Active set algorithms enable automated data-driven sparse basis selection in ACE MLIPs, producing models with improved efficiency, generalization accuracy, and interpretability on benchmark datasets.
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
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Iterative DFT/IDFT algorithms with sparsification converge on stable real-domain sparsity patterns and show utility for denoising periodic spike signals in simulations.
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Scalable Data-Driven Basis Selection for Linear Machine Learning Interatomic Potentials
Active set algorithms enable automated data-driven sparse basis selection in ACE MLIPs, producing models with improved efficiency, generalization accuracy, and interpretability on benchmark datasets.