Flow Matching Neural Processes

Abstract

Neural processes (NPs) are a class of models that learn stochastic processes directly from data and can be used for inference, sampling, and conditional sampling. We introduce a new NP model, which is based on flow matching, a generative modeling paradigm that has demonstrated strong performance on various data modalities. Our model is simple to implement, is efficient in training and evaluation, and outperforms previous state-of-the-art methods on various benchmarks including synthetic 1D Gaussian processes data, 2D images, and real-world weather data.