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ArXiv ML Papers | BERTopic | 43 | 10 | [
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ArXiv ML Papers | BERTopic | 44 | 10 | [
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ArXiv ML Papers | BERTopic | 45 | 10 | [
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ArXiv ML Papers | BERTopic | 46 | 10 | [
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ArXiv ML Papers | BERTopic | 44 | 20 | [
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ArXiv ML Papers | BERTopic | 45 | 20 | [
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ArXiv ML Papers | BERTopic | 46 | 20 | [
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ArXiv ML Papers | BERTopic | 44 | 30 | [
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ArXiv ML Papers | BERTopic | 45 | 30 | [
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ArXiv ML Papers | BERTopic | 46 | 30 | [
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ArXiv ML Papers | BERTopic | 43 | 40 | [
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ArXiv ML Papers | BERTopic | 44 | 40 | [
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ArXiv ML Papers | BERTopic | 45 | 40 | [
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ArXiv ML Papers | BERTopic | 46 | 40 | [
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ArXiv ML Papers | NMF | 46 | 30 | [
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ArXiv ML Papers | NMF | 44 | 40 | [
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ArXiv ML Papers | LDA | 46 | 20 | [
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ArXiv ML Papers | LDA | 44 | 40 | [
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ArXiv ML Papers | LDA | 45 | 40 | [
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ArXiv ML Papers | LDA | 46 | 40 | [
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] | 23.874444 | all-MiniLM-L6-v2 | 0.364 | -0.051004 | 0.189744 | 0.61902 |
ArXiv ML Papers | LDA | 44 | 50 | [
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] | 14.476796 | all-MiniLM-L6-v2 | 0.356 | -0.035463 | 0.186418 | 0.596312 |
ArXiv ML Papers | LDA | 45 | 50 | [
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] | 14.255966 | all-MiniLM-L6-v2 | 0.39 | -0.061498 | 0.18372 | 0.637337 |
ArXiv ML Papers | LDA | 46 | 50 | [
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]
] | 14.030783 | all-MiniLM-L6-v2 | 0.33 | -0.072721 | 0.191163 | 0.638094 |
ArXiv ML Papers | Top2Vec | 43 | 10 | [
[
"cnns",
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"adversarial",
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]
] | 656.29102 | all-MiniLM-L6-v2 | 0.54 | -0.278403 | 0.14962 | 0.846283 |
ArXiv ML Papers | Top2Vec | 44 | 10 | [
[
"supervised",
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]
] | 673.252285 | all-MiniLM-L6-v2 | 0.56 | -0.26459 | 0.160135 | 0.845661 |
ArXiv ML Papers | Top2Vec | 45 | 10 | [
[
"learning",
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] | 584.320705 | all-MiniLM-L6-v2 | 0.56 | -0.273268 | 0.174848 | 0.846271 |
ArXiv ML Papers | Top2Vec | 46 | 10 | [
[
"learning",
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]
] | 634.524863 | all-MiniLM-L6-v2 | 0.53 | -0.269991 | 0.153078 | 0.853081 |
ArXiv ML Papers | Top2Vec | 43 | 20 | [
[
"adversarially",
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] | 608.984458 | all-MiniLM-L6-v2 | 0.45 | -0.266672 | 0.173274 | 0.85077 |
ArXiv ML Papers | Top2Vec | 44 | 20 | [
[
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],
[
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],
[
"autoencoders",
"imagenet",
"benchmarks",
"bottleneck",
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"cnns",
"networks",
"neural",
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],
[
"imagenet",
"generative",
"cnn",
"autoencoder",
"autoencoders",
"gans",
"adversarially",
"adversarial",
"gan",
"cnns"
],
[
"lasso",
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"optimize",
"optimization",
"minimization",
"minimize",
"regularization",
"optimizing",
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],
[
"models",
"supervised",
"autoencoders",
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"priors",
"learning",
"rnns",
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"learns"
],
[
"imagenet",
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"recognition",
"softmax",
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"neural",
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"recognizing"
],
[
"recognizing",
"supervised",
"convolutions",
"imagenet",
"cnns",
"recognition",
"cnn",
"autoencoders",
"convolutional",
"classifiers"
]
] | 535.264348 | all-MiniLM-L6-v2 | 0.47 | -0.253657 | 0.174505 | 0.844253 |
ArXiv ML Papers | Top2Vec | 45 | 20 | [
[
"learning",
"learns",
"learnt",
"reinforcement",
"bandit",
"bandits",
"ai",
"planning",
"learnable",
"softmax"
],
[
"softmax",
"distributed",
"learns",
"federated",
"adversarially",
"learnt",
"learning",
"supervised",
"rnns",
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],
[
"adversary",
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"adversarially",
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"softmax",
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],
[
"biases",
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],
[
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"supervised",
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[
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],
[
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"adversarial"
],
[
"classifier",
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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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"neural",
"optimized"
],
[
"cnn",
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"neural",
"supervised",
"softmax",
"cnns",
"recognition",
"segmentation",
"recognizing"
],
[
"cnn",
"imagenet",
"cnns",
"recognition",
"convolutions",
"autoencoders",
"recognizing",
"supervised",
"softmax",
"convolutional"
],
[
"supervised",
"metrics",
"classifiers",
"classifying",
"softmax",
"classification",
"classifier",
"regularization",
"metric",
"similarity"
],
[
"regularized",
"lasso",
"sparse",
"regularization",
"minimization",
"softmax",
"tensors",
"optimizer",
"clustering",
"tensor"
]
] | 588.672674 | all-MiniLM-L6-v2 | 0.455 | -0.250703 | 0.178586 | 0.840558 |
ArXiv ML Papers | Top2Vec | 46 | 20 | [
[
"learning",
"ai",
"planning",
"reinforcement",
"bandit",
"learnt",
"learns",
"bandits",
"learnable",
"softmax"
],
[
"adversarially",
"distributed",
"softmax",
"federated",
"learning",
"learns",
"learnt",
"supervised",
"rnns",
"rnn"
],
[
"adversarial",
"adversarially",
"cnns",
"adversary",
"cnn",
"adversaries",
"attacks",
"softmax",
"classifiers",
"autoencoders"
],
[
"fairness",
"adversarially",
"bias",
"biases",
"classifiers",
"discrimination",
"adversarial",
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"classifier"
],
[
"softmax",
"autoencoder",
"lstm",
"supervised",
"rnns",
"embeddings",
"learns",
"corpus",
"autoencoders",
"cnn"
],
[
"learns",
"rnns",
"neural",
"backpropagation",
"learning",
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"rnn",
"modeling"
],
[
"forecasts",
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"prediction",
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"predicting",
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"classifiers",
"rnns"
],
[
"softmax",
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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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"regularized",
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],
[
"gan",
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"adversarially",
"adversarial",
"autoencoder",
"generative",
"imagenet",
"gans",
"cnns",
"cnn"
],
[
"cnns",
"rnns",
"cnn",
"imagenet",
"autoencoders",
"bottleneck",
"benchmarks",
"networks",
"softmax",
"neural"
],
[
"imagenet",
"recognizing",
"recognition",
"convolutions",
"cnns",
"cnn",
"supervised",
"autoencoders",
"convolutional",
"softmax"
],
[
"optimization",
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"optimizer",
"optimizing",
"optimize",
"minimize",
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"lasso",
"optimized",
"optimizes"
],
[
"autoencoders",
"learns",
"rnns",
"generative",
"softmax",
"models",
"supervised",
"backpropagation",
"priors",
"autoencoder"
]
] | 631.574545 | all-MiniLM-L6-v2 | 0.45 | -0.259184 | 0.173666 | 0.84807 |
ArXiv ML Papers | Top2Vec | 43 | 30 | [
[
"adversarially",
"classifiers",
"discrimination",
"bias",
"biases",
"fairness",
"supervised",
"adversarial",
"discriminate",
"classifier"
],
[
"softmax",
"distributed",
"learns",
"federated",
"adversarially",
"learnt",
"learning",
"supervised",
"rnns",
"rnn"
],
[
"adversary",
"security",
"private",
"adversarial",
"softmax",
"regularization",
"classifiers",
"randomized",
"adversarially",
"privacy"
],
[
"adversary",
"cnns",
"attacks",
"adversarial",
"adversarially",
"cnn",
"classifiers",
"adversaries",
"softmax",
"attacker"
],
[
"learnt",
"learns",
"ai",
"bandit",
"bandits",
"learnable",
"reinforcement",
"learning",
"planning",
"softmax"
],
[
"backpropagation",
"rnns",
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"neural",
"modeling",
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"learning"
],
[
"interpretability",
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"adversarially",
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],
[
"personalized",
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],
[
"lstm",
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"neural",
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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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],
[
"gan",
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"cnn",
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"autoencoder",
"cnns"
],
[
"imagenet",
"cnn",
"cnns",
"supervised",
"softmax",
"recognition",
"autoencoders",
"neural",
"segmentation",
"recognizing"
],
[
"bottleneck",
"benchmarks",
"autoencoder",
"networks",
"rnns",
"imagenet",
"autoencoders",
"cnns",
"cnn",
"optimized"
],
[
"convolutions",
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"recognizing",
"supervised",
"imagenet",
"cnns",
"recognition",
"cnn",
"convolutional",
"softmax"
],
[
"supervised",
"softmax",
"generative",
"autoencoders",
"rnns",
"probabilistic",
"learns",
"backpropagation",
"models",
"priors"
],
[
"optimization",
"optimizer",
"regularization",
"lasso",
"minimize",
"minimization",
"optimizing",
"optimize",
"optimized",
"optimal"
]
] | 577.164902 | all-MiniLM-L6-v2 | 0.452381 | -0.264192 | 0.17378 | 0.84458 |
ArXiv ML Papers | Top2Vec | 44 | 30 | [
[
"distributed",
"softmax",
"federated",
"learnt",
"learns",
"learning",
"supervised",
"adversarially",
"rnns",
"rnn"
],
[
"classifiers",
"adversary",
"cnns",
"cnn",
"attacks",
"adversarial",
"adversarially",
"softmax",
"exploiting",
"adversaries"
],
[
"fairness",
"biases",
"classifiers",
"classifier",
"adversarial",
"discrimination",
"discriminate",
"adversarially",
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"bias"
],
[
"supervised",
"leveraging",
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"interventions",
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],
[
"ai",
"reinforcement",
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"planning",
"learning",
"learns",
"learnt",
"bandits",
"learnable",
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],
[
"backpropagation",
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"learns",
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],
[
"softmax",
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"classifying",
"classify",
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"classification",
"supervised",
"lstm"
],
[
"graphs",
"softmax",
"supervised",
"networks",
"cnn",
"nodes",
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"cnns",
"embeddings",
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],
[
"cnns",
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"imagenet",
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],
[
"predicting",
"forecasts",
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"forecast",
"prediction",
"softmax",
"rnns",
"classifiers",
"predicts",
"predict"
],
[
"embeddings",
"recommender",
"recommendations",
"recommendation",
"supervised",
"softmax",
"factorization",
"personalized",
"ranking",
"retrieval"
],
[
"autoencoder",
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"learns",
"rnns",
"cnn",
"neural",
"autoencoders",
"lstm",
"cnns"
],
[
"classifier",
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"boosting",
"supervised",
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],
[
"classification",
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],
[
"optimizer",
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"lasso",
"regularized",
"clustering",
"tensors",
