1 citations · 1 across the 5 of their papers we have counts for
5 papers
Hierarchical Equivariant Policy via Frame Transfer
Haibo Zhao, Dian Wang, Yizhe Zhu +6
Recent advances in hierarchical policy learning highlight the advantages of decomposing systems into high-level and low-level agents, enabling efficient long-horizon reasoning and…
Reducing the Sensitivity of Neural Physics Simulators to Mesh Topology via Pretraining
Nathan Vaska, Justin Goodwin, Robin Walters +1
Meshes are used to represent complex objects in high fidelity physics simulators across a variety of domains, such as radar sensing and aerodynamics. There is growing interest in u…
MatrixNet: Learning over symmetry groups using learned group representations
Lucas Laird, Circe Hsu, Asilata Bapat +1
Group theory has been used in machine learning to provide a theoretically grounded approach for incorporating known symmetry transformations in tasks from robotics to protein model…
Modeling Dynamics over Meshes with Gauge Equivariant Nonlinear Message Passing
Jung Yeon Park, Lawson L. S. Wong, Robin Walters
Data over non-Euclidean manifolds, often discretized as surface meshes, naturally arise in computer graphics and biological and physical systems. In particular, solutions to partia…
Disentangling Node Attributes from Graph Topology for Improved Generalizability in Link Prediction
Ayan Chatterjee, Robin Walters, Giulia Menichetti +1
Link prediction is a crucial task in graph machine learning with diverse applications. We explore the interplay between node attributes and graph topology and demonstrate that inco…