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.

本文提出了一种新的神经过程成员 Conditional Quantile Neural Processes（CQNPs），采用量化回归来对复杂的分布进行建模，通过学习估计有信息量的分位数来增强抽样效率和预测准确性，并在实验数据集上与基线算法进行比较，展示了预测性能的显着提高。

自适应条件分位数神经过程

Adaptive Conditional Quantile Neural Processes

The recent proliferation of richly structured probabilistic models raises the
question of how to automatically determine an appropriate model for a dataset.
We investigate this question for a space of matrix decomposition models which
can express a variety of widely used models from unsupervised learning. To
enable model selection, we organize these models into a context-free grammar
which generates a wide variety of structures through the compositional
application of a few simple rules. We use our grammar to generically and
efficiently infer latent components and estimate predictive likelihood for
nearly 2500 structures using a small toolbox of reusable algorithms. Using a
greedy search over our grammar, we automatically choose the decomposition
structure from raw data by evaluating only a small fraction of all models. The
proposed method typically finds the correct structure for synthetic data and
backs off gracefully to simpler models under heavy noise. It learns sensible
structures for datasets as diverse as image patches, motion capture, 20
Questions, and U.S. Senate votes, all using exactly the same code.

通过生成上下文无关文法来组织矩阵分解模型以进行模型选择，使用小型可重用算法估计预测似然和推断潜在组件，通过贪心搜索自动选择分解结构。