"softmax",
"minimization",
"algorithms"
],
[
"autoencoders",
"imagenet",
"benchmarks",
"bottleneck",
"cnn",
"rnns",
"cnns",
"networks",
"neural",
"optimized"
],
[
"imagenet",
"generative",
"cnn",
"autoencoder",
"autoencoders",
"gans",
"adversarially",
"adversarial",
"gan",
"cnns"
],
[
"lasso",
"optimizer",
"optimize",
"optimization",
"minimization",
"minimize",
"regularization",
"optimizing",
"optimized",
"optimizes"
],
[
"supervised",
"learns",
"priors",
"autoencoders",
"backpropagation",
"probabilistic",
"rnns",
"softmax",
"generative",
"models"
],
[
"imagenet",
"cnn",
"cnns",
"recognition",
"softmax",
"supervised",
"autoencoders",
"neural",
"segmentation",
"recognizing"
],
[
"recognizing",
"supervised",
"convolutions",
"imagenet",
"cnns",
"recognition",
"cnn",
"autoencoders",
"convolutional",
"classifiers"
]
] | 601.105897 | all-MiniLM-L6-v2 | 0.480952 | -0.2564 | 0.172465 | 0.844825 |
ArXiv ML Papers | Top2Vec | 45 | 30 | [
[
"learning",
"learns",
"learnt",
"reinforcement",
"bandit",
"bandits",
"ai",
"planning",
"learnable",
"softmax"
],
[
"softmax",
"distributed",
"learns",
"federated",
"adversarially",
"learnt",
"learning",
"supervised",
"rnns",
"rnn"
],
[
"adversary",
"adversarial",
"adversarially",
"cnns",
"classifiers",
"softmax",
"cnn",
"exploiting",
"attacks",
"adversaries"
],
[
"biases",
"bias",
"fairness",
"adversarially",
"classifiers",
"adversarial",
"supervised",
"discrimination",
"discriminate",
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],
[
"rnns",
"lstm",
"learns",
"autoencoders",
"softmax",
"corpus",
"autoencoder",
"supervised",
"classifiers",
"cnn"
],
[
"softmax",
"learning",
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"autoencoders",
"modeling",
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"neural",
"models",
"rnn"
],
[
"imagenet",
"networks",
"regularization",
"neural",
"neuron",
"backpropagation",
"cnns",
"cnn",
"softmax",
"adversarial"
],
[
"classifier",
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"classifying",
"classification",
"classify",
"supervised",
"softmax",
"ensemble",
"boosting",
"ensembles"
],
[
"predict",
"forecasts",
"forecasting",
"forecast",
"softmax",
"predicts",
"rnns",
"prediction",
"predicting",
"lstm"
],
[
"classifier",
"classification",
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"classifiers",
"classifying",
"softmax",
"classify",
"supervised",
"recognition",
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],
[
"classifiers",
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],
[
"networks",
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"graph",
"cnn",
"cnns",
"embeddings",
"softmax",
"vertex",
"supervised"
],
[
"gan",
"gans",
"generative",
"adversarial",
"autoencoders",
"adversarially",
"cnn",
"imagenet",
"autoencoder",
"cnns"
],
[
"optimization",
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"optimizer",
"optimizing",
"lasso",
"minimize",
"regularization",
"optimal",
"softmax",
"optimize"
],
[
"softmax",
"autoencoders",
"supervised",
"models",
"rnns",
"generative",
"backpropagation",
"probabilistic",
"priors",
"learns"
],
[
"autoencoders",
"imagenet",
"benchmarks",
"bottleneck",
"cnn",
"rnns",
"cnns",
"networks",
"neural",
"optimized"
],
[
"cnn",
"imagenet",
"autoencoders",
"neural",
"supervised",
"softmax",
"cnns",
"recognition",
"segmentation",
"recognizing"
],
[
"cnns",
"recognition",
"imagenet",
"cnn",
"convolutions",
"recognizing",
"supervised",
"autoencoders",
"softmax",
"classifiers"
],
[
"maps",
"cnns",
"cnn",
"imagenet",
"3d",
"recognition",
"recognizing",
"convolutions",
"supervised",
"vision"
],
[
"supervised",
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"classifiers",
"classifying",
"softmax",
"classification",
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"regularization",
"metric",
"similarity"
],
[
"regularized",
"lasso",
"sparse",
"regularization",
"minimization",
"softmax",
"tensors",
"optimizer",
"clustering",
"tensor"
]
] | 597.81598 | all-MiniLM-L6-v2 | 0.442857 | -0.252986 | 0.175005 | 0.843511 |
ArXiv ML Papers | Top2Vec | 46 | 30 | [
[
"learning",
"ai",
"planning",
"reinforcement",
"bandit",
"learnt",
"learns",
"bandits",
"learnable",
"softmax"
],
[
"adversarially",
"distributed",
"softmax",
"federated",
"learning",
"learns",
"learnt",
"supervised",
"rnns",
"rnn"
],
[
"adversary",
"adversarial",
"adversarially",
"cnns",
"adversaries",
"cnn",
"classifiers",
"attacks",
"softmax",
"attacker"
],
[
"regularization",
"adversarially",
"adversarial",
"privacy",
"private",
"randomized",
"adversary",
"normalization",
"regularized",
"softmax"
],
[
"fairness",
"adversarially",
"bias",
"biases",
"classifiers",
"discrimination",
"adversarial",
"discriminate",
"supervised",
"classifier"
],
[
"softmax",
"autoencoder",
"lstm",
"supervised",
"rnns",
"embeddings",
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"corpus",
"autoencoders",
"cnn"
],
[
"learns",
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"neural",
"backpropagation",
"learning",
"softmax",
"autoencoders",
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],
[
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],
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],
[
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"adversarially",
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"generative",
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],
[
"cnns",
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],
[
"imagenet",
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],
[
"optimization",
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"optimize",
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"optimized",
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],
[
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"learns",
"rnns",
"generative",
"softmax",
"models",
"supervised",
"backpropagation",
"priors",
"autoencoder"
]
] | 498.065006 | all-MiniLM-L6-v2 | 0.452381 | -0.262968 | 0.171184 | 0.850088 |
ArXiv ML Papers | Top2Vec | 43 | 40 | [
[
"adversarially",
"classifiers",
"discrimination",
"bias",
"biases",
"fairness",
"supervised",
"adversarial",
"discriminate",
"classifier"
],
[
"softmax",
"distributed",
"learns",
"federated",
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],
[
"adversary",
"security",
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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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"convolutional",
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],
[
"supervised",
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"generative",
"autoencoders",
"rnns",
"probabilistic",
"learns",
"backpropagation",
"models",
"priors"
],
[
"optimization",
"optimizer",
"regularization",
"lasso",
"minimize",
"minimization",
"optimizing",
"optimize",
"optimized",
"optimal"
]
] | 640.464483 | all-MiniLM-L6-v2 | 0.452381 | -0.264192 | 0.17378 | 0.845774 |
ArXiv ML Papers | Top2Vec | 44 | 40 | [
[
"distributed",
"softmax",
"federated",
"learnt",
"learns",
"learning",
"supervised",
"adversarially",
"rnns",
"rnn"
],
[
"classifiers",
"adversary",
"cnns",
"cnn",
"attacks",
"adversarial",
"adversarially",
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],
[
"fairness",
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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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],
[
"imagenet",
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],
[
"lasso",
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],
[
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],
[
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],
[
"recognizing",
"supervised",
"convolutions",
"imagenet",
"cnns",
"recognition",
"cnn",
"autoencoders",
"convolutional",
"classifiers"
]
] | 569.045129 | all-MiniLM-L6-v2 | 0.480952 | -0.2564 | 0.172465 | 0.846093 |
ArXiv ML Papers | Top2Vec | 45 | 40 | [
[
"learning",
"learns",
"learnt",
"reinforcement",
"bandit",
"bandits",
"ai",
"planning",
"learnable",
"softmax"
],
[
"softmax",
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"adversarially",
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],
[
"adversary",
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],
[
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[
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[
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],
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[
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],
[
"cnn",
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],
[
"cnns",
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],
[
"maps",
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],
[
"supervised",
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],
[
"regularized",
"lasso",
"sparse",
"regularization",
"minimization",
"softmax",
"tensors",
"optimizer",
"clustering",
"tensor"
]
] | 569.015667 | all-MiniLM-L6-v2 | 0.442857 | -0.252986 | 0.175005 | 0.842255 |
ArXiv ML Papers | Top2Vec | 46 | 40 | [
[
"learning",
"ai",
"planning",
"reinforcement",
"bandit",
"learnt",
"learns",
"bandits",
"learnable",
"softmax"
],
[
"adversarially",
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],
[
"adversary",
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],
[
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],
[
"fairness",
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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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"regularized",
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],
[
"gan",
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"adversarially",
"adversarial",
"autoencoder",
"generative",
"imagenet",
"gans",
"cnns",
"cnn"
],
[
"cnns",
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"networks",
"softmax",
"neural"
],
[
"imagenet",
"recognizing",
"recognition",
"convolutions",
"cnns",
"cnn",
"supervised",
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"convolutional",
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],
[
"optimization",
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[
"autoencoders",
"learns",
"rnns",
"generative",
"softmax",
"models",
"supervised",
"backpropagation",
"priors",
"autoencoder"
]
] | 584.353214 | all-MiniLM-L6-v2 | 0.452381 | -0.262968 | 0.171184 | 0.844559 |
ArXiv ML Papers | Top2Vec | 43 | 50 | [
[
"adversarially",
"classifiers",
"discrimination",
"bias",
"biases",
"fairness",
"supervised",
"adversarial",
"discriminate",
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[
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[
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[
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[
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],
[
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],
[
"optimization",
"optimizer",
"regularization",
"lasso",
"minimize",
"minimization",
"optimizing",
"optimize",
"optimized",
"optimal"
]
] | 498.800022 | all-MiniLM-L6-v2 | 0.452381 | -0.264192 | 0.17378 | 0.850448 |
ArXiv ML Papers | Top2Vec | 44 | 50 | [
[
"distributed",
"softmax",
"federated",
"learnt",
"learns",
"learning",
"supervised",
"adversarially",
"rnns",
"rnn"
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[
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],
[
"fairness",
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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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],
[
"embeddings",
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],
[
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],
[
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[
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[
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],
[
"imagenet",
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"adversarially",
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],
[
"lasso",
"optimizer",
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],
[
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"rnns",
"softmax",
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],
[
"imagenet",
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"recognition",
"softmax",
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"neural",
"segmentation",
"recognizing"
],
[
"recognizing",
"supervised",
"convolutions",
"imagenet",
"cnns",
"recognition",
"cnn",
"autoencoders",
"convolutional",
"classifiers"
]
] | 530.921954 | all-MiniLM-L6-v2 | 0.480952 | -0.2564 | 0.172465 | 0.845788 |
ArXiv ML Papers | Top2Vec | 45 | 50 | [
[
"learning",
"learns",
"learnt",
"reinforcement",
"bandit",
"bandits",
"ai",
"planning",
"learnable",
"softmax"
],
[
"softmax",
"distributed",
"learns",
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"adversarially",
"learnt",
"learning",
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],
[
"adversary",
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],
[
"biases",
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[
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[
