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cs.LG2026
Projected Neural Additive Models as Universal Approximators
Nhon N. Phan, Qiang Du, John D. Clayton +1
Neural additive models (NAMs) offer interpretability but lack expressivity due to their rigid additive structure, whereas multi-layer perceptrons (MLPs) achieve universal approxima…
cs.LG2022★ 2 cited
Design of experiments for the calibration of history-dependent models via deep reinforcement learning and an enhanced Kalman filter
Ruben Villarreal, Nikolaos N. Vlassis, Nhon N. Phan +5
Experimental data is costly to obtain, which makes it difficult to calibrate complex models. For many models an experimental design that produces the best calibration given a limit…