Study on Data Filling Based on Global-attributes Attention Neural Process Model

Abstract

The attention neural process(ANP) model which adopts the method of generative model,takes any number context points of the sample as input,and outputs the distribution function of the entire sample,so as to approximate the function of Gaussian process regression(GPR) to realize the data fullfilling task.In reality,many scenes or datasets containe the attributes or labels data which are critical for generating the missing data.However,the ANP ignores full use of them.Inspired by CVAE model which control sample generation with lable as condition,this paper proposes global attribute attentional neural process(GANP),which embeds sample attributes or labels into ANP network to make the model generate samples more accurately,especially when the number of input context points are scarce.In detail,the sample attributes are embedded into the encoder network,so that the latent variables contain the sample attribute information.At the same time,the sample attributes are added as features in the decoder network to help generate more accurate samples.Finally,experimental results prove the superiority of GANP in both qualitative and quantitative,and it also reveals that GANP expands the application of NP families which can solve the Gaus-sian process regression problem more flexibly,quickly and accurately.

Keywords

• neural process|cross attention|variational inference|gaussian process|global attribute