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cs.LG2026
Embedding Dimension Lower Bounds for Universality of Deep Sets and Janossy Pooling
Ali Syed, Aditya Nambiar, Jonathan W. Siegel
In many practical applications it is important to build symmetries into neural network architectures. Consider the important case of permutation symmetry on point clouds consisting…
cs.LG2026
Quantitative Approximation Rates for Group Equivariant Learning
Jonathan W. Siegel, Snir Hordan, Hannah Lawrence +2
The universal approximation theorem establishes that neural networks can approximate any continuous function on a compact set. Later works in approximation theory provide quantitat…
cs.LG2026
In-Context Multi-Operator Learning with DeepOSets
Shao-Ting Chiu, Aditya Nambiar, Ali Syed +2
An important application of neural networks to scientific computing has been the learning of non-linear operators. In this framework, a neural network is trained to fit a non-linea…