documents",
"ml",
"distance",
"social",
"queries",
"stream",
"granularity",
"recommendations"
],
[
"counterfactual",
"explanations",
"explainable",
"boosting",
"concepts",
"explanation",
"explaining",
"base",
"black",
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],
[
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],
[
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],
[
"accelerated",
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"max",
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"convex",
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"min",
"nonconvex"
]
] | 21,010.94308 | all-MiniLM-L6-v2 | 0.893333 | -0.177103 | 0.133445 | 0.924199 |
ArXiv ML Papers | ECRTM | 44 | 30 | [
[
"shift",
"biases",
"unbiased",
"camera",
"bias",
"grained",
"million",
"scaling",
"strategies",
"changes"
],
[
"completion",
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"tensor",
"nonnegative",
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],
[
"words",
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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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],
[
"vehicles",
"iot",
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],
[
"kernel",
"inverse",
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"flows",
"temperature",
"informed",
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"kernels",
"normalizing",
"flow"
]
] | 121.898181 | all-MiniLM-L6-v2 | 0.9 | -0.157026 | 0.134444 | 0.917003 |
ArXiv ML Papers | ECRTM | 45 | 30 | [
[
"pruning",
"quantization",
"memory",
"hardware",
"pruned",
"scaling",
"nas",
"convolution",
"initialization",
"compact"
],
[
"differential",
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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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"logistic",
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"feature",
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],
[
"gan",
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"privacy",
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],
[
"detect",
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"traffic",
"vehicles",
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"iot",
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],
[
"language",
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],
[
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"day",
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],
[
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],
[
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],
[
"link",
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],
[
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],
[
"defense",
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[
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[
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[
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],
[
"monte",
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],
[
"pipelines",
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"computer",
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"gans",
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],
[
"completion",
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],
[
"social",
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"compliance",
"subjects",
"fairness",
"questions",
"ml",
"biases"
]
] | 121.165248 | all-MiniLM-L6-v2 | 0.933333 | -0.133228 | 0.134941 | 0.918399 |
ArXiv ML Papers | ECRTM | 46 | 30 | [
[
"activation",
"kernels",
"mixed",
"infinite",
"probabilistic",
"asymptotic",
"base",
"compositional",
"media",
"programming"
],
[
"gnns",
"graphs",
"link",
"node",
"nodes",
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"embeddings",
"vertex",
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"neighborhood"
],
[
"private",
"gan",
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"privacy",
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"generator",
"discriminator"
],
[
"ml",
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],
[
"bandit",
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],
[
"recommendation",
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],
[
"supervised",
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"cifar",
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],
[
"channel",
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"speech",
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"cnn",
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],
[
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"matrix",
"entries",
"rank",
"series",
"missing",
"anomaly",
"decomposition",
"patient"
],
[
"style",
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"nas",
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],
[
"explanations",
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],
[
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],
[
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],
[
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],
[
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],
[
"survey",
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"clinical",
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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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"conditional",
"logistic"
]
] | 117.883204 | all-MiniLM-L6-v2 | 0.906667 | -0.159988 | 0.135218 | 0.91787 |
ArXiv ML Papers | ECRTM | 43 | 40 | [
[
"compliance",
"quantum",
"subjects",
"human",
"individuals",
"ml",
"researchers",
"questions",
"interventions",
"social"
],
[
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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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"malware"
]
] | 95.518122 | all-MiniLM-L6-v2 | 0.93 | -0.199357 | 0.12476 | 0.924882 |
ArXiv ML Papers | ECRTM | 44 | 40 | [
[
"convex",
"proximal",
"minimax",
"convergence",
"stochastic",
"epsilon",
"descent",
"min",
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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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"quantization",
"variation"
]
] | 139.658376 | all-MiniLM-L6-v2 | 0.885 | -0.201981 | 0.127813 | 0.915715 |
ArXiv ML Papers | ECRTM | 45 | 40 | [
[
"varepsilon",
"frac",
"kernels",
"kernel",
"omega",
"hilbert",
"delta",
"laplacian",
"recovery",
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] | 129.142575 | all-MiniLM-L6-v2 | 0.96 | -0.192391 | 0.13562 | 0.923463 |
ArXiv ML Papers | ECRTM | 46 | 40 | [
[
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[
