2016/10/31 by Chuan-Yung Tsai, Andrew Saxe, Tsai, Chuan-Yung +3 · 1 citation
Computer Science · Mathematics · Physics and Astronomy · #Advanced Neural Network Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #Neural and Evolutionary Computing (cs.NE) #Tensor decomposition and applications
paper · pdf · doi:10.48550/arxiv.1610.10087
openalex publication_date 2016/10/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present a novel neural network algorithm, the Tensor Switching (TS) network, which generalizes the Rectified Linear Unit (ReLU) nonlinearity to tensor-valued hidden units. The TS network copies its entire input vector to different locations in an expanded representation, with the location determined by its hidden unit activity. In this way, even a simple linear readout from the TS representation can implement a highly expressive deep-network-like function. The TS network hence avoids the vanishing gradient problem by construction, at the cost of larger representation size. We develop several methods to train the TS network, including equivalent kernels for infinitely wide and deep TS networks, a one-pass linear learning algorithm, and two backpropagation-inspired representation learning algorithms. Our experimental results demonstrate that the TS network is indeed more expressive and consistently learns faster than standard ReLU networks.