universal approximation

Rethinking the Capacity of Graph Neural Networks for Branching Strategy NeurIPS 2024 Simple Cycle Reservoirs are Universal JMLR 2024 Universal In-Context Approximation By Prompting Fully Recurrent Models NeurIPS 2024 Vocabulary for Universal Approximation: A Linguistic Perspective of Mapping Compositions ICML 2024 Achieve the Minimum Width of Neural Networks for Universal Approximation ICLR 2023 Learning on Manifolds: Universal Approximations Properties using Geometric Controllability Conditions for Neural ODEs L4DC 2023 Minimal Width for Universal Property of Deep RNN JMLR 2023 Minimum Width of Leaky-ReLU Neural Networks for Uniform Universal Approximation ICML 2023 Neural approximation of Wasserstein distance via a universal architecture for symmetric and factorwise group invariant functions NeurIPS 2023 Neural Oscillators are Universal NeurIPS 2023 On Solution Functions of Optimization: Universal Approximation and Covering Number Bounds AAAI 2023 Probabilistic Invariant Learning with Randomized Linear Classifiers NeurIPS 2023 Quantitative Universal Approximation Bounds for Deep Belief Networks ICML 2023 Quasi-Equivalence between Width and Depth of Neural Networks JMLR 2023 Recurrent Neural Networks and Universal Approximation of Bayesian Filters AISTATS 2023 Structure of universal formulas NeurIPS 2023 Theoretical Analysis of the Inductive Biases in Deep Convolutional Networks NeurIPS 2023 Universal Approximation Property of Invertible Neural Networks JMLR 2023 Universality and Limitations of Prompt Tuning NeurIPS 2023 Deep Network Approximation: Achieving Arbitrary Accuracy with Fixed Number of Neurons JMLR 2022 Learning Operators with Coupled Attention JMLR 2022 Power and limitations of single-qubit native quantum neural networks NeurIPS 2022 Universal Approximation in Dropout Neural Networks JMLR 2022 Universal Approximation of Functions on Sets JMLR 2022 Universal Approximation Theorems for Differentiable Geometric Deep Learning JMLR 2022