Conditional Neural Processes~(CNPs) bridge neural networks with probabilistic
inference to approximate functions of Stochastic Processes under meta-learning
settings. Given a batch of non-{\it i.i.d} function instantiations, CNPs are
jointly optimized for in-instantiation observation prediction and
cross-instantiation meta-representation adaptation within a generative
reconstruction pipeline. There can be a challenge in tying together such two
targets when the distribution of function observations scales to
high-dimensional and noisy spaces. Instead, noise contrastive estimation might
be able to provide more robust representations by learning distributional
matching objectives to combat such inherent limitation of generative models. In
light of this, we propose to equip CNPs by 1) aligning prediction with encoded
ground-truth observation, and 2) decoupling meta-representation adaptation from
generative reconstruction. Specifically, two auxiliary contrastive branches are
set up hierarchically, namely in-instantiation temporal contrastive
learning~({\tt TCL}) and cross-instantiation function contrastive
learning~({\tt FCL}), to facilitate local predictive alignment and global
function consistency, respectively. We empirically show that {\tt TCL} captures
high-level abstraction of observations, whereas {\tt FCL} helps identify
underlying functions, which in turn provides more efficient representations.
Our model outperforms other CNPs variants when evaluating function distribution
reconstruction and parameter identification across 1D, 2D and high-dimensional
time-series.

本研究针对 Stochastic Processes 中函数的近似，提出了一种基于 Conditional Neural Processes 的方法并引入噪声对抗学习来改善生成模型的一些固有限制。具体而言，本方法包含两个辅助的对比分支（TCL 和 FCL）同时优化预测和适应问题的表征。实验表明，本文提出的方法在高维时间序列的函数分布重建和参数识别方面优于其他 CNPs 变体模型。