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Neural network-based Combinatorial Optimization (CO) methods have shown promising results in solving various NP-complete (NPC) problems without relying on hand-crafted domain knowledge.
A method for solving traveling-salesman problems
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A new local search algorithm providing high quality solutions to vehicle routing problems
Paul Shaw · 1997
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Combinatorial optimization: algorithms and complexity
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SATLIB: An online resource for research on SAT
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Concorde TSP solver
David Applegate, Ribert Bixby, Vasek Chvatal, and William Cook · 2006
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Teofilo F Gonzalez · 2007
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Convolutional deep belief networks on cifar-10
Alex Krizhevsky and Geoff Hinton · 2010
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Fast local search for the maximum independent set problem
Diogo V Andrade, Mauricio GC Resende, and Renato F Werneck · 2012
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Neural combinatorial optimization with reinforcement learning
Irwan Bello, Hieu Pham, Quoc V Le, Mohammad Norouzi, and Samy Bengio · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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An extension of the Lin-Kernighan-Helsgaun TSP solver for constrained traveling salesman and vehicle routing problems
K. Helsgaun · 2017
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Learning combinatorial optimization algorithms over graphs
Elias Khalil, Hanjun Dai, Yuyu Zhang, Bistra Dilkina, and Le Song · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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An experimental study of neural networks for variable graphs
Xavier Bresson and Thomas Laurent · 2018
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Learning heuristics for the TSP by policy gradient
Michel Deudon, Pierre Cournut, Alexandre Lacoste, Yossiri Adulyasak, and Louis-Martin Rousseau · 2018
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Non-autoregressive neural machine translation
Jiatao Gu, James Bradbury, Caiming Xiong, Victor OK Li, and Richard Socher · 2018
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Gurobi optimizer reference manual, 2018
LLC Gurobi Optimization · 2018
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Combinatorial optimization with graph convolutional networks and guided tree search
Zhuwen Li, Qifeng Chen, and Vladlen Koltun · 2018
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Reinforcement learning for solving the vehicle routing problem
Mohammadreza Nazari, Afshin Oroojlooy, Lawrence Snyder, and Martin Takác · 2018
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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Learning to perform local rewriting for combinatorial optimization
Xinyun Chen and Yuandong Tian · 2019
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Neural large neighborhood search for the capacitated vehicle routing problem
André Hottung and Kevin Tierney · 2019
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Capgemini research institute, the last-mile delivery challenge, 2023
Capgemini Research Institute · 2019
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An efficient graph convolutional network technique for the travelling salesman problem
Chaitanya K Joshi, Thomas Laurent, and Xavier Bresson · 2019
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Buy 4 REINFORCE samples, get a baseline for free!
Wouter Kool, Herke van Hoof, and Max Welling · 2019
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Combinatorial optimization by graph pointer networks and hierarchical reinforcement learning
Qiang Ma, Suwen Ge, Danyang He, Darshan Thaker, and Iddo Drori · 2019
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A deep reinforcement learning algorithm using dynamic attention model for vehicle routing problems
Bo Peng, Jiahai Wang, and Zizhen Zhang · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Learning local search heuristics for boolean satisfiability
Emre Yolcu and Barnabás Póczos · 2019
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Learning what to defer for maximum independent sets
Learning to schedule job-shop problems: representation and policy learning using graph neural network and reinforcement learning
Junyoung Park, Jaehyeong Chun, Sang Hun Kim, Youngkook Kim, and Jinkyoo Park · 2021
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Learning improvement heuristics for solving routing problems.
Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang, and Andrew Lim · 2021
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Geodiff: A geometric diffusion model for molecular conformation generation
Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, and Jian Tang · 2021
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Learning generalizable models for vehicle routing problems via knowledge distillation
Jieyi Bi, Yining Ma, Jiahai Wang, Zhiguang Cao, Jinbiao Chen, Yuan Sun, and Yeow Meng Chee · 2022
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What’s wrong with deep learning in tree search for combinatorial optimization
Maximilian Böther, Otto Kißig, Martin Taraz, Sarel Cohen, Karen Seidel, and Tobias Friedrich · 2022
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Sungsoo Ahn, Younggyo Seo, and Jinwoo Shin · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Wavegrad: Estimating gradients for waveform generation
Nanxin Chen, Yu Zhang, Heiga Zen, Ron J Weiss, Mohammad Norouzi, and William Chan · 2020
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Learning 2-opt heuristics for the traveling salesman problem via deep reinforcement learning
Paulo R d O Costa, Jason Rhuggenaath, Yingqian Zhang, and Alp Akcay · 2020
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Learning 2-OPT heuristics for the traveling salesman problem via deep reinforcement learning
Paulo R de O da Costa, Jason Rhuggenaath, Yingqian Zhang, and Alp Akcay · 2020
