5 citations · 6 across the 5 of their papers we have counts for
5 papers
On Equivariant Model Selection through the Lens of Uncertainty
Putri A. van der Linden, Alexander Timans, Dharmesh Tailor +1
Equivariant models leverage prior knowledge on symmetries to improve predictive performance, but misspecified architectural constraints can harm it instead. While work has explored…
CP: Leveraging Geometry for Conformal Prediction via Canonicalization
Putri A. van der Linden, Alexander Timans, Erik J. Bekkers
We study the problem of conformal prediction (CP) under geometric data shifts, where data samples are susceptible to transformations such as rotations or flips. While CP endows pre…
Learning Symmetries via Weight-Sharing with Doubly Stochastic Tensors
Putri A. van der Linden, Alejandro García-Castellanos, Sharvaree Vadgama +2
Group equivariance has emerged as a valuable inductive bias in deep learning, enhancing generalization, data efficiency, and robustness. Classically, group equivariant methods requ…
Fast, Expressive SE Equivariant Networks through Weight-Sharing in Position-Orientation Space
Erik J Bekkers, Sharvaree Vadgama, Rob D Hesselink +2
Based on the theory of homogeneous spaces we derive geometrically optimal edge attributes to be used within the flexible message-passing framework. We formalize the notion of weigh…
Learned Gridification for Efficient Point Cloud Processing
Putri A. van der Linden, David W. Romero, Erik J. Bekkers
Neural operations that rely on neighborhood information are much more expensive when deployed on point clouds than on grid data due to the irregular distances between points in a p…