Neural Variational Dropout Processes
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866914107806973952 |
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| author | Jeon, Insu Park, Youngjin Kim, Gunhee |
| author_facet | Jeon, Insu Park, Youngjin Kim, Gunhee |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_19425 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neural Variational Dropout Processes Jeon, Insu Park, Youngjin Kim, Gunhee Machine Learning Artificial Intelligence 68T07 (Artificial neural networks), 62F15 (Bayesian inference) 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. |
| title | Neural Variational Dropout Processes |
| topic | Machine Learning Artificial Intelligence 68T07 (Artificial neural networks), 62F15 (Bayesian inference) |
| url | https://arxiv.org/abs/2510.19425 |