Approximately equivariant graph networks
Graph neural networks (GNNs) are commonly described as being permutation equivariant
with respect to node relabeling in the graph. This symmetry of GNNs is often compared to …
with respect to node relabeling in the graph. This symmetry of GNNs is often compared to …
Invariant slot attention: Object discovery with slot-centric reference frames
O Biza, S Van Steenkiste, MSM Sajjadi… - ar**
Motivation Accurate prediction of acute dermal toxicity (ADT) is essential for the safe and
effective development of contact drugs. Currently, graph neural networks, a form of deep …
effective development of contact drugs. Currently, graph neural networks, a form of deep …
Newton–cotes graph neural networks: On the time evolution of dynamic systems
Abstract Reasoning system dynamics is one of the most important analytical approaches for
many scientific studies. With the initial state of a system as input, the recent graph neural …
many scientific studies. With the initial state of a system as input, the recent graph neural …
Equivariant graph neural operator for modeling 3d dynamics
Modeling the complex three-dimensional (3D) dynamics of relational systems is an
important problem in the natural sciences, with applications ranging from molecular …
important problem in the natural sciences, with applications ranging from molecular …
Relaxing continuous constraints of equivariant graph neural networks for broad physical dynamics learning
Incorporating Euclidean symmetries (eg rotation equivariance) as inductive biases into
graph neural networks has improved their generalization ability and data efficiency in …
graph neural networks has improved their generalization ability and data efficiency in …
Geometric trajectory diffusion models
Generative models have shown great promise in generating 3D geometric systems, which is
a fundamental problem in many natural science domains such as molecule and protein …
a fundamental problem in many natural science domains such as molecule and protein …
Dusego: Dual second-order equivariant graph ordinary differential equation
Graph Neural Networks (GNNs) with equivariant properties have achieved significant
success in modeling complex dynamic systems and molecular properties. However, their …
success in modeling complex dynamic systems and molecular properties. However, their …
Pose-Transformed Equivariant Network for 3D Point Trajectory Prediction
R Yu, J Sun - Proceedings of the IEEE/CVF Conference on …, 2024 - openaccess.thecvf.com
Predicting 3D point trajectory is a fundamental learning task which commonly should be
equivariant under Euclidean transformation eg SE (3). The existing equivariant models are …
equivariant under Euclidean transformation eg SE (3). The existing equivariant models are …