Hyungi Lee, Eunggu Yun, Giung Nam, Edwin Fong, Juho Lee
TL;DR本文提出了一种名为 Martingale Posterior Neural Process (MPNP) 的模型,将预测分布与神经网络隐含层结合,使用递归的贝叶斯后验方法来降低模型误差。实验表明,MPNP 在各种任务方面的表现要优于基准模型。
Abstract
A neural process (NP) estimates a stochastic process implicitly defined with neural networks given a stream of data, rather than pre-specifying priors already known, such as Gaussian processes. An ideal NP would learn everything from data without any inductive biases, but in practice,