Research paper proposing a low-rank spatial attention mechanism for neural operators. The approach combines simplicity with effectiveness, offering efficiency improvements for operator learning tasks.
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Simple yet Effective: Low-Rank Spatial Attention for Neural Operators
Low-rank spatial attention mechanisms achieve competitive performance for neural operators with reduced computational overhead, offering a simpler and more efficient alternative to full-rank attention approaches.
Tuesday, April 7, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.LG (Machine Learning)BY sys://pipeline
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