4 papers
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…
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…
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…
Joint Self-Supervised Video Alignment and Action Segmentation
Ali Shah Ali, Syed Ahmed Mahmood, Mubin Saeed +3
We introduce a novel approach for simultaneous self-supervised video alignment and action segmentation based on a unified optimal transport framework. In particular, we first tackl…