Professor Hong Yan is going to talk about; 'Efficient Machine Learning Methods Based on Matrix and Tensor CUR Decomposition'

The training of large deep neural networks can be very expensive in term of long computing time, which requires costly equipment and high energy consumption. Vector, matrix and tensor multiplications are among the most time-consuming tasks. In this talk, a CUR decomposition-based method will be presented to reduce the computational complexity. Our basic strategy is to approximate a large low-rank matrix as a product of three much smaller matrices. Then multiplications with these matrices become very efficient. This can increase the speed of the self-attention operations in transformers significantly. Another reason for the long computing time in neural network training is that a large training dataset is needed. Our research group has recently studied a pattern matching method that requires only a small number of training samples. To achieve high accuracy and reliability, we use high-order pattern matching based on hypergraph and tensor models. A difficulty with this approach is that the high-order compatibility tensor is very large. Employing tensor CUR decomposition, we have tackled this problem and proposed an efficient pattern matching algorithm. Based on pattern matching, we have recently developed an intelligent indoor positioning and navigation system, which will be demonstrated in this talk.