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],
[
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],
[
"classifier",
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],
[
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],
[
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],
[
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],
[
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],
[
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],
[
"optimization",
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],
[
"softmax",
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"generative",
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"priors",
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],
[
"autoencoders",
"imagenet",
"benchmarks",
"bottleneck",
"cnn",
"rnns",
"cnns",
"networks",
"neural",
"optimized"
],
[
"cnn",
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"autoencoders",
"neural",
"supervised",
"softmax",
"cnns",
"recognition",
"segmentation",
"recognizing"
],
[
"cnns",
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"recognizing",
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"softmax",
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],
[
"maps",
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"3d",
"recognition",
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"vision"
],
[
"supervised",
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"classifying",
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"similarity"
],
[
"regularized",
"lasso",
"sparse",
"regularization",
"minimization",
"softmax",
"tensors",
"optimizer",
"clustering",
"tensor"
]
] | 651.461712 | all-MiniLM-L6-v2 | 0.442857 | -0.252986 | 0.175005 | 0.843242 |
ArXiv ML Papers | Top2Vec | 46 | 50 | [
[
"learning",
"ai",
"planning",
"reinforcement",
"bandit",
"learnt",
"learns",
"bandits",
"learnable",
"softmax"
],
[
"adversarially",
"distributed",
"softmax",
"federated",
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"rnns",
"rnn"
],
[
"adversary",
"adversarial",
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"attacks",
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"attacker"
],
[
"regularization",
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[
"fairness",
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"discriminate",
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],
[
"softmax",
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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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],
[
"imagenet",
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"recognizing"
],
[
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"similarity",
"metric"
],
[
"sparse",
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"regularized",
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],
[
"gan",
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"adversarially",
"adversarial",
"autoencoder",
"generative",
"imagenet",
"gans",
"cnns",
"cnn"
],
[
"cnns",
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"cnn",
"imagenet",
"autoencoders",
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"benchmarks",
"networks",
"softmax",
"neural"
],
[
"imagenet",
"recognizing",
"recognition",
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"cnns",
"cnn",
"supervised",
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"convolutional",
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],
[
"optimization",
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"optimizing",
"optimize",
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"regularization",
"lasso",
"optimized",
"optimizes"
],
[
"autoencoders",
"learns",
"rnns",
"generative",
"softmax",
"models",
"supervised",
"backpropagation",
"priors",
"autoencoder"
]
] | 640.067949 | all-MiniLM-L6-v2 | 0.452381 | -0.262968 | 0.171184 | 0.850129 |
ArXiv ML Papers | GMM | 43 | 10 | [
[
"kernel",
"clustering",
"matrix",
"rank",
"series",
"time",
"regression",
"proposed",
"tensor",
"algorithm"
],
[
"gnns",
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],
[
"reward",
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],
[
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],
[
"images",
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"image",
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],
[
"convergence",
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[
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],
[
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],
[
"segmentation",
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"image",
"deep",
"detection",
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],
[
"perturbations",
"against",
"robustness",
"robust",
"attack",
"attacks",
"defense",
"adversarial",
"privacy",
"examples"
]
] | 2.576472 | all-MiniLM-L6-v2 | 0.94 | 0.053246 | 0.1459 | 0.813589 |
ArXiv ML Papers | GMM | 44 | 10 | [
[
"speech",
"recognition",
"speaker",
"acoustic",
"asr",
"audio",
"music",
"signal",
"end",
"quality"
],
[
"posterior",
"variational",
"inference",
"estimation",
"bayesian",
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"gaussian",
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],
[
"classifier",
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"label",
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"accuracy",
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"ml",
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],
[
"reinforcement",
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"rl",
"action",
"control",
"policy",
"games",
"policies"
],
[
"attack",
"defense",
"perturbations",
"privacy",
"attacks",
"adversarial",
"robustness",
"against",
"robust",
"examples"
],
[
"graphs",
"node",
"graph",
"nodes",
"embedding",
"link",
"structure",
"gnns",
"embeddings",
"representation"
],
[
"algorithm",
"convex",
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"regret",
"bound",
"stochastic",
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"algorithms",
"optimization",
"gradient"
],
[
"memory",
"layer",
"network",
"networks",
"neural",
"accuracy",
"architectures",
"deep",
"architecture",
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],
[
"image",
"segmentation",
"object",
"visual",
"images",
"gan",
"convolutional",
"dataset",
"deep",
"detection"
],
[
"sequence",
"sentences",
"word",
"natural",
"language",
"text",
"task",
"words",
"attention",
"semantic"
]
] | 2.836519 | all-MiniLM-L6-v2 | 0.98 | 0.06128 | 0.149774 | 0.825734 |
ArXiv ML Papers | GMM | 45 | 10 | [
[
"detection",
"segmentation",
"visual",
"gan",
"image",
"images",
"object",
"convolutional",
"supervised",
"medical"
],
[
"examples",
"against",
"adversarial",
"defense",
"perturbations",
"attack",
"attacks",
"privacy",
"robust",
"robustness"
],
[
"agent",
"reinforcement",
"reward",
"rl",
"policy",
"agents",
"regret",
"action",
"policies",
"games"
],
[
"text",
"speech",
"attention",
"audio",
"language",
"languages",
"speaker",
"word",
"translation",
"recurrent"
],
[
"quantum",
"posterior",
"parameters",
"variational",
"bayesian",
"physics",
"uncertainty",
"dynamics",
"inference",
"equations"
],
[
"series",
"classifier",
"prediction",
"classification",
"machine",
"time",
"accuracy",
"selection",
"label",
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],
[
"convergence",
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"optimization",
"rank",
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],
[
"neural",
"layer",
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"deep",
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],
[
"user",
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"language",
"recommendation",
"item",
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"label",
"recommender",
"topic",
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],
[
"nodes",
"graph",
"graphs",
"node",
"link",
"gnns",
"embedding",
"structure",
"embeddings",
"representation"
]
] | 2.766196 | all-MiniLM-L6-v2 | 0.97 | 0.024243 | 0.165378 | 0.838997 |
ArXiv ML Papers | GMM | 46 | 10 | [
[
"convex",
"matrix",
"algorithm",
"convergence",
"algorithms",
"linear",
"kernel",
"problems",
"rank",
"optimization"
],
[
"nodes",
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"gnns",
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],
[
"regret",
"reinforcement",
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"agents",
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"policy",
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],
[
"variational",
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],
[
"language",
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],
[
"object",
"images",
"segmentation",
"image",
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"detection",
"gan",
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],
[
"attack",
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"adversarial",
"privacy",
"against",
"perturbations",
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"examples",
"robust"
],
[
"audio",
"speaker",
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"acoustic",
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"asr",
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],
[
"series",
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"classification",
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"prediction",
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"fairness",
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"regression"
],
[
"neural",
"memory",
"network",
"hardware",
"architectures",
"accuracy",
"networks",
"deep",
"layer",
"energy"
]
] | 2.882266 | all-MiniLM-L6-v2 | 0.98 | 0.054135 | 0.155939 | 0.826401 |
ArXiv ML Papers | GMM | 43 | 20 | [
[
"algorithm",
"rank",
"tensor",
"matrix",
"low",
"kernel",
"clustering",
"sparse",
"linear",
"subspace"
],
[
"graph",
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[
"bound",
"online",
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"sqrt",
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[
"user",
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],
[
"image",
"generator",
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[
"convex",
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"optimization",
"convergence",
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[
"communication",
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"iot",
"distributed",
"traffic",
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"wireless",
"federated",
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],
[
"equations",
"physics",
"quantum",
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[
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"covid",
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],
[
"fair",
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[
"series",
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"day",
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"study",
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[
"images",
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"scene",
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],
[
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],
[
"sequence",
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[
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[
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"acoustic",
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[
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"bayesian",
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"gaussian",
"estimation",
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"probabilistic"
],
[
"pruning",
"hardware",
"architectures",
"networks",
"nas",
"accuracy",
"neural",
"memory",
"architecture",
"layers"
],
[
"robustness",
"attacks",
"adversarial",
"attack",
"perturbations",
"against",
"examples",
"defense",
"robust",
"attacker"
],
[
"environment",
"reward",
"control",
"agent",
"agents",
"action",
"policy",
"rl",
"reinforcement",
"policies"
]
] | 4.033339 | all-MiniLM-L6-v2 | 0.9 | 0.016575 | 0.153815 | 0.857305 |
ArXiv ML Papers | GMM | 44 | 20 | [
[
"subject",
"eeg",
"brain",
"signals",
"was",
"wearable",
"devices",
"accuracy",
"healthy",
"using"
],
[
"bayesian",
"variational",
"posterior",
"series",
"inference",
"probabilistic",
"causal",
"time",
"processes",
"treatment"
],
[
"federated",
"ml",
"fairness",
"machine",
"fair",
"classifier",
"label",
"classification",
"labels",
"prediction"
],
[
"environment",
"actions",
"rl",
"reward",
"robot",
"policy",
"agent",
"reinforcement",
"agents",
"environments"
],
[
"attack",
"attacks",
"defense",
"privacy",
"robustness",
"adversarial",
"perturbations",
"against",
"robust",
"examples"
],
[
"graph",
"node",
"graphs",
"nodes",
"embedding",
"structure",
"representation",
"link",
"embeddings",
"gnns"
],
[
"bandits",
"bandit",
"bound",
"armed",
"sqrt",
"reward",
"online",
"regret",
"arm",
"algorithm"
],
[
"architectures",
"pruning",
"hardware",
"architecture",
"memory",
"nas",
"quantization",
"layer",
"networks",
"accuracy"
],
[
