Suchergebnisse - Machine learning. Computational learning theory. Combinatorial optimization.

Learning Combinatorial Optimization on Graphs: A Survey With Applications to Networking von Vesselinova, Natalia, Steinert, Rebecca, Perez-Ramirez, Daniel F., Boman, Magnus

“… We organize and compare the structures involved with learning to solve combinatorial optimization problems, with a special eye on the telecommunications domain and its continuous development of live …”
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Theory of Neuromorphic Computing by Waves: Machine Learning by Rogue Waves, Dispersive Shocks, and Solitons von Marcucci, Giulia, Pierangeli, Davide, Conti, Claudio

“… datasets, and universal Boolean gates. The rank of the transmission matrix has a fundamental role in assessing the learning abilities of the wave …”
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Memristive Boltzmann machine: A hardware accelerator for combinatorial optimization and deep learning von Bojnordi, Mahdi Nazm, Ipek, Engin

“… The Boltzmann machine is a massively parallel computational model capable of solving a broad class of combinatorial optimization problems …”
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Quantum approximate optimization of non-planar graph problems on a planar superconducting processor von Harrigan, Matthew P, Sung, Kevin J, Neeley, Matthew, Satzinger, Kevin J, Arute Frank, Arya Kunal, Atalaya Juan, Bardin, Joseph C, Barends Rami, Boixo Sergio, Broughton, Michael, Buckley, Bob B, Buell, David A, Burkett, Brian, Bushnell, Nicholas, Chen, Yu, Chen, Zijun, Chiaro, Ben, Collins, Roberto, Courtney, William, Demura, Sean, Dunsworth, Andrew, Eppens, Daniel, Fowler, Austin, Brooks, Foxen, Gidney, Craig, Giustina Marissa, Graff, Rob, Habegger, Steve, Ho, Alan, Hong, Sabrina, Huang, Trent, Ioffe, L B, Isakov, Sergei V, Evan, Jeffrey, Zhang, Jiang, Jones, Cody, Kafri Dvir, Kechedzhi Kostyantyn, Kelly, Julian, Kim, Seon, Klimov, Paul V, Korotkov, Alexander N, Kostritsa Fedor, Landhuis, David, Laptev Pavel, Lindmark, Mike, Leib, Martin, Martin, Orion, Martinis, John M, McClean, Jarrod R, McEwen, Matt, Megrant Anthony, Xiao, Mi, Mohseni Masoud, Mruczkiewicz Wojciech, Mutus Josh, Naaman Ofer, Neill, Charles, Neukart Florian, Niu Murphy Yuezhen, O’Brien Thomas E, O’Gorman Bryan, Ostby, Eric, Petukhov, Andre, Putterman Harald, Quintana, Chris, Roushan Pedram, Rubin, Nicholas C, Sank, Daniel, Skolik Andrea, Smelyanskiy Vadim, Strain, Doug, Streif, Michael, Szalay, Marco, Vainsencher Amit, White, Theodore, Jamie, Yao Z, Yeh Ping, Zalcman, Adam, Zhou, Leo, Neven Hartmut, Bacon, Dave, Lucero, Erik, Farhi, Edward, Ryan, Babbush

“… Faster algorithms for combinatorial optimization could prove transformative for diverse areas such as logistics, finance and machine learning …”
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Structure of an Ultrathin Oxide on Pt3Sn(111) Solved by Machine Learning Enhanced Global Optimization von Merte, Lindsay R., Bisbo, Malthe Kjær, Sokolović, Igor, Setvín, Martin, Hagman, Benjamin, Shipilin, Mikhail, Schmid, Michael, Diebold, Ulrike, Lundgren, Edvin, Hammer, Bjørk

“… ‐based search algorithms. So far, such methods have been limited by the combinatorial complexity and computational expense of sufficiently accurate energy estimation for surfaces …”
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Towards fair machine learning using combinatorial methods von Saraswat, Anant, Pal, Manjish, Pokhriyal, Subham, Abhishek, Kumar

“… With the rise of artificial intelligence and machine learning in the last decade, there has been an increasing interest in developing a solid theory and implementing algorithmic fairness …”
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Solving combinatorial optimization and machine learning problems on hybrid near-term quantum photonic computers von Slysz, Mateusz, Grodzki, Łukasz, Rydlichowski, Piotr, Siera, Dawid, Kurowski, Krzysztof, Waligóra, Grzegorz, Węglarz, Jan