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[
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]
] | 139.110896 | all-MiniLM-L6-v2 | 0.9625 | -0.212002 | 0.130715 | 0.927364 |
ArXiv ML Papers | ECRTM | 43 | 50 | [
[
"imaging",
"check",
"unclear",
"minimizing",
"spatially",
"surface",
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"whilst",
"cross",
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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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"arises",
"provably"
]
] | 106.329468 | all-MiniLM-L6-v2 | 0.956 | -0.246896 | 0.119947 | 0.927086 |
ArXiv ML Papers | ECRTM | 44 | 50 | [
[
"decisions",
"protein",
"help",
"universal",
"explain",
"encoding",
"activity",
"predict",
"behavior",
"genetic"
],
[
"aspect",
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"bleu",
"automatically",
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],
[
"languages",
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"words",
"english",
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],
[
"queries",
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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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"node",
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"edges",
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],
[
"pac",
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"mutual",
"entropy",
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[
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[
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],
[
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[
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"throughput"
]
] | 94.302951 | all-MiniLM-L6-v2 | 0.936 | -0.235821 | 0.129265 | 0.925652 |
ArXiv ML Papers | ECRTM | 45 | 50 | [
[
"agent",
"rl",
"robot",
"policy",
"reward",
"robotic",
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"reinforcement",
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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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"2020"
]
] | 91.016291 | all-MiniLM-L6-v2 | 0.97 | -0.263356 | 0.120231 | 0.926986 |
ArXiv ML Papers | ECRTM | 46 | 50 | [
[
"preliminary",
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"equations",
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"recommendation",
"sensing",
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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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[
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[
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[
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[
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[
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"85",
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"94"
]
] | 94.262968 | all-MiniLM-L6-v2 | 0.964 | -0.23199 | 0.128409 | 0.928781 |
BBC News | BERTopic | 43 | 10 | [
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] | 878.928533 | all-MiniLM-L6-v2 | 0.5 | -0.010048 | 0.256275 | 0.603049 |
BBC News | BERTopic | 44 | 10 | [
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] | 902.157574 | all-MiniLM-L6-v2 | 0.5 | -0.010048 | 0.256275 | 0.610833 |
BBC News | BERTopic | 45 | 10 | [
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] | 881.797756 | all-MiniLM-L6-v2 | 0.5 | -0.010048 | 0.256275 | 0.601131 |
BBC News | BERTopic | 46 | 10 | [
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] | 836.7507 | all-MiniLM-L6-v2 | 0.5 | -0.010048 | 0.256275 | 0.607718 |
BBC News | BERTopic | 43 | 20 | [
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] | 885.883228 | all-MiniLM-L6-v2 | 0.5 | -0.010048 | 0.256275 | 0.612158 |
BBC News | BERTopic | 44 | 20 | [
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] | 856.648137 | all-MiniLM-L6-v2 | 0.5 | -0.010048 | 0.256275 | 0.60994 |
BBC News | BERTopic | 45 | 20 | [
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] | 891.810742 | all-MiniLM-L6-v2 | 0.5 | -0.010048 | 0.256275 | 0.604801 |
BBC News | BERTopic | 46 | 20 | [
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] | 870.464814 | all-MiniLM-L6-v2 | 0.5 | -0.010048 | 0.256275 | 0.607233 |
BBC News | BERTopic | 43 | 30 | [
[
"in",
"and",
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"to",
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],
[
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[
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[
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]
] | 837.507406 | all-MiniLM-L6-v2 | 0.5 | -0.010048 | 0.256275 | 0.610622 |
BBC News | BERTopic | 44 | 30 | [
[
"the",
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[
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[
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[
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[
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"and",
"race",
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]
] | 629.620124 | all-MiniLM-L6-v2 | 0.5 | -0.010048 | 0.256275 | 0.611601 |
BBC News | BERTopic | 45 | 30 | [
[
"the",
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"and",
"that",
"in",
"to",
"it",
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],
[
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[
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[
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[
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"and",
"race",
"world"
]
] | 783.705856 | all-MiniLM-L6-v2 | 0.5 | -0.010048 | 0.256275 | 0.599727 |
BBC News | BERTopic | 46 | 30 | [
[
"the",
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"and",
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"to",
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],
[
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[
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[
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[
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"she",
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"and",
"race",
"world"
]
] | 743.55428 | all-MiniLM-L6-v2 | 0.5 | -0.010048 | 0.256275 | 0.605389 |
BBC News | BERTopic | 43 | 40 | [
[
"in",
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"the",
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"to",
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[
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[
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[
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"and",
"race",
"world"
]
