activity
20162024
most citedFactor Graph Neural Networks

18 citations · 24 across the 10 of their papers we have counts for

collaborators

10 papers

cs.LG2024★ 1 cited

Differentiable Cluster Graph Neural Network

Yanfei Dong, Mohammed Haroon Dupty, Lambert Deng +3

Graph Neural Networks often struggle with long-range information propagation and in the presence of heterophilous neighborhoods. We address both challenges with a unified framework…

cs.CV2024★ 1 cited

Constrained Layout Generation with Factor Graphs

Mohammed Haroon Dupty, Yanfei Dong, Sicong Leng +4

This paper addresses the challenge of object-centric layout generation under spatial constraints, seen in multiple domains including floorplan design process. The design process ty…

cs.LG2024★ 2 cited

PF-GNN: Differentiable particle filtering based approximation of universal graph representations

Mohammed Haroon Dupty, Yanfei Dong, Wee Sun Lee

Message passing Graph Neural Networks (GNNs) are known to be limited in expressive power by the 1-WL color-refinement test for graph isomorphism. Other more expressive models eithe…

cs.CL2023

Tell2Design: A Dataset for Language-Guided Floor Plan Generation

Sicong Leng, Yang Zhou, Mohammed Haroon Dupty +3

We consider the task of generating designs directly from natural language descriptions, and consider floor plan generation as the initial research area. Language conditional genera…

cs.LG2023★ 18 cited

Factor Graph Neural Networks

Zhen Zhang, Mohammed Haroon Dupty, Fan Wu +2

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 rea…

cs.LG2023

Implicit Graph Neural Diffusion Networks: Convergence, Generalization, and Over-Smoothing

Guoji Fu, Mohammed Haroon Dupty, Yanfei Dong +1

Implicit Graph Neural Networks (GNNs) have achieved significant success in addressing graph learning problems recently. However, poorly designed implicit GNN layers may have limite…

cs.LG2022

Graph Representation Learning with Individualization and Refinement

Mohammed Haroon Dupty, Wee Sun Lee

Graph Neural Networks (GNNs) have emerged as prominent models for representation learning on graph structured data. GNNs follow an approach of message passing analogous to 1-dimens…

cs.LG2020★ 2 cited

Neuralizing Efficient Higher-order Belief Propagation

Mohammed Haroon Dupty, Wee Sun Lee

Graph neural network models have been extensively used to learn node representations for graph structured data in an end-to-end setting. These models often rely on localized first…

cs.CV2019

Visual Relationship Detection with Low Rank Non-Negative Tensor Decomposition

Mohammed Haroon Dupty, Zhen Zhang, Wee Sun Lee

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, obje…

cs.DS2016

Scheduling Under Power and Energy Constraints

Mohammed Haroon Dupty, Pragati Agrawal, Shrisha Rao

Given a system model where machines have distinct speeds and power ratings but are otherwise compatible, we consider various problems of scheduling under resource constraints on th…