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stat.ML2026
Adaptive multi-resolution Gaussian processes: Scalable exact inference with naturally data-sparse covariance matrices
Yanchuang Cao, Jun Liu, Tengchao Yu +1
Gaussian processes constitute a cornerstone of probabilistic machine learning, yet scaling them to large datasets typically forces a trade-off between computational efficiency and…
physics.comp-ph2026
pcNN-SN: a physics-enhanced predictor-corrector neural network algorithm coupled with the discrete ordinates method for neutron transport equations
Cong Liu, Junxia Wei, Shuai Wang +1
Super-resolution machine learning methods hold promise for addressing the high computational costs associated with high-dimensional features in traditional transport simulations. T…