Search Results for author:
Found 8 papers, 3 papers with code
Constrained Layout Generation with Factor Graphs
This paper addresses the challenge of object-centric layout generation under spatial constraints, seen in multiple domains including floorplan design process.
PF-GNN: Differentiable particle filtering based approximation of universal graph representations
Message passing Graph Neural Networks (GNNs) are known to be limited in expressive power by the 1-WL color-refinement test for graph isomorphism.
Tell2Design: A Dataset for Language-Guided Floor Plan Generation
We make multiple contributions to initiate research on this task.
Implicit Graph Neural Diffusion Networks: Convergence, Generalization, and Over-Smoothing
We show how implicit GNN layers can be viewed as the fixed-point equation of a Dirichlet energy minimization problem and give conditions under which it may suffer from over-smoothing during training (OST) and inference (OSI).
Factor Graph Neural Networks
In recent years, we have witnessed a surge of Graph Neural Networks (GNNs), most of which can learn powerful representations in an end-to-end fashion with great success in many real-world applications.
Graph Representation Learning with Individualization and Refinement
Individualization refers to artificially distinguishing a node in the graph and refinement is the propagation of this information to other nodes through message passing.
Neuralizing Efficient Higher-order Belief Propagation
In this paper, we propose to combine these approaches to learn better node and graph representations.
Visual Relationship Detection with Low Rank Non-Negative Tensor Decomposition
We address the problem of Visual Relationship Detection (VRD) which aims to describe the relationships between pairs of objects in the form of triplets of (subject, predicate, object).
