Abstract:We propose a framework of tensorial neural networks (TNNs) extending existing linear layers on low-order tensors to multilinear operations on higher-order tensors. TNNs have three advantages over existing networks: First, TNNs naturally apply to higher-order data without flattening, which preserves their multi-dimensional structures. Second, compressing a pre-trained network into a TNN results in a model with similar expressive power but fewer parameters. Finally, TNNs interpret advanced compact designs of net… Show more
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