arXiv Machine Learning
NObSP: Functional Decomposition of Neural Networks via Oblique Subspace Projections
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Revisiting Neural Processes via Fourier Transform and Volterra Series
arXiv:2606. 01172v1 Announce Type: new Abstract: Modeling unknown latent functions from finite, irregularly sampled measurements is a recurring challenge across science and engineering.
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Instance-wise Linearization of Neural Network for Model Interpretation
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arXiv Machine Learning
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Controlling for Omitted Variable Bias in Deep Neural Networks
The paper introduces a control‑variable framework for deep neural networks to mitigate omitted variable bias, particularly shortcut learning where covariates like demographics influence predictions. It refits the final layer of a pre‑trained network using cross‑fitting with ridge penalisation, orthogonalises covariate effects, and marginalises predictions over covariate distributions to achieve unbiased, interpretable results. Experiments on simulated images and neuroimaging data show consistent estimation of true effects and performance close to models trained on unconfounded data.
arXiv Machine Learning
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Feature Superposition in Neural Networks: From Theory to Practice
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In-Run Data Shapley for Adam Optimizer
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arXiv Machine Learning
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Improved Predictive Performance and Interpretability for Mesomorphic Neural Networks Using Local Fidelity Regularization
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