“… range of specific problems. Previous research has indicated that quantum computers have the potential to resolve optimization problems with theoretically lower computational complexity than classical algorithms …”
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Learning for Robust Combinatorial Optimization: Algorithm and Application von Shao, Zhihui, Yang, Jianyi, Shen, Cong, Ren, Shaolei

“… In this paper, we study robust combinatorial optimization and propose a novel learning-based optimizer, called LRCO …”
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A Review: Machine Learning for Combinatorial Optimization Problems in Energy Areas von Yang, Xinyi, Wang, Ziyi, Zhang, Hengxi, Ma, Nan, Yang, Ning, Liu, Hualin, Zhang, Haifeng, Yang, Lei

“… Traditional approaches for COPs suffer from high computational time and reliance on expert knowledge, and machine learning (ML …”
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A cooperative game for automated learning of elasto-plasticity knowledge graphs and models with AI-guided experimentation von Wang, Kun, Sun, WaiChing, Du, Qiang

“… We introduce a new concept from graph theory where a modeler agent is tasked with evaluating all the modeling options recast as a directed multigraph and find the optimal path that links the source …”
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Enforcing exact physics in scientific machine learning: A data-driven exterior calculus on graphs von Trask, Nathaniel, Huang, Andy, Hu, Xiaozhe

“… As traditional machine learning tools are increasingly applied to science and engineering applications, physics-informed methods have emerged as effective tools for endowing inferences …”
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Transfer Reinforcement Learning for Combinatorial Optimization Problems von Souza, Gleice Kelly Barbosa, Santos, Samara Oliveira Silva, Ottoni, André Luiz Carvalho, Oliveira, Marcos Santos, Oliveira, Daniela Carine Ramires, Nepomuceno, Erivelton Geraldo

“… ). The integration of transfer learning (TL) with AutoRL in combinatorial optimization is an area that requires further research …”
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Free-energy machine for combinatorial optimization von Shen, Zi-Song, Pan, Feng, Wang, Yao, Men, Yi-Ding, Xu, Wen-Biao, Yung, Man-Hong, Zhang, Pan

“… Here we propose a general method, free-energy machine (FEM), based on the ideas of free-energy minimization in statistical physics, combined with automatic differentiation and gradient-based optimization in machine learning …”
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A hybrid neural combinatorial optimization framework assisted by automated algorithm design von Ma, Liang, Hao, Xingxing, Zhou, Wei, He, Qianbao, Zhang, Ruibang, Chen, Li

“… ) and the Perturbative Neural Combinatorial Optimization (PNCO). The CNCO generally trains an encoder-decoder model via supervised learning to construct solutions from scratch …”
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Nonlinear stochastic dynamics of detuned bladed-disks with uncertain mistuning and detuning optimization using a probabilistic machine learning tool von Capiez-Lernout, Evangéline, Soize, Christian

“… We present an efficient computational methodology for reducing the computational cost, an analysis of the detuning, and the detuning optimization, based on the use of a high-fidelity computational model …”
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Learning Driven NOMA Assisted Vehicular Edge Computing via Underlay Spectrum Sharing von Qian, Liping, Wu, Yuan, Yu, Ningning, Jiang, Fuli, Zhou, Haibo, Quek, Tony Q.S.

“… Edge computing has been considered as one of the key paradigms in the fifth-generation (5G) networks for enabling computation-intensive yet latency-sensitive …”
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GAMMA: Graph Attention Model for Multiple Agents to Solve Team Orienteering Problem With Multiple Depots von Sankaran, Prashant, McConky, Katie, Sudit, Moises, Ortiz-Pena, Hector

“… ). The TOPMD instance is an NP-hard combinatorial optimization problem that involves multiple agents …”
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Sparse learning via Boolean relaxations von Pilanci, Mert, Wainwright, Martin J., El Ghaoui, Laurent

“… We introduce novel relaxations for cardinality-constrained learning problems, including least-squares regression as a special but important case …”
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Modern machine learning for tackling inverse problems in chemistry: molecular design to realization von Sridharan, Bhuvanesh, Goel, Manan, Priyakumar, U Deva

“… In pursuit of finding molecules with desired properties, chemists have traditionally relied on experimentation and recently on combinatorial methods to generate new substances often complimented …”
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Integrating statistical physics and machine learning for combinatorial optimization

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