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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 |
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