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Deep Generative Models: Complexity, Dimensionality, and Approximation
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Deep Generative Models: Complexity, Dimensionality, and Approximation
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Deep Generative Models: Complexity, Dimensionality, and Approximation
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Inferring Change Points in High-Dimensional Regression via Approximate Message Passing
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Inferring Change Points in High-Dimensional Regression via Approximate Message Passing
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Inferring Change Points in High-Dimensional Regression via Approximate Message Passing
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Deep Neural Networks are Adaptive to Function Regularity and Data Distribution in Approximation and Estimation
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Deep Neural Networks are Adaptive to Function Regularity and Data Distribution in Approximation and Estimation
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Detecting Generative Model Inversion Attacks for Protecting Intellectual Property of Deep Neural Networks
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On the Approximation of Kernel functions
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Jackpot: Approximating Uncertainty Domains with Adversarial Manifolds
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Reinforcement Learning for Infinite-Dimensional Systems