"generative",
"gans",
"image",
"gan",
"images",
"latent",
"generator",
"generation",
"variational",
"vae"
],
[
"visual",
"human",
"attention",
"domain",
"reasoning",
"representations",
"object",
"concepts",
"pre",
"target"
],
[
"object",
"images",
"cnn",
"segmentation",
"3d",
"image",
"convolutional",
"detection",
"deep",
"medical"
],
[
"stochastic",
"descent",
"sgd",
"convergence",
"gradient",
"communication",
"parallel",
"convex",
"strongly",
"proximal"
],
[
"sample",
"generalization",
"bounds",
"distribution",
"class",
"error",
"metric",
"kernel",
"bound",
"distance"
],
[
"audio",
"speaker",
"speech",
"acoustic",
"recognition",
"music",
"asr",
"quality",
"end",
"signal"
],
[
"problems",
"gradient",
"optimization",
"stochastic",
"convex",
"functions",
"order",
"epsilon",
"descent",
"exchange"
],
[
"matrix",
"rank",
"clustering",
"low",
"sparse",
"algorithm",
"tensor",
"linear",
"subspace",
"kernel"
],
[
"natural",
"translation",
"language",
"text",
"word",
"words",
"sentences",
"languages",
"bert",
"semantic"
],
[
"social",
"users",
"recommendation",
"user",
"clustering",
"their",
"distance",
"recommender",
"ranking",
"classification"
],
[
"networks",
"prediction",
"network",
"time",
"neural",
"systems",
"equations",
"series",
"quantum",
"physics"
],
[
"policy",
"reinforcement",
"policies",
"control",
"optimal",
"reward",
"rl",
"agent",
"action",
"games"
]
] | 4.012689 | all-MiniLM-L6-v2 | 0.875 | 0.017256 | 0.135977 | 0.849118 |
ArXiv ML Papers | GMM | 45 | 20 | [
[
"gan",
"images",
"gans",
"image",
"generator",
"generative",
"generation",
"reconstruction",
"latent",
"synthetic"
],
[
"server",
"communication",
"devices",
"distributed",
"federated",
"computing",
"cloud",
"iot",
"decentralized",
"centralized"
],
[
"reward",
"policy",
"regret",
"optimal",
"bandit",
"bandits",
"bound",
"games",
"sqrt",
"online"
],
[
"speech",
"language",
"audio",
"attention",
"speaker",
"word",
"text",
"translation",
"languages",
"recurrent"
],
[
"physics",
"machine",
"quantum",
"structures",
"systems",
"classical",
"queries",
"here",
"protein",
"ml"
],
[
"classifier",
"classification",
"label",
"domain",
"class",
"classifiers",
"active",
"generalization",
"prediction",
"machine"
],
[
"inference",
"mcmc",
"distribution",
"gaussian",
"distributions",
"posterior",
"bayesian",
"variational",
"monte",
"carlo"
],
[
"programs",
"concepts",
"code",
"reasoning",
"language",
"logic",
"human",
"symbolic",
"visual",
"programming"
],
[
"word",
"recommendation",
"items",
"user",
"language",
"item",
"recommender",
"words",
"text",
"users"
],
[
"graph",
"node",
"graphs",
"nodes",
"structure",
"link",
"gnns",
"embedding",
"embeddings",
"representation"
],
[
"rank",
"convex",
"convergence",
"stochastic",
"linear",
"problems",
"optimization",
"matrix",
"algorithm",
"subspace"
],
[
"private",
"probability",
"distance",
"sample",
"privacy",
"metric",
"local",
"bound",
"differential",
"complexity"
],
[
"patients",
"health",
"disease",
"eeg",
"patient",
"clinical",
"using",
"svm",
"classification",
"cancer"
],
[
"series",
"demand",
"forecast",
"forecasting",
"forecasts",
"time",
"prediction",
"traffic",
"weather",
"day"
],
[
"fair",
"fairness",
"ml",
"bias",
"social",
"causal",
"research",
"human",
"groups",
"outcomes"
],
[
"against",
"attacks",
"adversarial",
"attack",
"robustness",
"defense",
"perturbations",
"examples",
"robust",
"box"
],
[
"images",
"convolutional",
"image",
"object",
"segmentation",
"detection",
"video",
"cnn",
"feature",
"supervised"
],
[
"networks",
"architectures",
"deep",
"neural",
"layer",
"network",
"pruning",
"hardware",
"accuracy",
"memory"
],
[
"policy",
"reinforcement",
"rl",
"agents",
"agent",
"reward",
"action",
"environment",
"control",
"policies"
],
[
"networks",
"dynamics",
"systems",
"parameters",
"neural",
"physics",
"uncertainty",
"equations",
"function",
"differential"
]
] | 4.040468 | all-MiniLM-L6-v2 | 0.905 | 0.010263 | 0.160799 | 0.865261 |
ArXiv ML Papers | GMM | 46 | 20 | [
[
"quantum",
"classical",
"computing",
"machine",
"noise",
"body",
"systems",
"queries",
"hilbert",
"protocol"
],
[
"item",
"user",
"items",
"recommendation",
"users",
"recommender",
"social",
"topic",
"ranking",
"topics"
],
[
"bound",
"sqrt",
"regret",
"online",
"bandit",
"bandits",
"policy",
"agents",
"reward",
"agent"
],
[
"relu",
"networks",
"layer",
"neural",
"functions",
"network",
"function",
"activation",
"loss",
"uncertainty"
],
[
"sentences",
"word",
"translation",
"languages",
"language",
"words",
"text",
"natural",
"semantic",
"sentence"
],
[
"segmentation",
"ct",
"image",
"images",
"cell",
"covid",
"net",
"imaging",
"medical",
"brain"
],
[
"images",
"gan",
"gans",
"image",
"generative",
"object",
"domain",
"scale",
"source",
"supervised"
],
[
"speaker",
"speech",
"music",
"asr",
"acoustic",
"audio",
"recognition",
"end",
"quality",
"signal"
],
[
"fairness",
"bias",
"ml",
"label",
"machine",
"classifier",
"class",
"causal",
"labels",
"classification"
],
[
"structure",
"embedding",
"link",
"gnns",
"node",
"graphs",
"graph",
"nodes",
"representation",
"embeddings"
],
[
"human",
"explanations",
"reasoning",
"attention",
"concepts",
"visual",
"explanation",
"symbolic",
"brain",
"task"
],
[
"driving",
"object",
"detection",
"traffic",
"vehicle",
"video",
"spatial",
"objects",
"3d",
"dataset"
],
[
"matrix",
"kernel",
"rank",
"clustering",
"regression",
"algorithm",
"sparse",
"subspace",
"low",
"tensor"
],
[
"architecture",
"memory",
"accuracy",
"nas",
"pruning",
"hardware",
"architectures",
"networks",
"neural",
"layer"
],
[
"reward",
"reinforcement",
"control",
"action",
"agent",
"agents",
"rl",
"policy",
"environment",
"policies"
],
[
"gradient",
"optimization",
"stochastic",
"convergence",
"convex",
"bounds",
"descent",
"bayesian",
"posterior",
"function"
],
[
"machine",
"classifier",
"classification",
"feature",
"time",
"forest",
"features",
"ensemble",
"health",
"day"
],
[
"distributed",
"private",
"communication",
"federated",
"privacy",
"devices",
"server",
"decentralized",
"local",
"complexity"
],
[
"forecast",
"prediction",
"time",
"forecasting",
"series",
"processes",
"distribution",
"modeling",
"dynamics",
"demand"
],
[
"adversarial",
"robustness",
"attack",
"against",
"defense",
"attacks",
"perturbations",
"examples",
"robust",
"attacker"
]
] | 3.985345 | all-MiniLM-L6-v2 | 0.915 | 0.001838 | 0.149346 | 0.860443 |
ArXiv ML Papers | GMM | 43 | 30 | [
[
"audio",
"music",
"visual",
"video",
"representations",
"speech",
"style",
"signals",
"media",
"separation"
],
[
"embeddings",
"graph",
"link",
"gnn",
"node",
"representation",
"nodes",
"gnns",
"embedding",
"graphs"
],
[
"bandit",
"bounds",
"bound",
"stochastic",
"regret",
"sqrt",
"convex",
"optimal",
"gradient",
"online"
],
[
"classifier",
"patient",
"health",
"patients",
"cancer",
"disease",
"classification",
"prediction",
"machine",
"svm"
],
[
"images",
"generator",
"generative",
"gan",
"gans",
"image",
"generation",
"synthetic",
"resolution",
"adversarial"
],
[
"bayesian",
"posterior",
"inference",
"variables",
"latent",
"variational",
"distributions",
"generative",
"likelihood",
"distribution"
],
[
"machine",
"gradient",
"boosting",
"optimization",
"tree",
"convergence",
"incremental",
"algorithms",
"ensemble",
"algorithm"
],
[
"eeg",
"lstm",
"energy",
"series",
"recurrent",
"time",
"temporal",
"rnn",
"demand",
"prediction"
],
[
"classical",
"quantum",
"queries",
"hilbert",
"space",
"computing",
"noise",
"protocol",
"machine",
"body"
],
[
"relu",
"network",
"deep",
"networks",
"layer",
"input",
"neural",
"uncertainty",
"normalization",
"generalization"
],
[
"object",
"features",
"image",
"representations",
"domain",
"visual",
"supervised",
"self",
"images",
"video"
],
[
"speech",
"speaker",
"audio",
"acoustic",
"asr",
"recognition",
"end",
"enhancement",
"languages",
"voice"
],
[
"rl",
"control",
"simulation",
"human",
"autonomous",
"robot",
"agents",
"driving",
"safety",
"agent"
],
[
"differential",
"complexity",
"privacy",
"exchange",
"sample",
"private",
"distribution",
"queries",
"differentially",
"gaussian"
],
[
"users",
"code",
"ml",
"user",
"software",
"fact",
"engineering",
"automated",
"been",
"machine"
],
[
"hardware",
"pruning",
"memory",
"nas",
"architectures",
"quantization",
"accuracy",
"architecture",
"search",
"networks"
],
[
"examples",
"attacks",
"attack",
"box",
"robustness",
"robust",
"perturbations",
"against",
"adversarial",
"defense"
],
[
"graph",
"nodes",
"clustering",
"graphical",
"communities",
"graphs",
"social",
"community",
"structure",
"clusters"
],
[
"rl",
"policy",
"action",
"agent",
"reinforcement",
"tasks",
"imitation",
"reward",
"actions",
"environments"
],
[
"differential",
"function",
"equations",
"equation",
"physics",
"physical",
"informed",
"neural",
"systems",
"dynamics"
],
[
"tensor",
"algorithm",
"matrix",
"rank",
"sparse",
"regression",
"linear",
"approximation",
"low",
"subspace"
],
[
"language",
"translation",
"text",
"sentences",
"word",
"words",
"natural",
"languages",
"attention",
"bert"
],
[
"prediction",
"iot",
"vehicle",
"speed",
"traffic",
"location",
"transportation",
"vehicles",
"driving",
"spatial"
],
[
"policy",
"reward",
"policies",
"control",
"games",
"rl",
"reinforcement",
"optimal",
"action",
"value"
],
[
"forecasts",
"lead",
"series",
"forecast",
"time",
"forecasting",
"day",
"anomaly",
"probabilistic",
"weather"
],
[
"metric",
"labels",
"label",
"classification",
"classifier",
"class",
"classes",
"domain",
"multi",
"active"
],
[
"protein",
"interactions",
"user",
"items",
"recommender",
"users",
"recommendation",
"embedding",
"item",
"knowledge"
],
[
"3d",
"segmentation",
"image",
"images",
"convolutional",
"cnn",
"imaging",
"reconstruction",
"covid",
"net"
],
[
"causal",
"treatment",
"bias",
"fair",
"fairness",
"outcomes",
"interventions",
"groups",
"social",
"ml"
],
[
"agent",
"decentralized",
"devices",
"privacy",
"server",
"distributed",
"federated",
"communication",
"local",
"teacher"
]
] | 5.541347 | all-MiniLM-L6-v2 | 0.836667 | -0.0103 | 0.14496 | 0.858357 |
ArXiv ML Papers | GMM | 44 | 30 | [
[
"speech",
"audio",
"music",
"text",
"style",
"language",
"end",
"languages",
"recognition",
"acoustic"
],
[
"human",
"brain",
"visual",
"eeg",
"decoding",
"humans",
"cognitive",
"differences",
"here",
"neurons"
],
[
"bounds",
"likelihood",
"sample",
"divergence",
"distributions",
"bayesian",
"distribution",
"bound",
"probabilistic",
"error"
],
[
"gradient",
"sqrt",
"bandit",
"convergence",
"optimal",
"regret",
"optimization",
"stochastic",
"convex",
"algorithm"
],
[
"nas",
"pruning",
"hardware",
"quantization",
"architecture",
"architectures",
"networks",
"compression",
"bit",
"search"
],
[
"gnns",
"node",
"link",
"nodes",
"graphs",
"graph",
"structure",
"embedding",
"representation",
"embeddings"
],
[
"explanation",
"logic",
"explanations",
"concepts",
"reasoning",
"symbolic",
"ml",
"explainable",
"explaining",
"interpretability"
],
[
"anomaly",
"detection",
"series",
"time",
"detect",
"social",
"behavior",
"anomalies",
"monitoring",
"users"
],
[
"nearest",
"svm",
"classifier",
"classification",
"label",
"neighbor",
"kernel",
"support",
"regression",
"distance"
],
[
"patient",
"cancer",
"patients",
"health",
"medical",
"protein",
"disease",
"covid",
"diagnosis",
"clinical"
],
[
"segmentation",
"images",
"medical",
"image",
"imaging",
"shape",
"3d",
"net",
"brain",
"reconstruction"
],
[
"words",
"text",
"translation",
"language",
"sentences",
"word",
"bert",
"nlp",
"natural",
"attention"
],
[
"activation",
"relu",
"neural",
"networks",
"layer",
"network",
"bounds",
"function",
"width",
"functions"
],
[
"agent",
"rl",
"reward",
"environment",
"games",
"reinforcement",
"policy",
"agents",
"control",
"policies"
],
[
"parallel",
"computing",
"optimization",
"memory",
"resource",
"computation",
"cloud",
"cost",
"consumption",
"iot"
],
[
"engineering",
"quantum",
"system",
"test",
"materials",
"material",
"physical",
"measurement",
"ml",
"machine"
],
[
"clustering",
"sparse",
"matrix",
"rank",
"tensor",
"algorithm",
"low",
"regression",
"noise",
"algorithms"
],
[
"defense",
"adversarial",
"against",
"robustness",
"attack",
"attacks",
"perturbations",
"examples",
"robust",
"box"
],
[
"object",
"detection",
"image",
"recognition",
"feature",
"video",
"cnn",
"classification",
"vision",
"visual"
],
[
"posterior",
"distributions",
"variational",
"gaussian",
"bayesian",
"inference",
"uncertainty",
"mcmc",
"distribution",
"approximate"
],
[
"informed",
"equations",
"quantum",
"systems",
"physics",
"differential",
"equation",
"parameters",
"dynamics",
"physical"
],
[
"fair",
"human",
"machine",
"interventions",
"users",
"user",
"bias",
"groups",
"social",
"fairness"
],
[
"ranking",
"recommender",
"user",
"recommendation",
"users",
"items",
"metric",
"embedding",
"item",
"distance"
],
[
"classifier",
"prediction",
"label",
"ensemble",
"class",
"generalization",
"domain",
"bias",
"classification",
"classifiers"
],
[
"signal",
"sound",
"speech",
"enhancement",
"speaker",
"acoustic",
"separation",
"audio",
"convolutional",
"cnn"
],
[
"weather",
"time",
"forecasting",
"prediction",
"series",
"forecasts",
"day",
"forecast",
"ensemble",
"power"
],
[
"gan",
"image",
"generative",
