Symmetries and equivariance are fundamental to the generalization of neural networks on
domains such as images, graphs, and point clouds. Existing work has primarily focused on a …

Symmetry-based neural networks often constrain the architecture in order to achieve
invariance or equivariance to a group of transformations. In this paper, we propose an …

Deep reinforcement learning (DRL)-based combinatorial optimization (CO) methods (ie,
DRL-NCO) have shown significant merit over the conventional CO solvers as DRL-NCO is …

In reinforcement learning (RL), representation learning is a proven tool for complex image-
based tasks, but is often overlooked for environments with low-level states, such as physical …

Biological intelligence is remarkable in its ability to produce complex behaviour in many
diverse situations through data efficient, generalisable and transferable skill acquisition. It is …

Equivariant neural networks enforce symmetry within the structure of their convolutional
layers, resulting in a substantial improvement in sample efficiency when learning an …

A trustworthy reinforcement learning algorithm should be competent in solving challenging
real-world problems, including {robustly} handling uncertainties, satisfying {safety} …

Motivated by the vast success of deep convolutional networks, there is a great interest in
generalizing convolutions to non-Euclidean manifolds. A major complication in comparison …

In planar grasp detection, the goal is to learn a function from an image of a scene onto a set
of feasible grasp poses in $\mathrm {SE}(2) $. In this paper, we recognize that the optimal …

Convolutions encode equivariance symmetries into neural networks leading to better
generalisation performance. However, symmetries provide fixed hard constraints on the …