Experimental performance of graph neural networks on random instances of max-cut

15 August 2019

Papers citing "Experimental performance of graph neural networks on random instances of max-cut"

20 / 20 papers shown

Title
An Unsupervised Learning Framework Combined with Heuristics for the Maximum Minimal Cut Problem Huaiyuan Liu Xianzhang Liu Donghua Yang Hongzhi Wang Yingchi Long Mengtong Ji Dongjing Miao Zhiyu Liang 15 0 0 16 Aug 2024
A Benchmark for Maximum Cut: Towards Standardization of the Evaluation of Learned Heuristics for Combinatorial Optimization Ankur Nath Alan Kuhnle CML 47 0 0 14 Jun 2024
A Unified Pre-training and Adaptation Framework for Combinatorial Optimization on Graphs Ruibin Zeng Minglong Lei Lingfeng Niu Lan Cheng AI4CE 19 0 0 16 Dec 2023
Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Methods C. Caramanis Dimitris Fotakis Alkis Kalavasis Vasilis Kontonis Christos Tzamos 11 5 0 08 Oct 2023
Controlling Continuous Relaxation for Combinatorial Optimization Yuma Ichikawa 22 4 0 29 Sep 2023
Monte Carlo Policy Gradient Method for Binary Optimization Cheng Chen Ruitao Chen Tian-cheng Li Ruicheng Ao Zaiwen Wen 13 3 0 03 Jul 2023
Towards fully covariant machine learning Soledad Villar D. Hogg Weichi Yao George A. Kevrekidis Bernhard Schölkopf AI4CE 30 10 0 31 Jan 2023
Unsupervised Learning for Combinatorial Optimization Needs Meta-Learning Hao Wang Pan Li 11 14 0 08 Jan 2023
Learning Feasibility of Factored Nonlinear Programs in Robotic Manipulation Planning Joaquim Ortiz de Haro Jung-Su Ha Danny Driess E. Karpas Marc Toussaint 19 2 0 22 Oct 2022
Annealed Training for Combinatorial Optimization on Graphs Haoran Sun E. Guha H. Dai 11 18 0 23 Jul 2022
Unsupervised Learning for Combinatorial Optimization with Principled Objective Relaxation Haoyu Wang Nan Wu Hang Yang Cong Hao Pan Li 19 29 0 13 Jul 2022
Graph neural network initialisation of quantum approximate optimisation Nishant Jain Brian Coyle E. Kashefi N. Kumar GNN AI4CE 17 48 0 04 Nov 2021
Combinatorial Optimization with Physics-Inspired Graph Neural Networks M. Schuetz J. K. Brubaker H. Katzgraber AI4CE 20 174 0 02 Jul 2021
Scalars are universal: Equivariant machine learning, structured like classical physics Soledad Villar D. Hogg Kate Storey-Fisher Weichi Yao Ben Blum-Smith PINN AI4CE 24 130 0 11 Jun 2021
Combinatorial optimization and reasoning with graph neural networks Quentin Cappart Didier Chételat Elias Boutros Khalil Andrea Lodi Christopher Morris Petar Velickovic AI4CE 30 346 0 18 Feb 2021
The Power of Graph Convolutional Networks to Distinguish Random Graph Models: Short Version A. Magner Mayank Baranwal Alfred Hero GNN 9 13 0 13 Feb 2020
Can Graph Neural Networks Count Substructures? Zhengdao Chen Lei Chen Soledad Villar Joan Bruna GNN 37 319 0 10 Feb 2020
Fundamental Limits of Deep Graph Convolutional Networks A. Magner Mayank Baranwal Alfred Hero GNN 12 7 0 28 Oct 2019
Graph Neural Networks for Maximum Constraint Satisfaction Jan Toenshoff Martin Ritzert Hinrikus Wolf Martin Grohe GNN NAI AI4CE 8 56 0 18 Sep 2019
Geometric deep learning: going beyond Euclidean data M. Bronstein Joan Bruna Yann LeCun Arthur Szlam P. Vandergheynst GNN 236 3,234 0 24 Nov 2016