"images",
"generator",
"likelihood",
"generation",
"latent",
"gans",
"vae"
],
[
"lstm",
"recurrent",
"rnn",
"series",
"time",
"rnns",
"temporal",
"sequence",
"memory",
"demand"
],
[
"object",
"3d",
"vehicle",
"vehicles",
"driving",
"traffic",
"speed",
"sensor",
"autonomous",
"location"
],
[
"privacy",
"distributed",
"federated",
"communication",
"devices",
"private",
"decentralized",
"server",
"local",
"agent"
]
] | 5.480684 | all-MiniLM-L6-v2 | 0.836667 | 0.000041 | 0.163247 | 0.884124 |
ArXiv ML Papers | GMM | 45 | 30 | [
[
"images",
"3d",
"video",
"object",
"pose",
"scene",
"objects",
"detection",
"cnn",
"reconstruction"
],
[
"posterior",
"bayesian",
"carlo",
"mcmc",
"monte",
"gaussian",
"processes",
"inference",
"distributions",
"distribution"
],
[
"bandit",
"regret",
"bound",
"arm",
"reward",
"algorithm",
"armed",
"sqrt",
"online",
"bandits"
],
[
"word",
"translation",
"text",
"language",
"languages",
"words",
"natural",
"sentences",
"attention",
"task"
],
[
"error",
"class",
"risk",
"generalization",
"distribution",
"bounds",
"sample",
"pac",
"decision",
"test"
],
[
"graph",
"gnns",
"nodes",
"link",
"embedding",
"node",
"graphs",
"gnn",
"representation",
"networks"
],
[
"image",
"convolutional",
"vision",
"pre",
"classification",
"feature",
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"recognition",
"domain",
"images"
],
[
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"private",
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"server",
"decentralized",
"differential",
"distributed",
"differentially",
"devices",
"local"
],
[
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"latent",
"vae",
"variables",
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"posterior"
],
[
"medical",
"segmentation",
"covid",
"images",
"image",
"net",
"brain",
"imaging",
"shape",
"cell"
],
[
"time",
"forecasting",
"forecast",
"series",
"forecasts",
"prediction",
"day",
"weather",
"ensemble",
"vehicle"
],
[
"reinforcement",
"policy",
"agent",
"rl",
"games",
"optimal",
"control",
"agents",
"policies",
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],
[
"adversarial",
"attacks",
"robustness",
"against",
"perturbations",
"attack",
"robust",
"defense",
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],
[
"recovery",
"low",
"rank",
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"sparse",
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"noise",
"linear",
"alternating",
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],
[
"recommendation",
"item",
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"language",
"word",
"ranking",
"users"
],
[
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"gans",
"adversarial",
"resolution",
"generate",
"image",
"images",
"gan",
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],
[
"series",
"demand",
"temporal",
"time",
"prediction",
"traffic",
"lstm",
"recurrent",
"event",
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],
[
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"groups",
"bias",
"outcomes",
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],
[
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],
[
"memory",
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],
[
"parameters",
"equation",
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"optimization",
"physical",
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"informed",
"physics",
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],
[
"recognition",
"speech",
"audio",
"visual",
"signal",
"signals",
"eeg",
"style",
"music",
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],
[
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"gradient",
"convergence",
"descent",
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"sgd",
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],
[
"classification",
"machine",
"concept",
"ml",
"classifier",
"software",
"label",
"code",
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"concepts"
],
[
"algorithm",
"clusters",
"clustering",
"cluster",
"means",
"subspace",
"semi",
"problem",
"algorithms",
"graph"
],
[
"nearest",
"manifold",
"rank",
"kernel",
"similarity",
"dimensionality",
"distance",
"measure",
"metric",
"regression"
],
[
"speaker",
"acoustic",
"audio",
"speech",
"asr",
"quality",
"recognition",
"training",
"task",
"enhancement"
],
[
"game",
"robot",
"agent",
"agents",
"actions",
"visual",
"human",
"environments",
"reasoning",
"environment"
],
[
"health",
"disease",
"patients",
"cancer",
"classification",
"svm",
"predict",
"patient",
"classifier",
"machine"
],
[
"policy",
"exploration",
"action",
"rl",
"reward",
"policies",
"control",
"imitation",
"reinforcement",
"actions"
]
] | 5.6155 | all-MiniLM-L6-v2 | 0.843333 | 0.005776 | 0.145922 | 0.864035 |
ArXiv ML Papers | GMM | 46 | 30 | [
[
"noise",
"classical",
"quantum",
"hilbert",
"machine",
"queries",
"computing",
"protocol",
"space",
"body"
],
[
"topics",
"embeddings",
"metric",
"protein",
"clustering",
"sequences",
"distance",
"word",
"embedding",
"words"
],
[
"policy",
"control",
"variance",
"forgetting",
"decision",
"optimal",
"rl",
"reinforcement",
"algorithms",
"trajectory"
],
[
"network",
"activation",
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],
[
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"adaptation",
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],
[
"vehicle",
"detect",
"detection",
"attacks",
"traffic",
"video",
"iot",
"attack",
"malware",
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],
[
"generative",
"likelihood",
"latent",
"variables",
"gan",
"inference",
"vae",
"variational",
"distribution",
"posterior"
],
[
"ct",
"brain",
"medical",
"image",
"images",
"imaging",
"segmentation",
"cell",
"covid",
"net"
],
[
"explanations",
"explanation",
"reasoning",
"logic",
"concepts",
"explainable",
"black",
"interpretability",
"interpretable",
"human"
],
[
"item",
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"recommender",
"users",
"user",
"recommendation",
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],
[
"tensor",
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"sparse",
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"recovery",
"norm",
"noise",
"sparsity"
],
[
"robust",
"defense",
"examples",
"attack",
"robustness",
"attacks",
"adversarial",
"box",
"against",
"perturbations"
],
[
"hardware",
"pruning",
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"quantization",
"nas",
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"layer"
],
[
"graph",
"graphs",
"node",
"gnns",
"nodes",
"link",
"structure",
"embedding",
"gnn",
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],
[
"object",
"image",
"resolution",
"cnn",
"representations",
"visual",
"gan",
"text",
"images",
"pre"
],
[
"equation",
"design",
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"differential",
"systems",
"dynamics",
"informed",
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"physics",
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],
[
"time",
"series",
"forecasting",
"forecast",
"forecasts",
"prediction",
"weather",
"day",
"demand",
"temporal"
],
[
"fair",
"fairness",
"outcomes",
"groups",
"social",
"bias",
"ml",
"research",
"causal",
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],
[
"gradient",
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"sample",
"stochastic",
"bayesian",
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"bounds",
"convergence",
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],
[
"audio",
"speaker",
"acoustic",
"speech",
"music",
"sound",
"quality",
"signal",
"enhancement",
"asr"
],
[
"classification",
"classifier",
"ensemble",
"machine",
"boosting",
"class",
"label",
"classifiers",
"prediction",
"stream"
],
[
"distance",
"feature",
"kernel",
"metric",
"regression",
"manifold",
"nearest",
"method",
"kernels",
"points"
],
[
"process",
"research",
"user",
"software",
"social",
"reports",
"machine",
"media",
"text",
"fact"
],
[
"attention",
"language",
"semantic",
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"word",
"natural",
"text",
"task",
"words",
"bert"
],
[
"object",
"3d",
"objects",
"video",
"point",
"visual",
"robot",
"pose",
"scene",
"driving"
],
[
"games",
"sqrt",
"bandit",
"bandits",
"regret",
"reward",
"bound",
"arm",
"policy",
"algorithm"
],
[
"languages",
"translation",
"speech",
"end",
"recurrent",
"character",
"language",
"english",
"rnn",
"recognition"
],
[
"distributed",
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"communication",
"privacy",
"server",
"devices",
"decentralized",
"convergence",
"local",
"private"
],
[
"signals",
"energy",
"activity",
"health",
"eeg",
"wearable",
"brain",
"were",
"human",
"subject"
],
[
"policy",
"rl",
"agent",
"agents",
"reinforcement",
"reward",
"action",
"environment",
"actions",
"robot"
]
] | 5.060708 | all-MiniLM-L6-v2 | 0.86 | -0.017335 | 0.14739 | 0.881051 |
ArXiv ML Papers | GMM | 43 | 40 | [
[
"transformation",
"preference",
"net",
"visual",
"smoothing",
"video",
"over",
"semantic",
"enhancement",
"related"
],
[
"gnns",
"gnn",
"nodes",
"node",
"graph",
"graphs",
"vertex",
"representation",
"spectral",
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],
[
"online",
"sqrt",
"armed",
"reward",
"regret",
"bandit",
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],
[
"predict",
"patients",
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"cancer",
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"patient",
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],
[
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],
[
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],
[
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"tree",
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"forest",
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"trees",
"classifier"
],
[
"energy",
"signals",
"eeg",
"brain",
"wearable",
"activity",
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"subject",
"human",
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],
[
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"rl",
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],
[
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],
[
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"pre",
"image",
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"attention",
"visual",
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],
[
"speaker",
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"asr",
"speech",
"signal",
"audio",
"music",
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],
[
"agent",
"environment",
"safety",
"driving",
"rl",
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"game",
"autonomous",
"ai"
],
[
"private",
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"privacy",
"differentially",
"quantum",
"guarantees",
"sample",
"complexity",
"preserving",
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],
[
"users",
"attack",
"profiles",
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"attacks",
"iot",
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"malicious",
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"malware"
],
[
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"hardware",
"nas",
"memory",
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],
[
"robust",
"against",
"perturbations",
"adversarial",
"attack",
"defense",
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"robustness",
"examples",
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],
[
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"graph",
"nodes",
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"network",
"service",
"trust",
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],
[
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"agent",
"policy",
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"environments",
"robot",
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],
[
"mcmc",
"monte",
"bayesian",
"posterior",
"carlo",
"inference",
"distributions",
"distribution",
"sampling",
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],
[
"rank",
"tensor",
"low",
"matrix",
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],
[
"translation",
"languages",
"language",
"text",
"english",
"bleu",
"sentences",
"question",
"words",
"word"
],
[
"prediction",
"speed",
"traffic",
"city",
"transportation",
"demand",
"temporal",
"vehicle",
"location",
"spatial"
],
[
"policies",
"rl",
"policy",
"action",
"control",
"reinforcement",
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"exploration",
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],
[
"software",
"machine",
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"developers",
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],
[
"labels",
"class",
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"loss",
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"metric",
"active",
"classification",
"label",
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],
[
"interactions",
"structure",
"graph",
"graphs",
"node",
"embedding",
"knowledge",
"protein",
"link",
"nodes"
],
[
"frame",
"video",
