7 citations · 8 across the 3 of their papers we have counts for
4 papers
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…
Neural Networks for Programming Quantum Annealers
Samuel Bosch, Bobak Kiani, Rui Yang +2
Quantum machine learning has the potential to enable advances in artificial intelligence, such as solving problems intractable on classical computers. Some fundamental ideas behind…
Equivariant Polynomials for Graph Neural Networks
Omri Puny, Derek Lim, Bobak T. Kiani +2
Graph Neural Networks (GNN) are inherently limited in their expressive power. Recent seminal works (Xu et al., 2019; Morris et al., 2019b) introduced the Weisfeiler-Lehman (WL) hie…
The SSL Interplay: Augmentations, Inductive Bias, and Generalization
Vivien Cabannes, Bobak T. Kiani, Randall Balestriero +2
Self-supervised learning (SSL) has emerged as a powerful framework to learn representations from raw data without supervision. Yet in practice, engineers face issues such as instab…