Spectral Transformer Neural Processes

2026-05-10

AI Summary

Existing neural processes often suffer from underfitting and limited generalization when modeling strongly periodic or quasi-periodic time series, spatial, and image data. This work proposes a Spectral-Aware Transformer Neural Process that enhances periodicity modeling by estimating the spectral characteristics of context points via a spectral aggregator and compressing them into a task-adaptive spectral mixture model, which is then fused with time-domain embeddings. Innovatively, a spectral mixture kernel prior is incorporated into the neural process to reshape the geometry of latent-space similarity, ensuring that points distant in Euclidean space but close in periodic structure remain proximate—thereby overcoming the limitations of conventional translation-equivariant assumptions. Experiments demonstrate that the proposed method significantly outperforms current baselines on both synthetic and real-world time series and image datasets, achieving superior predictive performance for periodic data.

Abstract

Time series, spatial data, and images are natural applications of Neural Processes. However, when such data exhibit strong periodicity and quasi-periodicity, existing methods often suffer from underfitting and generalise poorly beyond the training distribution. In this work, we propose Spectral Transformer Neural Processes (STNPs), a frequency-aware extension of Transformer Neural Processes (TNPs). STNPs introduce a Spectral Aggregator that estimates an empirical context spectrum, compresses it into a spectral mixture, samples task-adaptive spectral features, and concatenates them with time-domain embeddings, thereby injecting a spectral-mixture-kernel bias into TNPs. This design reshapes the similarity geometry, allowing inputs that are distant in Euclidean space to remain close in an induced periodic manifold while enhancing time-frequency interactions. Extensive experiments on synthetic regression tasks, real-world time-series datasets, and an image dataset demonstrate that STNPs consistently improve predictive performance over existing baselines, extending Neural Processes beyond translation equivariance towards effective modelling of periodicity and quasi-periodicity.

Problem

• periodicity
• quasi-periodicity
• Neural Processes
• underfitting
• generalization

Innovation

• Spectral Transformer Neural Processes
• frequency-aware modeling
• spectral aggregator
• periodicity
• neural processes