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Chen, Cong (author), Batselier, K. (author), Ko, Ching Yun (author), Wong, Ngai (author)
A restricted Boltzmann machine (RBM) learns a probability distribution over its input samples and has numerous uses like dimensionality reduction, classification and generative modeling. Conventional RBMs accept vectorized data that dismiss potentially important structural information in the original tensor (multi-way) input. Matrix-variate...
conference paper 2019
document
Chen, Cong (author), Batselier, K. (author), Ko, Ching Yun (author), Wong, Ngai (author)
There has been growing interest in extending traditional vector-based machine learning techniques to their tensor forms. Support tensor machine (STM) and support Tucker machine (STuM) are two typical tensor generalization of the conventional support vector machine (SVM). However, the expressive power of STM is restrictive due to its rank-one...
conference paper 2019