2 citations · 5 across the 10 of their papers we have counts for
3 papers · 1 filter
Unitary convolutions for learning on graphs and groups
Bobak T. Kiani, Lukas Fesser, Melanie Weber
Data with geometric structure is ubiquitous in machine learning often arising from fundamental symmetries in a domain, such as permutation-invariance in graphs and translation-inva…
Hardness of Learning Neural Networks under the Manifold Hypothesis
Bobak T. Kiani, Jason Wang, Melanie Weber
The manifold hypothesis presumes that high-dimensional data lies on or near a low-dimensional manifold. While the utility of encoding geometric structure has been demonstrated empi…
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…