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- W4289489535 abstract "Understanding theoretical properties of deep and locally connected nonlinear network, such as deep convolutional neural network (DCNN), is still a hard problem despite its empirical success. In this paper, we propose a novel theoretical framework for such networks with ReLU nonlinearity. The framework explicitly formulates data distribution, favors disentangled representations and is compatible with common regularization techniques such as Batch Norm. The framework is built upon teacher-student setting, by expanding the student forward/backward propagation onto the teacher's computational graph. The resulting model does not impose unrealistic assumptions (e.g., Gaussian inputs, independence of activation, etc). Our framework could help facilitate theoretical analysis of many practical issues, e.g. overfitting, generalization, disentangled representations in deep networks." @default.
- W4289489535 created "2022-08-02" @default.
- W4289489535 creator A5084821923 @default.
- W4289489535 date "2018-09-27" @default.
- W4289489535 modified "2023-10-18" @default.
- W4289489535 title "A theoretical framework for deep locally connected ReLU network" @default.
- W4289489535 doi "https://doi.org/10.48550/arxiv.1809.10829" @default.
- W4289489535 hasPublicationYear "2018" @default.
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