Graphs have a superior ability to represent relational data, like chemical compounds,
proteins, and social networks. Hence, graph-level learning, which takes a set of graphs as …

Graph matching (GM) has been a long-standing combinatorial problem due to its NP-hard
nature. Recently (deep) learning-based approaches have shown their superiority over the …

Jointly matching multiple, non-rigidly deformed 3D shapes is a challenging, NP-hard
problem. A perfect matching is necessarily cycle-consistent: Following the pairwise point …

Robust image matching is a fundamental and long-standing open problem in computer
vision. Conventional wisdom has exploited redundancy to improve the robustness of image …

The problem of synchronization over a group G aims to estimate a collection of group
elements G 1⁎,…, G n⁎∈ G based on noisy observations of a subset of all pairwise ratios of …

Group synchronization aims to recover the group elements from their noisy pairwise
measurements. It has found many applications in community detection, clock …

This paper studies a shuffled linear regression problem. As a variant of ordinary linear
regression, it requires estimating not only the regression variable, but also permutational …

Multigraph matching is a recent variant of the graph matching problem. In this framework, the
optimization procedure considers several graphs and enforces the consistency of the …

Pose synchronization, which seeks to estimate consistent absolute poses among a
collection of objects from noisy relative poses estimated between pairs of objects in …

Potential energy surface (PES) is one of the fundamental concepts in the field of quantum
chemistry. An accurate PES provides us with an atomistic-scale insight into molecular …