Deep associative neural network for associative memory based on unsupervised representation learning

Jia Liu, Maoguo Gong, Haibo He

Abstract: Highlights•We propose a deep associative neural network for associative memory.•The network parameters are learnt by establishing a probability model.•The probability model aims to make the network best capture the data distributions.•The model is optimized by an improved contrastive divergence algorithm.•A novel sampling method is proposed to sample from the deep architecture based model.•The network can achieve several pattern recognition tasks with only one training.