Neural Variational Dropout Processes

• URL: http://arxiv.org/abs/2510.19425v1
• Date: Wed, 22 Oct 2025 09:45:44 GMT
• Title: Neural Variational Dropout Processes
• Authors: Insu Jeon, Youngjin Park, Gunhee Kim,
• Abstract summary: This paper presents a new Bayesian meta-learning approach called Neural Variational Dropout Processes (NVDPs)<n>NVDPs model the conditional posterior distribution based on a task-specific dropout.<n>Surprisingly, this enables the robust approximation of task-specific dropout rates.
• Score: 44.95055503650414
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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