] | 826.509843 | all-MiniLM-L6-v2 | 0.5 | -0.010048 | 0.256275 | 0.609603 |
BBC News | BERTopic | 44 | 40 | [
[
"the",
"of",
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"and",
"that",
"in",
"to",
"it",
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],
[
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[
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[
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[
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"and",
"race",
"world"
]
] | 769.670217 | all-MiniLM-L6-v2 | 0.5 | -0.010048 | 0.256275 | 0.605696 |
BBC News | BERTopic | 45 | 40 | [
[
"the",
"of",
"for",
"is",
"and",
"that",
"in",
"to",
"it",
"said"
],
[
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[
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[
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[
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"and",
"race",
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]
] | 816.676008 | all-MiniLM-L6-v2 | 0.5 | -0.010048 | 0.256275 | 0.603034 |
BBC News | BERTopic | 46 | 40 | [
[
"the",
"of",
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"is",
"and",
"that",
"in",
"to",
"it",
"said"
],
[
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[
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[
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[
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"and",
"race",
"world"
]
] | 793.18764 | all-MiniLM-L6-v2 | 0.5 | -0.010048 | 0.256275 | 0.612705 |
BBC News | BERTopic | 43 | 50 | [
[
"in",
"and",
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"that",
"the",
"is",
"to",
"for",
"said",
"it"
],
[
"the",
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[
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[
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[
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"and",
"race",
"world"
]
] | 887.912302 | all-MiniLM-L6-v2 | 0.5 | -0.010048 | 0.256275 | 0.607666 |
BBC News | BERTopic | 44 | 50 | [
[
"the",
"of",
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"and",
"that",
"in",
"to",
"it",
"said"
],
[
"to",
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[
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[
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[
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"and",
"race",
"world"
]
] | 953.922494 | all-MiniLM-L6-v2 | 0.5 | -0.010048 | 0.256275 | 0.602802 |
BBC News | BERTopic | 45 | 50 | [
[
"the",
"of",
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"and",
"that",
"in",
"to",
"it",
"said"
],
[
"to",
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],
[
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[
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],
[
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"she",
"the",
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"and",
"race",
"world"
]
] | 795.812462 | all-MiniLM-L6-v2 | 0.5 | -0.010048 | 0.256275 | 0.607512 |
BBC News | BERTopic | 46 | 50 | [
[
"the",
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"and",
"that",
"in",
"to",
"it",
"said"
],
[
"to",
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],
[
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[
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],
[
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"she",
"the",
"her",
"and",
"race",
"world"
]
] | 831.127964 | all-MiniLM-L6-v2 | 0.5 | -0.010048 | 0.256275 | 0.611343 |
BBC News | NMF | 43 | 10 | [
[
"at",
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"first",
"which",
"it",
"on",
"to",
"time",
"from"
],
[
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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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"it",
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]
] | 1.788574 | all-MiniLM-L6-v2 | 0.49 | -0.001058 | 0.269654 | 0.570532 |
BBC News | NMF | 44 | 10 | [
[
"at",
"by",
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"first",
"which",
"it",
"on",
"to",
"time",
"from"
],
[
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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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],
[
"for",
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"robbie",
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"best",
"it",
"and"
]
] | 2.07404 | all-MiniLM-L6-v2 | 0.49 | -0.001058 | 0.269654 | 0.565647 |
BBC News | NMF | 45 | 10 | [
[
"at",
"by",
"the",
"first",
"which",
"it",
"on",
"to",
"time",
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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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"best",
"it",
"and"
]
] | 1.986854 | all-MiniLM-L6-v2 | 0.49 | -0.001058 | 0.269654 | 0.56901 |
BBC News | NMF | 46 | 10 | [
[
"at",
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"first",
"which",
"it",
"on",
"to",
"time",
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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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"it",
"and"
]
] | 2.463064 | all-MiniLM-L6-v2 | 0.49 | -0.001058 | 0.269654 | 0.567671 |
BBC News | NMF | 43 | 20 | [
[
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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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"and",
"by",
"with"
]
] | 2.887159 | all-MiniLM-L6-v2 | 0.445 | 0.000025 | 0.2376 | 0.583025 |
BBC News | NMF | 44 | 20 | [
[
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"first",
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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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BBC News | NMF | 45 | 20 | [
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BBC News | NMF | 46 | 30 | [
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BBC News | NMF | 43 | 40 | [
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BBC News | NMF | 44 | 40 | [
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BBC News | NMF | 45 | 40 | [
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] | 5.805614 | all-MiniLM-L6-v2 | 0.4625 | 0.023241 | 0.226866 | 0.642111 |
BBC News | NMF | 46 | 40 | [
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BBC News | NMF | 43 | 50 | [
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] | 7.022824 | all-MiniLM-L6-v2 | 0.48 | 0.022558 | 0.210626 | 0.663033 |
BBC News | NMF | 44 | 50 | [
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BBC News | NMF | 46 | 50 | [
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BBC News | LDA | 43 | 10 | [
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