korrelationsformer

Korrelationsformer is a family of neural network architectures inspired by the transformer, designed to explicitly model correlations among features and across time. Unlike standard transformers that rely primarily on token similarity, it introduces a correlation module that computes pairwise and higher-order correlations from input representations and uses these to guide information flow.

In typical implementations, the architecture includes an input embedding layer, a correlation computation block, a correlation-aware attention or gating mechanism, and a final prediction head. The correlation block may construct feature-feature and instance-instance matrices via differentiable statistics or learned functions, and these matrices reweight or mix representations in subsequent layers.

Training is performed end-to-end using supervised objectives for classification, regression, or forecasting, often with auxiliary losses to stabilize estimated correlations or promote sparsity. The method is particularly suited to multivariate time-series, structured tabular data, and relational domains where dependencies among variables carry predictive signal.

Benefits of korrelationsformer include improved performance when dependencies are strong, enhanced interpretability through correlation maps, and robustness to certain noise patterns. Drawbacks include higher computational overhead from correlation estimation, sensitivity to non-stationary correlations, and potential overfitting on small datasets.

The concept has appeared in recent academic and industry discussions as a variant of transformer models that foreground correlation structure. It remains an area of active research with ongoing work on scalable estimators, theoretical guarantees, and practical guidelines for training and deployment.