Exchangeable Neural ODE for Set Modeling

Understand

Reasoning over an instance composed of a set of vectors, like a point cloud, requires that one accounts for intra-set dependent features among elements.

• However, since such instances are unordered, the elements' features should remain unchanged when the input's order is permuted.
• This property, permutation equivariance, is a challenging constraint for most neural architectures.
• While recent work has proposed global pooling and attention-based solutions, these may be limited in the way that intradependencies are captured in practice.