"detection",
"object",
"tracking",
"recognition",
"driving",
"face",
"cnn",
"system"
],
[
"fairness",
"fair",
"treatment",
"social",
"ml",
"groups",
"interventions",
"bias",
"outcomes",
"causal"
],
[
"communication",
"decentralized",
"centralized",
"devices",
"distributed",
"server",
"federated",
"privacy",
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],
[
"segmentation",
"image",
"medical",
"images",
"net",
"19",
"imaging",
"covid",
"convolutional",
"brain"
],
[
"3d",
"object",
"scene",
"pose",
"objects",
"visual",
"shape",
"images",
"depth",
"reconstruction"
],
[
"attention",
"item",
"recurrent",
"sequence",
"language",
"rnn",
"items",
"bert",
"embedding",
"rnns"
],
[
"forecasts",
"multivariate",
"weather",
"time",
"forecasting",
"prediction",
"day",
"series",
"forecast",
"demand"
],
[
"width",
"neural",
"relu",
"activation",
"convolutional",
"network",
"pooling",
"networks",
"neurons",
"functions"
],
[
"parameters",
"equation",
"systems",
"equations",
"physics",
"dynamics",
"quantum",
"informed",
"differential",
"optimization"
],
[
"cluster",
"algorithm",
"semi",
"clusters",
"subspace",
"means",
"clustering",
"series",
"graph",
"non"
],
[
"media",
"topic",
"corpus",
"sentiment",
"topics",
"text",
"language",
"documents",
"words",
"word"
],
[
"minimax",
"sgd",
"gradient",
"optimization",
"stochastic",
"convex",
"proximal",
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],
[
"bounds",
"frac",
"queries",
"algorithm",
"sample",
"divergence",
"bound",
"approximation",
"greedy",
"kernel"
]
] | 6.90215 | all-MiniLM-L6-v2 | 0.7925 | -0.016528 | 0.145007 | 0.877302 |
ArXiv ML Papers | GMM | 44 | 40 | [
[
"speech",
"text",
"acoustic",
"audio",
"languages",
"speaker",
"music",
"style",
"word",
"quality"
],
[
"differences",
"eeg",
"subject",
"human",
"brain",
"decoding",
"visual",
"signals",
"individual",
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],
[
"sample",
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[
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],
[
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"nas",
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"compression",
"search",
"scaling"
],
[
"gnns",
"nodes",
"node",
"graphs",
"graph",
"link",
"networks",
"embedding",
"embeddings",
"structure"
],
[
"explanation",
"reasoning",
"concepts",
"interpretability",
"logic",
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"counterfactual",
"features",
"explaining",
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],
[
"anomalies",
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"anomaly",
"behavior",
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],
[
"neighbor",
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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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],
[
"video",
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[
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[
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],
[
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],
[
"items",
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],
[
"classifier",
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],
[
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"speaker",
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"asr",
"enhancement",
"convolutional",
"recognition"
],
[
"day",
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"forecasting",
"xgboost",
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[
"space",
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],
[
"temporal",
"rnns",
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"forecasting",
"series",
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"demand",
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"memory",
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],
[
"traffic",
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"sensor",
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[
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[
"regret",
"bandit",
"sqrt",
"bandits",
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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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"treatment",
"causal",
"inference",
"bayesian",
"observational",
"interventions",
"variables"
],
[
"agent",
"reinforcement",
"rl",
"robot",
"policy",
"environment",
"environments",
"agents",
"imitation",
"reward"
]
] | 7.214933 | all-MiniLM-L6-v2 | 0.815 | -0.016414 | 0.149405 | 0.88353 |
ArXiv ML Papers | GMM | 45 | 40 | [
[
"image",
"detection",
"video",
"object",
"visual",
"recognition",
"images",
"feature",
"supervised",
"cnn"
],
[
"probabilistic",
"graphical",
"markov",
"posterior",
"processes",
"inference",
"bayesian",
"likelihood",
"carlo",
"monte"
],
[
"bandits",
"arm",
"armed",
"sqrt",
"regret",
"bandit",
"bound",
"online",
"reward",
"exploration"
],
[
"translation",
"attention",
"sentences",
"text",
"task",
"languages",
"source",
"language",
"words",
"word"
],
[
"testing",
"summary",
"statistics",
"tests",
"decision",
"test",
"shift",
"imputation",
"selection",
"distribution"
],
[
"graphs",
"gnns",
"graph",
"node",
"nodes",
"link",
"representation",
"embedding",
"embeddings",
"structure"
],
[
"covid",
"convolutional",
"cnn",
"19",
"classification",
"deep",
"was",
"were",
"dataset",
"feature"
],
[
"distributed",
"federated",
"server",
"privacy",
"communication",
"devices",
"decentralized",
"group",
"centralized",
"updates"
],
[
"vae",
"variational",
"generative",
"autoencoders",
"inference",
"variables",
"latent",
"autoencoder",
"divergence",
"likelihood"
],
[
"segmentation",
"medical",
"images",
"image",
"net",
"imaging",
"brain",
"convolutional",
"mri",
"shape"
],
[
"traffic",
"iot",
"computing",
"vehicle",
"sensor",
"service",
"attacks",
"detection",
"consumption",
"sensors"
],
[
"reward",
"constraints",
"policy",
"rl",
"reinforcement",
"control",
"safety",
"safe",
"policies",
"autonomous"
],
[
"attacks",
"attack",
"adversarial",
"against",
"defense",
"robustness",
"robust",
"examples",
"perturbations",
"attacker"
],
[
"low",
"sparse",
"rank",
"matrix",
"alternating",
"tensor",
"recovery",
"noise",
"algorithm",
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],
[
"language",
"word",
"text",
"words",
"product",
"topics",
"documents",
"media",
"categories",
"recommender"
],
[
"generative",
"gan",
"gans",
"generation",
"generate",
"generator",
"image",
"images",
"adversarial",
"resolution"
],
[
"demand",
"series",
"recommendation",
"time",
"rnn",
"forecasting",
"prediction",
"sequence",
"event",
"temporal"
],
[
"interventions",
"outcomes",
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"effect",
"causal",
"questions",
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"social",
"research"
],
[
"neural",
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"relu",
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],
[
"hardware",
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"memory",
"pruning",
"architecture",
"quantization",
"architectures",
"compression",
"search",
"accuracy"
],
[
"differential",
"equation",
"quantum",
"physics",
"physical",
"parameters",
"equations",
"informed",
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],
[
"music",
"acoustic",
"word",
"visual",
"generation",
"style",
"speech",
"representations",
"audio",
"sound"
],
[
"convex",
"stochastic",
"descent",
"sgd",
"optimization",
"gradient",
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],
[
"machine",
"ml",
"labels",
"code",
"classifiers",
"classifier",
"classification",
"label",
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"concept"
],
[
"sparsity",
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"set",
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"norm",
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"solution"
],
[
"rank",
"kernel",
"metric",
"distance",
"similarity",
"measure",
"nearest",
"dimensionality",
"ranking",
"manifold"
],
[
"acoustic",
"voice",
"audio",
"speech",
"asr",
"recognition",
"enhancement",
"end",
"speaker",
"languages"
],
[
"robot",
"environments",
"visual",
"agents",
"actions",
"agent",
"human",
"games",
"game",
"environment"
],
[
"health",
"patients",
"disease",
"patient",
"cancer",
"svm",
"diagnosis",
"classification",
"classifier",
"machine"
],
[
"rl",
"policies",
"reward",
"planning",
"control",
"policy",
"action",
"reinforcement",
"imitation",
"exploration"
],
[
"forecast",
"weather",
"forecasting",
"forecasts",
"series",
"prediction",
"probabilistic",
"time",
"day",
"ensemble"
],
[
"fairness",
"fair",
"groups",
"ml",
"bias",
"machine",
"face",
"human",
"outcomes",
"research"
],
[
"neurons",
"temporal",
"functional",
"brain",
"systems",
"dynamics",
"decoding",
"biological",
"how",
"networks"
],
[
"input",
"uncertainty",
"estimate",
"ensemble",
"distributions",
"neural",
"weights",
"dropout",
"estimation",
"bayesian"
],
[
"gaussian",
"exchange",
"posterior",
"gradient",
"delta",
"mcmc",
"fidelity",
"distributions",
"approximation",
"approximate"
],
[
"object",
"objects",
"3d",
"pose",
"shape",
"point",
"scene",
"depth",
"spatial",
"estimation"
],
[
"cluster",
"algorithm",
"clusters",
"means",
"clustering",
"algorithms",
"non",
"series",
"analysis",
"semi"
],
[
"differential",
"sensitive",
"privacy",
"user",
"quantum",
"attacks",
"private",
"differentially",
"sample",
"guarantees"
],
[
"sample",
"distribution",
"bounds",
"generalization",
"risk",
"error",
"bound",
"class",
"classes",
"pac"
],
[
"agent",
"agents",
"games",
"policy",
"game",
"optimal",
"reinforcement",
"communication",
"reward",
"value"
]
] | 7.441308 | all-MiniLM-L6-v2 | 0.7925 | -0.019606 | 0.149441 | 0.879719 |
ArXiv ML Papers | GMM | 46 | 40 | [
[
"noise",
"classical",
"quantum",
"hilbert",
"machine",
"queries",
"computing",
"protocol",
"space",
"body"
],
[
"metric",
"similarity",
"distance",
"clustering",
"topics",
"label",
"measure",
"embedding",
"documents",
"causal"
],
[
"forgetting",
"knowledge",
"tasks",
"task",
"meta",
"decision",
"group",
"distillation",
"online",
"iot"
],
[
"networks",
"relu",
"activation",
"layer",
"neural",
"network",
"input",
"width",
"neurons",
"function"
],
[
"unlabeled",
"shift",
"source",
"adaptation",
"target",
"labeled",
"domain",
"active",
"imbalance",
"label"
],
[
"vehicle",
"detection",
"attacks",
"traffic",
"video",
"iot",
"attack",
"detect",
"malware",
"object"
],
[
"imputation",
"dropout",
"estimation",
"uncertainty",
"predictive",
"posterior",
"generalization",
"pac",
"bayesian",
"distribution"
],
[
"segmentation",
"image",
"net",
"images",
"imaging",
"shape",
"medical",
"mri",
"brain",
"covid"
],
[
"black",
"concepts",
"logic",
"explanations",
"reasoning",
"explanation",
"explainable",
"decision",
"interpretability",
"features"
],
[
"recommender",
"item",
"recommendation",
"items",
"users",
"user",
"preferences",
"embedding",
"recommendations",
"systems"
],
[
"matrix",
"rank",
"tensor",
"clustering",
"noise",
"sparse",
"low",
"subspace",
"algorithm",
"norm"
],
[
"adversarial",
"attack",
"against",
"attacks",
"perturbations",
"robustness",
"examples",
"robust",
"defense",
"box"
],
[
"nas",
"hardware",
"architectures",
"quantization",
"pruning",
"architecture",
"search",
"networks",
"neural",
"compression"
],
[
"graph",
"node",
"graphs",
"nodes",
"structure",
"link",
"social",
"communities",
"links",
"community"
],
[
"image",
"cnn",
"object",
"visual",
"pre",
"supervised",
"images",
"convolutional",
"detection",
"vision"
],
[
"equations",
"informed",
"differential",
"physics",
"dynamics",
"physical",
"equation",
"optimization",
"systems",
"parameters"
],
[
"weather",
"day",
"hierarchical",
"forecasts",
"series",
"time",
"forecasting",
"forecast",
"temperature",
"probabilistic"
],
[
"bias",
"fair",
"fairness",
"groups",
"human",
"face",
"machine",
"how",
"decisions",
"outcomes"
],
[
"monte",
"posterior",
"gaussian",
"carlo",
"distributions",
"inference",
"variational",
"distribution",
"bayesian",
"mcmc"
],
[
"speaker",
"audio",
"speech",
"acoustic",
"music",
"signal",
"enhancement",
"sound",
"quality",
"separation"
],
[
"label",
"ensemble",
"classifiers",
"classifier",
"classification",
"labels",
"noisy",
"class",
"datasets",
"noise"
],
[
"nearest",
"kernel",
"tree",
"classification",
"regression",
"boosting",
"neighbor",
"machine",
"algorithms",
"prediction"
],
[
"media",
"text",
"social",
"software",
"process",
"ml",
"industry",
"research",
"user",
"machine"
],
[
"words",
"language",
"text",
"sentences",
"semantic",
"attention",
"natural",
"word",
"task",
"sentence"
],
[
"scene",
"videos",
"video",
"visual",
"objects",
"robot",
"human",
"object",
"tracking",
"eye"
],
[
"regret",
"bandit",
"arm",
"ucb",
"online",
"armed",
"bound",
"bandits",
"sqrt",
"reward"
],
[
"languages",
"speech",
"translation",
"character",
"language",
"recurrent",
"recognition",
"rnn",
"end",
"english"
],
[
"computing",
"distributed",
"parallel",
"ml",
"computation",
"execution",
"communication",
"cloud",
"memory",
"consumption"
],
[
"brain",
"wearable",
"subject",
"eeg",
"signals",
"activity",
"energy",
"memory",
"human",
"features"
],
[
"agents",
