Neural processes are a family of probabilistic models that inherit the flexibility of neural networks to parameterize stochastic processes. Despite providing well-calibrated predictions, especially in regression problems, and quick adaptation to new tasks, the Gaussian assumption that is commonly used to represent the predictive likelihood fails to capture more complicated distributions such as multimodal ones. To overcome this limitation, we propose Conditional Quantile Neural Processes (CQNPs), a new member of the neural processes family, which exploits the attractive properties of quantile regression in modeling the distributions irrespective of their form. By introducing an extension of quantile regression where the model learns to focus on estimating informative quantiles, we show that the sampling efficiency and prediction accuracy can be further enhanced. Our experiments with real and synthetic datasets demonstrate substantial improvements in predictive performance compared to the baselines, and better modeling of heterogeneous distributions' characteristics such as multimodality.

翻译：神经过程是一类继承神经网络灵活性的概率模型家族，用于参数化随机过程。尽管在回归问题中能提供校准良好的预测并快速适应新任务，但常用作表征预测似然的高斯假设无法捕捉多峰分布等更复杂的分布形态。为突破这一局限，我们提出条件分位数神经过程（CQNPs）——神经过程家族的新成员，该模型利用分位数回归的优良性质对任意形式的分布进行建模。通过引入分位数回归的扩展形式，使模型学会聚焦于预测信息性分位数，我们进一步提升了采样效率与预测精度。在真实与合成数据集上的实验表明，与基线模型相比，本方法在预测性能上取得显著提升，并能更有效地刻画异质性分布的特征（如多峰性）。