2 citations · 4 across the 8 of their papers we have counts for
10 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…
No One-Size-Fits-All Neurons: Task-based Neurons for Artificial Neural Networks
Feng-Lei Fan, Meng Wang, Hang-Cheng Dong +2
In the past decade, many successful networks are on novel architectures, which almost exclusively use the same type of neurons. Recently, more and more deep learning studies have b…
An Efficient Algorithm for Vertex Enumeration of Arrangement
Zelin Dong, Fenglei Fan, Huan Xiong +1
This paper presents a state-of-the-art algorithm for the vertex enumeration problem of arrangements, which is based on the proposed new pivot rule, called the Zero rule. The Zero r…
VDIP-TGV: Blind Image Deconvolution via Variational Deep Image Prior Empowered by Total Generalized Variation
Tingting Wu, Zhiyan Du, Zhi Li +2
Recovering clear images from blurry ones with an unknown blur kernel is a challenging problem. Deep image prior (DIP) proposes to use the deep network as a regularizer for a single…
On Expressivity of Height in Neural Networks
Feng-Lei Fan, Ze-Yu Li, Huan Xiong +1
In this work, beyond width and depth, we augment a neural network with a new dimension called height by intra-linking neurons in the same layer to create an intra-layer hierarchy,…