universal approximation

Universal Approximation Under Constraints is Possible with Transformers ICLR 2022 Universality of Group Convolutional Neural Networks Based on Ridgelet Analysis on Groups NeurIPS 2022 Universal Joint Approximation of Manifolds and Densities by Simple Injective Flows ICML 2022 Your Transformer May Not be as Powerful as You Expect NeurIPS 2022 Elementary superexpressive activations ICML 2021 Minimum Width for Universal Approximation ICLR 2021 NEU: A Meta-Algorithm for Universal UAP-Invariant Feature Representation JMLR 2021 On the Representation Power of Set Pooling Networks NeurIPS 2021 On Universal Approximation and Error Bounds for Fourier Neural Operators JMLR 2021 Training deep residual networks for uniform approximation guarantees L4DC 2021 UNIPoint: Universally Approximating Point Processes Intensities AAAI 2021 Universal approximation power of deep residual neural networks via nonlinear control theory ICLR 2021 Universal Approximation Using Well-Conditioned Normalizing Flows NeurIPS 2021 Are Transformers universal approximators of sequence-to-sequence functions? ICLR 2020 Minimal Learning Machine: Theoretical Results and Clustering-Based Reference Point Selection JMLR 2020 Neural tangent kernels, transportation mappings, and universal approximation ICLR 2020 Non-Euclidean Universal Approximation NeurIPS 2020 O(n) Connections are Expressive Enough: Universal Approximability of Sparse Transformers NeurIPS 2020 Universal Approximation with Certified Networks ICLR 2020 Universal Approximation with Deep Narrow Networks COLT 2020 Efficient Deep Approximation of GMMs NeurIPS 2019 Universal Approximation Property and Equivalence of Stochastic Computing-Based Neural Networks and Binary Neural Networks AAAI 2019 Fourier Policy Gradients ICML 2018 The power of deeper networks for expressing natural functions ICLR 2018 Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank ICML 2017