Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Dupty, Mohammed Haroon"'
Autor:
Goh, Yong Liang, Cao, Zhiguang, Ma, Yining, Dong, Yanfei, Dupty, Mohammed Haroon, Lee, Wee Sun
Existing neural constructive solvers for routing problems have predominantly employed transformer architectures, conceptualizing the route construction as a set-to-sequence learning task. However, their efficacy has primarily been demonstrated on ent
Externí odkaz:
http://arxiv.org/abs/2408.03585
Autor:
Dong, Yanfei, Dupty, Mohammed Haroon, Deng, Lambert, Liu, Zhuanghua, Goh, Yong Liang, Lee, Wee Sun
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 that incorporates a clustering inductive bias into the message passin
Externí odkaz:
http://arxiv.org/abs/2405.16185
Autor:
Dupty, Mohammed Haroon, Dong, Yanfei, Leng, Sicong, Fu, Guoji, Goh, Yong Liang, Lu, Wei, Lee, Wee Sun
This paper addresses the challenge of object-centric layout generation under spatial constraints, seen in multiple domains including floorplan design process. The design process typically involves specifying a set of spatial constraints that include
Externí odkaz:
http://arxiv.org/abs/2404.00385
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 either are computationally expensive or need preprocessing to extract struc
Externí odkaz:
http://arxiv.org/abs/2401.17752
Implicit Graph Neural Networks (GNNs) have achieved significant success in addressing graph learning problems recently. However, poorly designed implicit GNN layers may have limited adaptability to learn graph metrics, experience over-smoothing issue
Externí odkaz:
http://arxiv.org/abs/2308.03306
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. They have resemblance to Probabilistic Graphical
Externí odkaz:
http://arxiv.org/abs/2308.00887
Autor:
Dupty, Mohammed Haroon, Lee, Wee Sun
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-dimensional Weisfeiler Lehman (1-WL) test for graph isomorphism and conseque
Externí odkaz:
http://arxiv.org/abs/2203.09141
Autor:
Dupty, Mohammed Haroon, Lee, Wee Sun
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 order approximations of spectral graph convolutions and hence are unab
Externí odkaz:
http://arxiv.org/abs/2010.09283
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). We observe that given a pair of bounding box proposals, objects o
Externí odkaz:
http://arxiv.org/abs/1911.09895
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 the system which place the restriction that not all machines can be run
Externí odkaz:
http://arxiv.org/abs/1609.07354