frequency-domain

DSTFView is a dual-input spatio-temporal-frequency multi-view framework for cloud-edge workload forecasting, jointly modeling closeness and period dependencies with an adaptive fusion mechanism to capture abrupt changes.

This paper introduces FourierQK, a method that applies FFT-based frequency-domain preprocessing to learned query and key projections in transformer attention, achieving significant validation loss reductions on character-level language modelling. The approach preserves the full attention score structure and demonstrates reproducible gains over standard dot-product attention.

This paper introduces FRESCO, an Echo State Network architecture operating entirely in the frequency domain to achieve O(N) complexity for dense recurrent updates, matching state-of-the-art performance on benchmarks while reducing computational costs.