Dimension Agnostic Neural Processes

• URL: http://arxiv.org/abs/2502.20661v1
• Date: Fri, 28 Feb 2025 02:40:59 GMT
• Title: Dimension Agnostic Neural Processes
• Authors: Hyungi Lee, Chaeyun Jang, Dongbok Lee, Juho Lee,
• Abstract summary: Meta-learning aims to train models that can generalize to new tasks with limited labeled data by extracting shared features across diverse task datasets.<n>We introduce Dimension A Neural Processes(DANP), which transforms input features into a fixed-dimensional space and learns a wider range of features that are generalizable across various tasks.<n>We empirically show that DANP outperforms previous NP variations, showcasing its effectiveness in overcoming the limitations of traditional NP models.
• Score: 17.417747846307996
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Meta-learning aims to train models that can generalize to new tasks with limited labeled data by extracting shared features across diverse task datasets. Additionally, it accounts for prediction uncertainty during both training and evaluation, a concept known as uncertainty-aware meta-learning. Neural Process(NP) is a well-known uncertainty-aware meta-learning method that constructs implicit stochastic processes using parametric neural networks, enabling rapid adaptation to new tasks. However, existing NP methods face challenges in accommodating diverse input dimensions and learned features, limiting their broad applicability across regression tasks. To address these limitations and advance the utility of NP models as general regressors, we introduce Dimension Agnostic Neural Processes(DANP). DANP incorporates Dimension Aggregator Block(DAB) to transform input features into a fixed-dimensional space, enhancing the model's ability to handle diverse datasets. Furthermore, leveraging the Transformer architecture and latent encoding layers, DANP learns a wider range of features that are generalizable across various tasks. Through comprehensive experimentation on various synthetic and practical regression tasks, we empirically show that DANP outperforms previous NP variations, showcasing its effectiveness in overcoming the limitations of traditional NP models and its potential for broader applicability in diverse regression scenarios.

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