The Expressive Power of Deep Neural Networks with Circulant Matrices
Recent results from linear algebra stating that any matrix can be decomposed into products of diagonal and circulant matrices has lead to the design of compact deep neural network architectures that perform well in practice. In this paper, we bridge the gap between these good empirical results and the theoretical approximation capabilities of Deep diagonal-circulant ReLU networks... (read more)
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Methods used in the Paper
| METHOD | TYPE | |
|---|---|---|
ReLU
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Activation Functions |