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Analysis and Design of Convolutional Networks via Hierarchical Tensor Decompositions
Expressive efficiency refers to the ability of a network architecture to realize functions that require an alternative architecture to be much larger.
Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design
This description enables us to carry a graph-theoretic analysis of a convolutional network, with which we demonstrate a direct control over the inductive bias of the deep network via its channel numbers, that are related to the min-cut in the underlying graph.
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