2 citations · 5 across the 14 of their papers we have counts for
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Longitudinal Flow Matching for Trajectory Modeling
Mohammad Mohaiminul Islam, Thijs P. Kuipers, Sharvaree Vadgama +4
Generative models for sequential data often struggle with sparsely sampled and high-dimensional trajectories, typically reducing the learning of dynamics to pairwise transitions. W…
Randomized HyperSteiner: A Stochastic Delaunay Triangulation Heuristic for the Hyperbolic Steiner Minimal Tree
Aniss Aiman Medbouhi, Alejandro García-Castellanos, Giovanni Luca Marchetti +3
We study the problem of constructing Steiner Minimal Trees (SMTs) in hyperbolic space. Exact SMT computation is NP-hard, and existing hyperbolic heuristics such as HyperSteiner are…
Platonic Transformers: A Solid Choice For Equivariance
Mohammad Mohaiminul Islam, Rishabh Anand, David R. Wessels +7
While widespread, Transformers lack inductive biases for geometric symmetries common in science and computer vision. Existing equivariant methods often sacrifice the efficiency and…
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…
Equivariant Eikonal Neural Networks: Grid-Free, Scalable Travel-Time Prediction on Homogeneous Spaces
Alejandro García-Castellanos, David R. Wessels, Nicky J. van den Berg +3
We introduce Equivariant Neural Eikonal Solvers, a novel framework that integrates Equivariant Neural Fields (ENFs) with Neural Eikonal Solvers. Our approach employs a single neura…
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…