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Learning to solve combinatorial optimization problems on real-world graphs in linear time
Iddo Drori, Anant Kharkar, William R Sickinger, Brandon Kates, Qiang Ma, Suwen Ge, Eden Dolev, Brenda Dietrich, David P Williamson, and Madeleine Udell · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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A continuous time framework for discrete denoising models
Andrew Campbell, Joe Benton, Valentin De Bortoli, Tom Rainforth, George Deligiannidis, and Arnaud Doucet · 2022
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Maskgit: Masked generative image transformer
Huiwen Chang, Han Zhang, Lu Jiang, Ce Liu, and William T Freeman · 2022
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Analog bits: Generating discrete data using diffusion models with self-conditioning
Ting Chen, Ruixiang Zhang, and Geoffrey Hinton · 2022
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Simulation-guided beam search for neural combinatorial optimization
Jinho Choo, Yeong-Dae Kwon, Jihoon Kim, Jeongwoo Jae, André Hottung, Kevin Tierney, and Youngjune Gwon · 2022
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Continuous diffusion for categorical data
Sander Dieleman, Laurent Sartran, Arman Roshannai, Nikolay Savinov, Yaroslav Ganin, Pierre H Richemond, Arnaud Doucet, Robin Strudel, Chris Dyer, Conor Durkan, et al · 2022
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Generalization of neural combinatorial solvers through the lens of adversarial robustness
Simon Geisler, Johanna Sommer, Jan Schuchardt, Aleksandar Bojchevski, and Stephan Günnemann · 2022
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Diffuseq: Sequence to sequence text generation with diffusion models
Shansan Gong, Mukai Li, Jiangtao Feng, Zhiyong Wu, and LingPeng Kong · 2022
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Diffusion models as plug-and-play priors
Alexandros Graikos, Nikolay Malkin, Nebojsa Jojic, and Dimitris Samaras · 2022
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Vector quantized diffusion model for text-to-image synthesis
Shuyang Gu, Dong Chen, Jianmin Bao, Fang Wen, Bo Zhang, Dongdong Chen, Lu Yuan, and Baining Guo · 2022
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Xiaochuang Han, Sachin Kumar, and Yulia Tsvetkov · 2022
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Diffusionbert: Improving generative masked language models with diffusion models
Zhengfu He, Tianxiang Sun, Kuanning Wang, Xuanjing Huang, and Xipeng Qiu · 2022
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Equivariant diffusion for molecule generation in 3d
Emiel Hoogeboom, Vıctor Garcia Satorras, Clément Vignac, and Max Welling · 2022
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Graph neural network guided local search for the traveling salesperson problem
Benjamin Hudson, Qingbiao Li, Matthew Malencia, and Amanda Prorok · 2022
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Learning the travelling salesperson problem requires rethinking generalization
Chaitanya K Joshi, Quentin Cappart, Louis-Martin Rousseau, and Thomas Laurent · 2022
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Elucidating the design space of diffusion-based generative models
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine · 2022
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Sym-NCO: Leveraging symmetricity for neural combinatorial optimization
Minsu Kim, Junyoung Park, and Jinkyoo Park · 2022
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Diffusion-LM improves controllable text generation
Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, and Tatsunori Hashimoto · 2022
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Diffsinger: Singing voice synthesis via shallow diffusion mechanism
Jinglin Liu, Chengxi Li, Yi Ren, Feiyang Chen, and Zhou Zhao · 2022
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Antigen-specific antibody design and optimization with diffusion-based generative models for protein structures
Shitong Luo, Yufeng Su, Xingang Peng, Sheng Wang, Jian Peng, and Jianzhu Ma · 2022
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Optimistic tree searches for combinatorial black-box optimization
Cedric Malherbe, Antoine Grosnit, Rasul Tutunov, Haitham Bou Ammar, and Jun Wang · 2022
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Dimes: A differentiable meta solver for combinatorial optimization problems
Ruizhong Qiu, Zhiqing Sun, and Yiming Yang · 2022
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Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Palette: Image-to-image diffusion models
Chitwan Saharia, William Chan, Huiwen Chang, Chris Lee, Jonathan Ho, Tim Salimans, David Fleet, and Mohammad Norouzi · 2022
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Make-a-video: Text-to-video generation without text-video data
Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni, et al · 2022
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Score-based continuous-time discrete diffusion models
Haoran Sun, Lijun Yu, Bo Dai, Dale Schuurmans, and Hanjun Dai · 2022
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Digress: Discrete denoising diffusion for graph generation
Clement Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, and Pascal Frossard · 2022
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Diffusion-based molecule generation with informative prior bridges
Lemeng Wu, Chengyue Gong, Xingchao Liu, Mao Ye, and Qiang Liu · 2022
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Diffsound: Discrete diffusion model for text-to-sound generation
Dongchao Yang, Jianwei Yu, Helin Wang, Wen Wang, Chao Weng, Yuexian Zou, and Dong Yu · 2022
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Magvit: Masked generative video transformer
Lijun Yu, Yong Cheng, Kihyuk Sohn, José Lezama, Han Zhang, Huiwen Chang, Alexander G Hauptmann, Ming-Hsuan Yang, Yuan Hao, Irfan Essa, et al · 2022
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Pitney bowes parcel shipping index, 2023
pitney bowes · 2023
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