Neural Variational Dropout Processes

Insu Jeon; Youngjin Park; Gunhee Kim

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arXiv · 2510.19425

Neural Variational Dropout Processes

Insu Jeon·Youngjin Park·Gunhee Kim

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Abstract

Learning to infer the conditional posterior model is a key step for robust meta-learning. This paper presents a new Bayesian meta-learning approach called Neural Variational Dropout Processes (NVDPs). NVDPs model the conditional posterior distribution based on a task-specific dropout; a low-rank product of Bernoulli experts meta-model is utilized for a memory-efficient mapping of dropout rates from a few observed contexts. It allows for a quick reconfiguration of a globally learned and shared neural network for new tasks in multi-task few-shot learning. In addition, NVDPs utilize a novel prior conditioned on the whole task data to optimize the conditional \textit{dropout} posterior in the amortized variational inference. Surprisingly, this enables the robust approximation of task-specific dropout rates that can deal with a wide range of functional ambiguities and uncertainties. We compared the proposed method with other meta-learning approaches in the few-shot learning tasks such as 1D stochastic regression, image inpainting, and classification. The results show the excellent performance of NVDPs.

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BibTeX RIS

Insu Jeon, Youngjin Park, Gunhee Kim. 2025-10-22. Neural Variational Dropout Processes. https://arxiv.org/abs/2510.19425

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