"games",
"reinforcement",
"agent",
"human",
"feedback",
"environment",
"game",
"actions",
"rl"
],
[
"health",
"cancer",
"patient",
"patients",
"clinical",
"research",
"protein",
"medical",
"disease",
"net"
],
[
"optimal",
"markov",
"reinforcement",
"convergence",
"policies",
"policy",
"games",
"stochastic",
"agents",
"agent"
],
[
"forecasting",
"series",
"prediction",
"lstm",
"time",
"traffic",
"forecast",
"demand",
"term",
"market"
],
[
"communication",
"decentralized",
"distributed",
"federated",
"privacy",
"devices",
"server",
"centralized",
"local",
"updates"
],
[
"images",
"gans",
"gan",
"image",
"generative",
"latent",
"generator",
"variational",
"vae",
"generation"
],
[
"object",
"3d",
"map",
"depth",
"images",
"estimation",
"pose",
"reconstruction",
"geometry",
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],
[
"reinforcement",
"policies",
"control",
"imitation",
"policy",
"reward",
"rl",
"action",
"environment",
"robot"
],
[
"convergence",
"gradient",
"sgd",
"descent",
"stochastic",
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"optimization",
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],
[
"graphs",
"gnns",
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"node",
"gnn",
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"spectral",
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"embedding",
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],
[
"privacy",
"differentially",
"private",
"differential",
"wise",
"guarantees",
"complexity",
"sample",
"preserving",
"queries"
]
] | 6.893929 | all-MiniLM-L6-v2 | 0.795 | -0.003586 | 0.149456 | 0.882221 |
ArXiv ML Papers | GMM | 43 | 50 | [
[
"transformation",
"enhancement",
"dimensional",
"net",
"smoothing",
"over",
"images",
"video",
"transformer",
"frames"
],
[
"node",
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"cluster",
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[
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"arm",
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[
"health",
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"patient",
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"disease",
"patients",
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],
[
"images",
"adversarial",
"gan",
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"generation",
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"generator",
"resolution",
"image",
"generate"
],
[
"vae",
"autoencoders",
"generative",
"variables",
"latent",
"variational",
"likelihood",
"autoencoder",
"distribution",
"inference"
],
[
"regression",
"tree",
"boosting",
"ensemble",
"forest",
"stream",
"machine",
"trees",
"classification",
"combination"
],
[
"wearable",
"activity",
"temporal",
"eeg",
"brain",
"series",
"energy",
"human",
"patterns",
"time"
],
[
"policy",
"online",
"agent",
"optimal",
"communication",
"regret",
"distributed",
"reinforcement",
"rl",
"iot"
],
[
"shift",
"deep",
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"uncertainty",
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"dnns",
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],
[
"adaptation",
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],
[
"media",
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],
[
"rl",
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"ai",
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[
"private",
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"quantum",
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],
[
"attack",
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],
[
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[
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[
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"spread",
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],
[
"reward",
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"robot",
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],
[
"bayesian",
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"posterior",
"variational",
"mcmc",
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"inference",
"carlo",
"estimation"
],
[
"sparse",
"linear",
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"rank",
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"alternating",
"tensor",
"matrix",
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],
[
"languages",
"translation",
"language",
"sentences",
"attention",
"word",
"sequence",
"bert",
"question",
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],
[
"sensor",
"prediction",
"vehicle",
"location",
"traffic",
"sensors",
"transportation",
"speed",
"demand",
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],
[
"action",
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"imitation",
"policy",
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],
[
"software",
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],
[
"label",
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"nearest",
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],
[
"graphs",
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"node",
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[
"detection",
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"face",
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"segmentation",
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],
[
"bias",
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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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"recognition",
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[
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[
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[
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"segmentation",
"imaging",
"shape",
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"images",
"image",
"brain"
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[
"protein",
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"effect",
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[
"objects",
"scene",
"pose",
"object",
"3d",
"robot",
"point",
"driving",
"depth",
"estimation"
],
[
"protein",
"quantum",
"spectrum",
"physics",
"machine",
"power",
"chemical",
"parameters",
"systems",
"chain"
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[
"inverse",
"images",
"flow",
"imaging",
"phase",
"reconstruction",
"denoising",
"3d",
"image",
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[
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"concepts",
"interaction",
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"textit",
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"features",
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],
[
"music",
"visual",
"audio",
"style",
"speech",
"signals",
"signal",
"vae",
"separation",
"word"
]
] | 8.365348 | all-MiniLM-L6-v2 | 0.748 | -0.038428 | 0.144147 | 0.885943 |
ArXiv ML Papers | GMM | 44 | 50 | [
[
"music",
"audio",
"speech",
"style",
"media",
"vae",
"word",
"quality",
"generation",
"acoustic"
],
[
"brain",
"eeg",
"decoding",
"subject",
"signals",
"functional",
"individual",
"differences",
"artificial",
"activity"
],
[
"bayesian",
"inference",
"variational",
"markov",
"bounds",
"posterior",
"mcmc",
"graphical",
"topic",
"likelihood"
],
[
"convergence",
"convex",
"minimax",
"gradient",
"stochastic",
"optimization",
"descent",
"variance",
"proximal",
"function"
],
[
"pruning",
"hardware",
"nas",
"quantization",
"accelerators",
"memory",
"architectures",
"scaling",
"architecture",
"compression"
],
[
"graph",
"embedding",
"graphs",
"gnns",
"nodes",
"representation",
"node",
"link",
"networks",
"embeddings"
],
[
"logic",
"reasoning",
"visual",
"question",
"knowledge",
"textit",
"integration",
"four",
"symbolic",
"answer"
],
[
"event",
"time",
"demand",
"location",
"energy",
"challenges",
"temporal",
"deep",
"series",
"prediction"
],
[
"neighbor",
"classification",
"sets",
"feature",
"clustering",
"analysis",
"kernel",
"distance",
"classifier",
"combination"
],
[
"face",
"bias",
"unlabeled",
"facial",
"supervised",
"labels",
"dataset",
"detection",
"imbalance",
"labeled"
],
[
"images",
"resolution",
"optical",
"phase",
"ct",
"transform",
"address",
"light",
"imaging",
"reconstruction"
],
[
"sentences",
"language",
"attention",
"text",
"words",
"sentence",
"translation",
"source",
"natural",
"word"
],
[
"activation",
"layer",
"network",
"networks",
"neural",
"width",
"relu",
"initialization",
"function",
"functions"
],
[
"reward",
"reinforcement",
"agent",
"rl",
"agents",
"environments",
"games",
"policy",
"imitation",
"game"
],
[
"parallel",
"computing",
"consumption",
"cloud",
"machine",
"search",
"descent",
"tasks",
"gradient",
"performance"
],
[
"monitoring",
"engineering",
"material",
"condition",
"test",
"tests",
"property",
"system",
"design",
"materials"
],
[
"factorization",
"low",
"matrices",
"matrix",
"algorithm",
"rank",
"decomposition",
"approximation",
"tensor",
"tensors"
],
[
"fact",
"language",
"word",
"detection",
"media",
"text",
"speech",
"content",
"multimodal",
"social"
],
[
"images",
"deep",
"convolutional",
"classification",
"pooling",
"object",
"visual",
"image",
"cnn",
"tasks"
],
[
"processes",
"variational",
"posterior",
"monte",
"bayesian",
"gaussian",
"carlo",
"mcmc",
"inference",
"approximate"
],
[
"equation",
"informed",
"quantum",
"physics",
"equations",
"differential",
"systems",
"optimization",
"parameters",
"physical"
],
[
"compliance",
"groups",
"fairness",
"machine",
"ml",
"fair",
"outcomes",
"identity",
"questions",
"utility"
],
[
"recommendation",
"ranking",
"items",
"embedding",
"item",
"metric",
"user",
"users",
"recommender",
"query"
],
[
"trust",
"ai",
"coverage",
"values",
"approximations",
"prediction",
"answering",
"missing",
"machine",
"question"
],
[
"enhancement",
"sound",
"audio",
"speaker",
"speech",
"acoustic",
"separation",
"convolutional",
"asr",
"recognition"
],
[
"series",
"time",
"day",
"forecasts",
"forecasting",
"forecast",
"weather",
"prediction",
"ensemble",
"xgboost"
],
[
"generative",
"space",
"autoencoders",
"manifold",
"variational",
"distribution",
"latent",
"vae",
"likelihood",
"inference"
],
[
"recurrent",
"lstm",
"rnn",
"rnns",
"term",
"memory",
"language",
"sequence",
"series",
"variable"
],
[
"vehicles",
"traffic",
"vehicle",
"driving",
"speed",
"sensors",
"recognition",
"accuracy",
"detection",
"safety"
],
[
"aggregation",
"distributed",
"updates",
"group",
"decentralized",
"devices",
"privacy",
"communication",
"server",
"federated"
],
[
"agents",
"online",
"agent",
"iot",
"communication",
"distributed",
"multi",
"reinforcement",
"channel",
"execution"
],
[
"medical",
"image",
"cell",
"images",
"segmentation",
"net",
"shape",
"covid",
"imaging",
"brain"
],
[
"software",
"disease",
"research",
"health",
"patient",
"covid",
"cancer",
"patients",
"19",
"predict"
],
[
"matrix",
"clustering",
"subspace",
"recovery",
"subspaces",
"noise",
"norm",
"algorithm",
"rank",
"sparsity"
],
[
"attack",
"robustness",
"examples",
"box",
"against",
"adversarial",
"perturbations",
"attacks",
"defense",
"robust"
],
[
"text",
"documents",
"word",
"topics",
"topic",
"language",
"document",
"categories",
"nlp",
"words"
],
[
"wise",
"quantum",
"private",
"privacy",
"differentially",
"differential",
"sample",
"complexity",
"queries",
"concept"
],
[
"generation",
"image",
"images",
"gans",
"generator",
"gan",
"generative",
"resolution",
"text",
"adversarial"
],
[
"variables",
"inference",
"outcome",
"treatment",
"causal",
"estimator",
"interventions",
"fidelity",
"observational",
"imputation"
],
[
"robot",
"rl",
"planning",
"control",
"reinforcement",
"robotic",
"policies",
"policy",
"goal",
"action"
],
[
"objects",
"videos",
"robot",
"human",
"object",
"video",
"motion",
"eye",
"interaction",
"mapping"
],
[
"label",
"classifier",
"classification",
"ensemble",
"classes",
"stream",
"classifiers",
"boosting",
"class",
"concept"
],
[
"object",
"depth",
"pose",
"3d",
"scene",
"point",
"estimation",
"cloud",
"objects",
"reconstruction"
],
[
"attention",
"feature",
"explainable",
"explanations",
"explanation",
"concepts",
"importance",
"black",
"interpretability",
"predictions"
],
[
"sqrt",
"arm",
"armed",
"bandit",
"regret",
"bandits",
"reward",
"bound",
"online",
"algorithm"
],
[
"privacy",
"attack",
"user",
"sensitive",
"private",
"attacks",
"bert",
"adversary",
"preserving",
"information"
],
[
"rl",
"sample",
"action",
"games",
"reinforcement",
"value",
"policies",
"control",
"policy",
"optimal"
],
[
"languages",
"language",
"speech",
"translation",
"asr",
"end",
"character",
"english",
"cross",
"recognition"
],
[
"detect",
"anomaly",
"intrusion",
"social",
"iot",
"code",
"attacks",
"detection",
"users",
"malware"
],
[
"transferring",
"transfer",
"alignment",
"domain",
"adaptation",
"shift",
"domains",
"source",
"target",
"knowledge"
]
] | 8.40698 | all-MiniLM-L6-v2 | 0.768 | -0.035974 | 0.144284 | 0.883965 |
ArXiv ML Papers | GMM | 45 | 50 | [
[
"frames",
"object",
"images",
"video",
"face",
"detection",
"recognition",
"frame",
"vehicle",
"spectral"
],
[
"probabilistic",
"inference",
"bayesian",
"quantum",
