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• W2949233439 abstract "Empirical evidence suggests that neural networks with ReLU activations generalize better with over-parameterization. However, there is currently no theoretical analysis that explains this observation. In this work, we provide theoretical and empirical evidence that, in certain cases, overparameterized convolutional networks generalize better than small networks because of an interplay between weight clustering and feature exploration at initialization. We demonstrate this theoretically for a 3-layer convolutional neural network with max-pooling, in a novel setting which extends the XOR problem. We show that this interplay implies that with overparamterization, gradient descent converges to global minima with better generalization performance compared to global minima of small networks. Empirically, we demonstrate these phenomena for a 3-layer convolutional neural network in the MNIST task." @default.
• W2949233439 created "2019-06-27" @default.
• W2949233439 creator A5029118855 @default.
• W2949233439 creator A5047817959 @default.
• W2949233439 date "2018-10-06" @default.
• W2949233439 modified "2023-09-27" @default.
• W2949233439 title "Why do Larger Models Generalize Better? A Theoretical Perspective via the XOR Problem" @default.
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• W2949233439 hasPublicationYear "2018" @default.
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