7 papers
On Explicit Super-Expressive Approximation for Neural Networks
Feng-Lei Fan, Ze-Yu Li, Chen-Yu Wang +1
In this work, we investigate the fixed-architecture neural network approximation with explicit parameter bounds and elementary activations. While prior work demonstrated super-expr…
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…
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…
Retinal Vessel Segmentation via Neuron Programming
Tingting Wu, Ruyi Min, Peixuan Song +3
The accurate segmentation of retinal blood vessels plays a crucial role in the early diagnosis and treatment of various ophthalmic diseases. Designing a network model for this task…
Hyper-Compression: Model Compression via Hyperfunction
Fenglei Fan, Juntong Fan, Dayang Wang +5
The rapid growth of large models' size has far outpaced that of computing resources. To bridge this gap, encouraged by the parsimonious relationship between genotype and phenotype…
Don't Fear Peculiar Activation Functions: EUAF and Beyond
Qianchao Wang, Shijun Zhang, Dong Zeng +4
In this paper, we propose a new super-expressive activation function called the Parametric Elementary Universal Activation Function (PEUAF). We demonstrate the effectiveness of PEU…