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stat.ML2026
Efficient Approximation to Analytic and functions by Height-Augmented ReLU Networks
ZeYu Li, FengLei Fan, TieYong Zeng
This work addresses two fundamental limitations in neural network approximation theory. We demonstrate that a three-dimensional network architecture enables a significantly more ef…
cs.LG2026
Neural Network Approximation: A View from Polytope Decomposition
ZeYu Li, ShiJun Zhang, TieYong Zeng +1
Universal approximation theory offers a foundational framework to verify neural network expressiveness, enabling principled utilization in real-world applications. However, most ex…