Neural Network Approximation Theory

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  1. Exact ReLU realization of tensor-product refinement iterates

    May 5, 2026Tsogtgerel GantumurReLU Neural NetworksNeural Network Approximation Theory

  2. Simultaneous CNN Approximation on Manifolds with Applications to Boundary Value Problems

    May 5, 2026Hanfei Zhou, Lei ShiPDE SolvingNeural Network Approximation Theory

  3. Parametrizing Convex Sets Using Sublinear Neural Networks

    May 5, 2026Eloi MartinetNeural Network Approximation Theory

  4. ParaRNN: An Interpretable and Parallelizable Recurrent Neural Network for Time-Dependent Data

    May 4, 2026Yuxi Cai, Lan Li, Feiqing Huang +1Interpretable MLRecurrent Neural Networks

  5. Floating-Point Networks with Automatic Differentiation Can Represent Almost All Floating-Point Functions and Their Gradients

    May 3, 2026Sejun Park, Yeachan Park, Geonho HwangAutomatic DifferentiationNeural Network Approximation Theory

  6. Exact Loop Controllers for ReLU Realization of Homogeneous Curve Refinements

    May 3, 2026Boldsaikhan Bolorkhuu, Tsogtgerel GantumurReLU Neural NetworksNeural Network Approximation Theory

  7. Hyper Input Convex Neural Networks for Shape Constrained Learning and Optimal Transport

    Apr 29, 2026Shayan Hundrieser, Insung Kong, Johannes Schmidt-HieberInput Convex Neural NetworksNeural Network Approximation Theory

  8. Transformer Approximations from ReLUs

    Apr 27, 2026Jerry Yao-Chieh Hu, Mingcheng Lu, Yi-Chen Lee +1Softmax AttentionNeural Network Approximation Theory

  9. Progressive Approximation in Deep Residual Networks: Theory and Validation

    Apr 27, 2026Wei Wang, Xiao-Yong Wei, Qing LiEfficient Neural Network InferenceResidual Learning

  10. SOC-ICNN: From Polyhedral to Conic Geometry for Learning Convex Surrogate Functions

    Apr 24, 2026Kang Liu, Jianchen Hu, Wei PengNeural Surrogate ModelingInput Convex Neural Networks

  11. Scale-Parameter Selection in Gaussian Kolmogorov-Arnold Networks

    Apr 23, 2026Amir Noorizadegan, Sifan WangKolmogorov-Arnold NetworksNeural Network Approximation Theory

  12. Layer-wise Geometric Approximation Rates for Deep Networks

    Apr 22, 2026Shijun Zhang, Zuowei Shen, Yuesheng XuNeural Network Approximation TheoryNeural Network Expressivity

  13. Closing the Theory-Practice Gap in Spiking Transformers via Effective Dimension

    Apr 17, 2026Dongxin Guo, Jikun Wu, Siu Ming YiuSpiking TransformersTransformer Attention

  14. Implicit Neural Representations: A Signal Processing Perspective

    Apr 16, 2026Dhananjaya Jayasundara, Vishal M. PatelImplicit Neural RepresentationsSpectral Bias

  15. Rational Neural Networks have Expressivity Advantages

    Feb 12, 2026Maosen Tang, Alex TownsendNeural Network Activation FunctionsNeural Network Approximation Theory

  16. Clifford Kolmogorov-Arnold Networks

    Feb 5, 2026Matthias Wolff, Francesco Alesiani, Christof Duhme +1Kolmogorov-Arnold NetworksNeural Network Approximation Theory

  17. Geometry-Preserving Neural Architectures on Manifolds with Boundary

    Feb 3, 2026Karthik Elamvazhuthi, Shiba Biswal, Kian Rosenblum +4Geometric Representation LearningNeural Network Approximation Theory

  18. Agile Reinforcement Learning through Separable Neural Architecture and Applications

    Jan 30, 2026Rajib Mostakim, Reza T. Batley, Sourav SahaRL ControlNeural Network Approximation Theory

  19. Linearized subspace refinement framework to expose hidden accuracy in trained neural networks

    Jan 20, 2026Wenbo Cao, Weiwei ZhangNeural Network OptimizationNeural Network Approximation Theory

  20. On the Dimension-Free Approximation of Deep Neural Networks for Symmetric Korobov Functions

    Nov 16, 2025Yulong Lu, Tong Mao, Jinchao Xu +1Neural Network GeneralizationNeural Network Approximation Theory

  21. DFNN: A Deep Fréchet Neural Network Framework for Learning Metric-Space-Valued Responses

    Oct 20, 2025Kyum Kim, Yaqing Chen, Paromita DubeyNeural Network GeneralizationNeural Network Approximation Theory

  22. Configuration-Dependent Lower Bounds for Approximation by Shallow ReLUk^k Networks on the Sphere

    Oct 5, 2025Tong Mao, Jinchao XuShallow Neural NetworksNeural Network Approximation Theory