用流匹配提升神经过程，实现高效可控的条件采样。
Flow Matching Neural Processes
- 基于流匹配构建新神经过程模型，直接学习数据分布
- 在1D、2D及真实气象数据上超越现有最先进方法
- 通过ODE求解步数灵活调节精度与速度，适合实时应用
神经过程（Neural Processes, NPs）是一类可直接从数据中学习随机过程的模型，适用于推断、采样和条件采样。本文提出一种基于流匹配（Flow Matching）的新NPs模型，遵循标准NP训练框架，可对任意数据点提供条件分布的摊销预测。相比以往方法，本模型实现简单，无需额外条件化机制即可使用常微分方程（ODE）求解器进行采样，且可通过调整ODE求解步数控制精度与运行时间的权衡。在合成1D高斯过程数据、2D图像及真实世界气象数据等多个基准测试中，该模型均优于现有最先进神经过程方法。
原文摘要 · Abstract (English)
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 based on flow matching, a generative modeling paradigm that has demonstrated strong performance on various data modalities. Following the NP training framework, the model provides amortized predictions of conditional distributions over any arbitrary points in the data. Compared to previous NP models, our model is simple to implement and can be used to sample from conditional distributions using an ODE solver, without requiring auxiliary conditioning methods. In addition, the model provides a controllable tradeoff between accuracy and running time via the number of steps in the ODE solver. We show that our model outperforms previous state-of-the-art neural process methods on various benchmarks including synthetic 1D Gaussian processes data, 2D images, and real-world weather data.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。