  Vector-valued neural learning has emerged as a promising direction in deep
learning recently. Traditionally, training data for neural networks (NNs) are
formulated as a vector of scalars; however, its performance may not be optimal
since associations among adjacent scalars are not modeled. In this paper, we
propose a new vector neural architecture called the Arbitrary BIlinear Product
Neural Network (ABIPNN), which processes information as vectors in each neuron,
and the feedforward projections are defined using arbitrary bilinear products.
Such bilinear products can include circular convolution, seven-dimensional
vector product, skew circular convolution, reversed- time circular convolution,
or other new products not seen in previous work. As a proof-of-concept, we
apply our proposed network to multispectral image denoising and singing voice
sepa- ration. Experimental results show that ABIPNN gains substantial
improvements when compared to conventional NNs, suggesting that associations
are learned during training.