"mcmc",
"posterior",
"likelihood",
"markov",
"carlo",
"distribution"
],
[
"arm",
"armed",
"bound",
"bandits",
"sqrt",
"reward",
"regret",
"bandit",
"online",
"ucb"
],
[
"language",
"text",
"decoder",
"english",
"languages",
"translation",
"attention",
"bleu",
"multimodal",
"task"
],
[
"test",
"testing",
"tests",
"summary",
"imputation",
"statistics",
"hypothesis",
"costs",
"distribution",
"selection"
],
[
"graph",
"graphs",
"gnns",
"embedding",
"link",
"nodes",
"node",
"embeddings",
"representation",
"prediction"
],
[
"feature",
"recognition",
"classification",
"metrics",
"eeg",
"accuracy",
"deep",
"features",
"art",
"convolutional"
],
[
"privacy",
"communication",
"server",
"group",
"distributed",
"devices",
"federated",
"decentralized",
"centralized",
"aggregation"
],
[
"variational",
"latent",
"variables",
"divergence",
"inference",
"vae",
"likelihood",
"autoencoders",
"generative",
"autoencoder"
],
[
"segmentation",
"pooling",
"cnns",
"supervised",
"cnn",
"object",
"convolutional",
"resolution",
"images",
"image"
],
[
"cost",
"consumption",
"execution",
"computing",
"computation",
"distributed",
"cloud",
"ml",
"per",
"machine"
],
[
"policy",
"control",
"rl",
"reinforcement",
"safety",
"safe",
"policies",
"autonomous",
"constraints",
"environment"
],
[
"defense",
"attacks",
"adversarial",
"attack",
"perturbations",
"against",
"robustness",
"robust",
"examples",
"box"
],
[
"matrix",
"low",
"rank",
"decomposition",
"alternating",
"tensor",
"sparse",
"recovery",
"algorithm",
"matrices"
],
[
"question",
"words",
"questions",
"word",
"answering",
"language",
"sentences",
"reasoning",
"answer",
"entity"
],
[
"gans",
"generative",
"image",
"gan",
"generator",
"images",
"generation",
"adversarial",
"samples",
"generate"
],
[
"items",
"item",
"recommendation",
"recommender",
"users",
"ranking",
"user",
"embedding",
"recommendations",
"systems"
],
[
"interventions",
"series",
"outcome",
"causal",
"treatment",
"observational",
"effect",
"variables",
"time",
"inference"
],
[
"width",
"depth",
"bounds",
"function",
"network",
"networks",
"neural",
"relu",
"initialization",
"layer"
],
[
"precision",
"accuracy",
"compression",
"quantization",
"pruning",
"bit",
"hardware",
"memory",
"networks",
"neural"
],
[
"quantum",
"equations",
"physics",
"differential",
"informed",
"equation",
"parameters",
"physical",
"optimization",
"dynamics"
],
[
"speech",
"audio",
"signals",
"sound",
"signal",
"acoustic",
"music",
"style",
"generation",
"word"
],
[
"convex",
"stochastic",
"gradient",
"descent",
"convergence",
"optimization",
"sgd",
"proximal",
"accelerated",
"problems"
],
[
"reasoning",
"interpretability",
"black",
"explanation",
"explainable",
"explanations",
"concepts",
"logic",
"counterfactual",
"explaining"
],
[
"sparsity",
"norm",
"sparse",
"subspaces",
"lasso",
"screening",
"subspace",
"selection",
"covariates",
"noisy"
],
[
"kernel",
"manifold",
"metric",
"distance",
"approximation",
"structured",
"dimensionality",
"rank",
"nearest",
"neighbor"
],
[
"speaker",
"asr",
"speech",
"recognition",
"acoustic",
"end",
"audio",
"enhancement",
"training",
"rnn"
],
[
"game",
"human",
"agents",
"imitation",
"robot",
"agent",
"games",
"environments",
"environment",
"reinforcement"
],
[
"predict",
"svm",
"disease",
"cancer",
"health",
"research",
"machine",
"classification",
"patients",
"patient"
],
[
"policies",
"rl",
"action",
"reinforcement",
"policy",
"reward",
"control",
"goal",
"planning",
"exploration"
],
[
"forecast",
"power",
"day",
"weather",
"forecasting",
"forecasts",
"prediction",
"ensemble",
"temperature",
"year"
],
[
"bias",
"fairness",
"identity",
"algorithmic",
"fair",
"outcomes",
"face",
"groups",
"outlier",
"machine"
],
[
"neurons",
"brain",
"biological",
"systems",
"networks",
"filters",
"graph",
"activation",
"temporal",
"convolutional"
],
[
"dropout",
"weights",
"bayesian",
"networks",
"uncertainty",
"estimation",
"ensemble",
"neural",
"input",
"calibration"
],
[
"fidelity",
"approximate",
"gaussian",
"delta",
"processes",
"distributions",
"posterior",
"exchange",
"optimization",
"gradient"
],
[
"pose",
"objects",
"3d",
"object",
"shape",
"point",
"scene",
"estimation",
"depth",
"geometric"
],
[
"gaussian",
"non",
"kernel",
"time",
"regression",
"clusters",
"clustering",
"cluster",
"series",
"means"
],
[
"privacy",
"private",
"attacks",
"user",
"differentially",
"differential",
"quantum",
"sensitive",
"bert",
"sample"
],
[
"pac",
"error",
"bounds",
"generalization",
"sample",
"risk",
"bound",
"variance",
"distribution",
"estimator"
],
[
"markov",
"reinforcement",
"games",
"communication",
"policy",
"reward",
"agents",
"agent",
"optimal",
"game"
],
[
"19",
"medical",
"segmentation",
"covid",
"ct",
"net",
"image",
"images",
"were",
"brain"
],
[
"visual",
"sequence",
"memory",
"code",
"language",
"programs",
"attention",
"symbolic",
"natural",
"learns"
],
[
"supervised",
"labeled",
"samples",
"unlabeled",
"labels",
"datasets",
"augmentation",
"regularization",
"classification",
"semi"
],
[
"attacks",
"anomalies",
"detect",
"security",
"detection",
"intrusion",
"iot",
"anomaly",
"sensor",
"users"
],
[
"series",
"forecast",
"time",
"prediction",
"temporal",
"lstm",
"forecasting",
"demand",
"rnn",
"market"
],
[
"words",
"topic",
"word",
"topics",
"language",
"media",
"corpus",
"text",
"documents",
"social"
],
[
"clusters",
"communities",
"nodes",
"recovery",
"clustering",
"community",
"cluster",
"graph",
"set",
"greedy"
],
[
"adaptation",
"shift",
"target",
"domain",
"search",
"domains",
"transfer",
"nas",
"source",
"pre"
],
[
"research",
"machine",
"process",
"code",
"user",
"ml",
"humans",
"human",
"reasoning",
"software"
],
[
"label",
"classifier",
"classification",
"boosting",
"classifiers",
"ensemble",
"stream",
"prediction",
"class",
"labels"
]
] | 8.525204 | all-MiniLM-L6-v2 | 0.758 | -0.03042 | 0.147946 | 0.875139 |
ArXiv ML Papers | GMM | 46 | 50 | [
[
"epsilon",
"quantum",
"spectral",
"sampling",
"rank",
"entries",
"algorithm",
"bounds",
"classical",
"matrix"
],
[
"distance",
"sequences",
"measure",
"similarity",
"metric",
"manifold",
"ranking",
"objects",
"rank",
"embedding"
],
[
"tasks",
"knowledge",
"group",
"distillation",
"prediction",
"teacher",
"decision",
"forgetting",
"task",
"student"
],
[
"activation",
"layer",
"relu",
"neural",
"network",
"networks",
"function",
"functions",
"width",
"bounds"
],
[
"target",
"active",
"label",
"domain",
"labeled",
"unlabeled",
"adaptation",
"shift",
"imbalance",
"source"
],
[
"iot",
"video",
"detect",
"traffic",
"attacks",
"security",
"malware",
"detection",
"attack",
"code"
],
[
"label",
"expected",
"semantic",
"channels",
"population",
"level",
"during",
"channel",
"bayes",
"auc"
],
[
"medical",
"segmentation",
"imaging",
"images",
"image",
"shape",
"net",
"mri",
"brain",
"covid"
],
[
"logic",
"explainable",
"reasoning",
"explanations",
"explanation",
"black",
"concepts",
"decision",
"interpretability",
"features"
],
[
"recommender",
"recommendation",
"item",
"user",
"users",
"items",
"embedding",
"recommendations",
"systems",
"preferences"
],
[
"clustering",
"tensor",
"rank",
"low",
"matrix",
"subspace",
"noise",
"alternating",
"analysis",
"algorithm"
],
[
"attack",
"adversarial",
"attacks",
"against",
"defense",
"perturbations",
"robustness",
"examples",
"robust",
"box"
],
[
"pruning",
"nas",
"architectures",
"quantization",
"hardware",
"architecture",
"pooling",
"search",
"networks",
"neural"
],
[
"nodes",
"embedding",
"link",
"social",
"node",
"graph",
"graphs",
"structure",
"embeddings",
"representation"
],
[
"image",
"object",
"cnn",
"pre",
"visual",
"classification",
"detection",
"supervised",
"feature",
"vision"
],
[
"spectrum",
"physics",
"energy",
"power",
"design",
"dnn",
"optimization",
"edge",
"consumption",
"temperature"
],
[
"time",
"series",
"forecasting",
"forecasts",
"day",
"forecast",
"weather",
"probabilistic",
"hierarchical",
"spatial"
],
[
"groups",
"fair",
"fairness",
"machine",
"how",
"bias",
"human",
"face",
"outcomes",
"algorithmic"
],
[
"posterior",
"distribution",
"distributions",
"estimator",
"inference",
"mcmc",
"bayesian",
"carlo",
"monte",
"sample"
],
[
"audio",
"speech",
"speaker",
"signal",
"music",
"acoustic",
"enhancement",
"sound",
"quality",
"separation"
],
[
"label",
"ensemble",
"classifier",
"classifiers",
"classification",
"noisy",
"labels",
"class",
"noise",
"consistency"
],
[
"classification",
"kernel",
"regression",
"tree",
"machine",
"stream",
"nearest",
"classifier",
"boosting",
"prediction"
],
[
"social",
"user",
"users",
"online",
"software",
"industry",
"humans",
"research",
"ml",
"challenges"
],
[
"language",
"attention",
"semantic",
"text",
"recurrent",
"word",
"bert",
"sentences",
"translation",
"task"
],
[
"videos",
"video",
"visual",
"scene",
"human",
"robot",
"action",
"objects",
"eye",
"actions"
],
[
"sqrt",
"arm",
"regret",
"bandit",
"bandits",
"armed",
"reward",
"online",
"ucb",
"bound"
],
[
"character",
"languages",
"translation",
"speech",
"language",
"end",
"asr",
"english",
"recognition",
"decoder"
],
[
"distributed",
"computing",
"computation",
"cloud",
"execution",
"parallel",
"ml",
"communication",
"systems",
"memory"
],
[
"subject",
"wearable",
"activity",
"energy",
"eeg",
"brain",
"human",
"signals",
"svm",
"was"
],
[
"reinforcement",
"human",
"actions",
"agent",
"agents",
"feedback",
"language",
"robot",
"game",
"modal"
],
[
"medical",
"cancer",
"research",
"patient",
"disease",
"patients",
"health",
"healthcare",
"diagnosis",
"clinical"
],
[
"convergence",
"policies",
"policy",
"sample",
"markov",
"optimal",
"tilde",
"reinforcement",
"controller",
"value"
],
[
"lstm",
"forecast",
"series",
"demand",
"prediction",
"traffic",
"forecasting",
"time",
"market",
"term"
],
[
"distributed",
"devices",
"federated",
"communication",
"privacy",
"server",
"decentralized",
"centralized",
"updates",
"local"
],
[
"gans",
"gan",
"generative",
"image",
"generation",
"generator",
"images",
"adversarial",
"generate",
"discriminator"
],
[
"depth",
"estimation",
"object",
"scene",
"point",
"pose",
"objects",
"3d",
"shape",
"cloud"
],
[
"rl",
"policy",
"environment",
"policies",
"reinforcement",
"reward",
"imitation",
"environments",
"control",
"action"
],
[
"sparsity",
"structured",
"feature",
"dictionary",
"selection",
"regression",
"linear",
"sparse",
"norm",
"problems"
],
[
"gnns",
"graph",
"graphs",
"laplacian",
"gnn",
"sensor",
"spectral",
"geometric",
"node",
"networks"
],
[
"differential",
"private",
"complexity",
"sample",
"privacy",
"differentially",
"guarantees",
"margin",
"wise",
"queries"
],
[
"uncertainty",
"variational",
"estimation",
"distribution",
"approximate",
"weights",
"dropout",
"bayesian",
"posterior",
"gaussian"
],
[
"effects",
"side",
"protein",
"sequences",
"cell",
"cancer",
"interactions",
"net",
"prediction",
"expression"
],
[
"vae",
"likelihood",
"inference",
"autoencoder",
"variational",
"latent",
"autoencoders",
"variables",
"generative",
"flows"
],
[
"word",
"documents",
"language",
"text",
"words",
"nlp",
"document",
"categories",
"style",
"corpus"
],
[
"resolution",
"images",
"channel",
"image",
"reconstruction",
"phase",
"imaging",
"transmission",
"source",
"optical"
],
[
"machine",
"protocol",
"classical",
"computing",
"systems",
"queries",
"quantum",
"ground",
"hilbert",
"noise"
],
[
"cooperative",
"communication",
"agent",
"games",
"multi",
"agents",
"reinforcement",
"game",
"optimal",
"policy"
],
[
"differential",
"dynamics",
"equation",
"equations",
"solution",
"physics",
"systems",
"physical",
"informed",
"solving"
],
[
"sensor",
"driving",
"traffic",
"autonomous",
"control",
"vehicle",
"vehicles",
"simulation",
"behavior",
"motion"
],
[
"stochastic",
"convex",
"proximal",
"gradient",
"sgd",
"optimization",
"problems",
"descent",
"convergence",
"rate"
]
] | 8.07573 | all-MiniLM-L6-v2 | 0.76 | -0.016994 | 0.148292 | 0.891849 |
ArXiv ML Papers | KeyNMF | 43 | 10 | [
[
"learned",
"training",
"representations",
"knowledge",
"learning",
"machine",
"tasks",
"learn",
"supervised",
"deep"
],
[
"neural",
"network",
"deep",
"training",
"convolutional",
"networks",
"trained",
"layers",
"layer",
"cnns"
],
[
"attack",
"robustness",
"adversarial",
"attacks",
"robust",
"defense",
"security",
"examples",
"training",
"detection"
],
[
"accuracy",
"classifier",
"classifiers",
"features",
"detection",
"classification",
"class",
"supervised",
"feature",
"classes"
],
[
"models",
"data",
"modeling",
"prediction",
"inference",
"model",
"predictions",
"bayesian",
"forecasting",
"predictive"
],
[
"algorithm",
"algorithms",
"gradient",
"complexity",
"optimization",
"descent",
"sampling",
"stochastic",
"convex",
"optimal"
],
[
"accuracy",
"dataset",
"training",
"trained",
"datasets",
"features",
"labeled",
"data",
"feature",
"supervised"
],
[
"reward",
"learning",
"reinforcement",
"agents",
"agent",
"policy",
"tasks",
"control",
"policies",
"rl"
],
[
"image",
"generative",
"gans",
"adversarial",
"generation",
"gan",
"images",
"autoencoder",
"generate",
"variational"
],
[
"node",
"networks",
"network",
"nodes",
"graph",
"graphs",
"embedding",
"embeddings",
"representations",
"clustering"
]
] | 315.248808 | all-MiniLM-L6-v2 | 0.82 | 0.080636 | 0.173903 | 0.772573 |