Contrastive Conditional Neural Processes

• URL: http://arxiv.org/abs/2203.03978v1
• Date: Tue, 8 Mar 2022 10:08:45 GMT
• Title: Contrastive Conditional Neural Processes
• Authors: Zesheng Ye, Lina Yao
• Abstract summary: Conditional Neural Processes(CNPs) bridge neural networks with probabilistic inference to approximate functions of Processes under meta-learning settings. Two auxiliary contrastive branches are set up hierarchically, namely in-instantiation temporal contrastive learning(tt TCL) and cross-instantiation function contrastive learning(tt FCL) 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.
• Score: 45.70735205041254
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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