1 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2024★ 1 cited
On the hardness of learning under symmetries
Bobak T. Kiani, Thien Le, Hannah Lawrence +2
We study the problem of learning equivariant neural networks via gradient descent. The incorporation of known symmetries ("equivariance") into neural nets has empirically improved…
cs.LG2023
Limits, approximation and size transferability for GNNs on sparse graphs via graphops
Thien Le, Stefanie Jegelka
Can graph neural networks generalize to graphs that are different from the graphs they were trained on, e.g., in size? In this work, we study this question from a theoretical persp…
cs.LG2022★ 1 cited
Training invariances and the low-rank phenomenon: beyond linear networks
Thien Le, Stefanie Jegelka
The implicit bias induced by the training of neural networks has become a topic of rigorous study. In the limit of gradient flow and gradient descent with appropriate step size, it…