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：神经过程是一类融合神经网络灵活性来参数化随机过程的概率模型家族。尽管它们在回归问题中能提供标定良好的预测并快速适应新任务，但通常用于表示预测似然函数的高斯假设无法捕捉多模态等更复杂的分布。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，它利用分位数回归的优良特性来建模任意形式的分布。通过引入扩展的分位数回归方法（模型学习聚焦于估计信息量丰富的分位数），我们能够进一步提升采样效率与预测精度。在真实与合成数据集上的实验表明，与基线方法相比，我们在预测性能上取得了显著提升，并能更准确地建模异质分布特性（如多模态性）。