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cs.LG2026
Progressive Approximation in Deep Residual Networks: Theory and Validation
Wei Wang, Xiao-Yong Wei, Qing Li
The Universal Approximation Theorem (UAT) guarantees universal function approximation but does not explain how residual models distribute approximation across layers. We reframe re…
cs.LG2025
TeamFormer: Shallow Parallel Transformers with Progressive Approximation
Wei Wang, Xiao-Yong Wei, Qing Li
The widespread 'deeper is better' philosophy has driven the creation of architectures like ResNet and Transformer, which achieve high performance by stacking numerous layers. Howev…
cs.LG2024
Dynamic Universal Approximation Theory: Foundations for Parallelism in Neural Networks
Wei Wang, Qing Li
Neural networks are increasingly evolving towards training large models with big data, a method that has demonstrated superior performance across many tasks. However, this approach…