3 accepted papers
Many real-world datasets are represented as 3D point clouds -- yet they often lack a predefined reference frame, posing a challenge for machine learning or general data analysis. Traditional methods for determining reference frames and normalizing 3D point clouds often struggle with specific inputs,…
Theoretical and empirical comparisons have been made to assess the expressive power and performance of invariant and equivariant GNNs. However, there is currently no theoretical result comparing the expressive power of $k$-hop invariant GNNs and equivariant GNNs. Additionally, little is understood a…
Implicit graph neural networks (IGNNs) -- that solve a fixed-point equilibrium equation using Picard iteration for representation learning -- have shown remarkable performance in learning long-range dependencies (LRD) in the underlying graphs. However, IGNNs suffer from several issues, including 1)…