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Understanding the mechanism of how convolutional neural networks learn features from image data is a fundamental problem in machine learning and computer vision.
Digital image processing
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Lets keep it simple, using simple architectures to outperform deeper and more complex architectures
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High-dimensional asymptotics of feature learning: How one gradient step improves the representation
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Neural networks can learn representations with gradient descent
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A theoretical analysis on feature learning in neural networks: Emergence from inputs and advantage over fixed features
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Kernel regression with infinite-width neural networks on millions of examples
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Linear neural network layers promote learning single-and multiple-index models
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