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：神经过程是一类概率模型，它继承了神经网络参数化随机过程的灵活性。尽管在回归问题中能提供良好校准的预测并能快速适应新任务，但通常用于表示预测似然的高斯假设无法捕捉更复杂的分布，例如多模态分布。为克服这一局限，我们提出条件分位数神经过程（CQNPs）——神经过程家族的新成员，它利用分位数回归在建模任意形式分布时的优良特性。通过引入一种扩展的分位数回归方法，使模型学会聚焦于估计信息性分位数，我们进一步提升了采样效率和预测精度。基于真实与合成数据集的实验表明，与基线方法相比，本模型在预测性能上有显著提升，并能更有效地建模异质性分布的特征（如多模态性）。