In this work, we propose a novel and simple framework to achieve equivariance for point cloud analysis based on the message passing (graph neural network) scheme.
In this work, we propose the Directed Weight Neural Network for better capturing geometric relations among different amino acids.
Assuming different forms of the underlying potential energy function, we can not only reinterpret and unify many of the existing models but also derive new variants of SE(3)-equivariant neural networks in a principled manner.
Automatically reasoning about future human behaviors is a difficult problem but has significant practical applications to assistive systems.
Deep neural networks have been remarkable successful in various AI tasks but often cast high computation and energy cost for energy-constrained applications such as mobile sensing.