Toshiba's breakthrough algorithm realizes world's fastest, largest-scale combinatorial optimization
Toshiba Corporation has realized a major breakthrough in combinatorial optimization—the selection of the best solutions from among an enormous number of combinatorial patterns—with the development of an algorithm that delivers the world's fastest and largest-scale performance, and an approximately 10-fold improvement over current methods. Toshiba's new method can be applied to such daunting but essential tasks as identifying efficient delivery routes, determining the most effective molecular structures to investigate in new drug development, and building portfolios of profitable financial products.
https://m.phys.org/news/2019-04-toshiba-breakthrough-algorithm-world-fastest.html
#optimization #algorithms #computing
Toshiba Corporation has realized a major breakthrough in combinatorial optimization—the selection of the best solutions from among an enormous number of combinatorial patterns—with the development of an algorithm that delivers the world's fastest and largest-scale performance, and an approximately 10-fold improvement over current methods. Toshiba's new method can be applied to such daunting but essential tasks as identifying efficient delivery routes, determining the most effective molecular structures to investigate in new drug development, and building portfolios of profitable financial products.
https://m.phys.org/news/2019-04-toshiba-breakthrough-algorithm-world-fastest.html
#optimization #algorithms #computing
phys.org
Toshiba's breakthrough algorithm realizes world's fastest, largest-scale combinatorial optimization
Toshiba Corporation has realized a major breakthrough in combinatorial optimization—the selection of the best solutions from among an enormous number of combinatorial patterns—with the development ...
Autonomous skill discovery with Quality-Diversity and Unsupervised Descriptors
Cully et al.: https://arxiv.org/pdf/1905.11874.pdf
#QualityDiversity #Optimization #Evolution #Robotics #DeepLearning
Cully et al.: https://arxiv.org/pdf/1905.11874.pdf
#QualityDiversity #Optimization #Evolution #Robotics #DeepLearning
New Frontiers of Automated Mechanism Design for Pricing and Auctions by Maria-Florina Balcan, @mldcmu, Tuomas Sandholm, Ellen Vitercik @csdatcmu
Learn more → https://mld.ai/y1m
Tutorial Video Part I: https://youtu.be/buK3KXZcGAI
Tutorial Video Part II: https://youtu.be/T8gaK4Yw4zI
#MechanismDesign #GameTheory #Tutorial #MachineLearning #Optimization #ML
Learn more → https://mld.ai/y1m
Tutorial Video Part I: https://youtu.be/buK3KXZcGAI
Tutorial Video Part II: https://youtu.be/T8gaK4Yw4zI
#MechanismDesign #GameTheory #Tutorial #MachineLearning #Optimization #ML
Google
EC19 New Frontiers of Automated Mechanism Design for Pricing and Auctions
Meta-Learning with Implicit Gradients
Aravind Rajeswaran, Chelsea Finn, Sham Kakade, Sergey Levine : https://arxiv.org/abs/1909.04630
#MachineLearning #ArtificialIntelligence #Optimization #Control #MetaLearning
Aravind Rajeswaran, Chelsea Finn, Sham Kakade, Sergey Levine : https://arxiv.org/abs/1909.04630
#MachineLearning #ArtificialIntelligence #Optimization #Control #MetaLearning
arXiv.org
Meta-Learning with Implicit Gradients
A core capability of intelligent systems is the ability to quickly learn new tasks by drawing on prior experience. Gradient (or optimization) based meta-learning has recently emerged as an...
Meta-Learning with Implicit Gradients
Aravind Rajeswaran, Chelsea Finn, Sham Kakade, Sergey Levine : https://arxiv.org/abs/1909.04630
#MachineLearning #ArtificialIntelligence #Optimization #Control #MetaLearning
Aravind Rajeswaran, Chelsea Finn, Sham Kakade, Sergey Levine : https://arxiv.org/abs/1909.04630
#MachineLearning #ArtificialIntelligence #Optimization #Control #MetaLearning
arXiv.org
Meta-Learning with Implicit Gradients
A core capability of intelligent systems is the ability to quickly learn new tasks by drawing on prior experience. Gradient (or optimization) based meta-learning has recently emerged as an...
Logarithmic Regret for Online Control
Naman Agarwal, Elad Hazan, Karan Singh : https://arxiv.org/abs/1909.05062
#MachineLearning #Optimization #Control
Naman Agarwal, Elad Hazan, Karan Singh : https://arxiv.org/abs/1909.05062
#MachineLearning #Optimization #Control
arXiv.org
Logarithmic Regret for Online Control
We study optimal regret bounds for control in linear dynamical systems under
adversarially changing strongly convex cost functions, given the knowledge of
transition dynamics. This includes...
adversarially changing strongly convex cost functions, given the knowledge of
transition dynamics. This includes...
Neural reparameterization improves structural optimization
Hoyer et al.: https://arxiv.org/abs/1909.04240
#MachineLearning #NeuralNetworks #Optimization
Hoyer et al.: https://arxiv.org/abs/1909.04240
#MachineLearning #NeuralNetworks #Optimization
arXiv.org
Neural reparameterization improves structural optimization
Structural optimization is a popular method for designing objects such as bridge trusses, airplane wings, and optical devices. Unfortunately, the quality of solutions depends heavily on how the...
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search
White et al.: https://arxiv.org/abs/1910.11858
#Bayesian #Optimization #NeuralArchitectureSearch
White et al.: https://arxiv.org/abs/1910.11858
#Bayesian #Optimization #NeuralArchitectureSearch
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search
White et al.: https://arxiv.org/abs/1910.11858
#Bayesian #Optimization #NeuralArchitectureSearch
White et al.: https://arxiv.org/abs/1910.11858
#Bayesian #Optimization #NeuralArchitectureSearch
arXiv.org
BANANAS: Bayesian Optimization with Neural Architectures for...
Over the past half-decade, many methods have been considered for neural architecture search (NAS). Bayesian optimization (BO), which has long had success in hyperparameter optimization, has...
A Modern Introduction to Online Learning
Francesco Orabona : https://arxiv.org/abs/1912.13213
#MachineLearning #Optimization #Control
Francesco Orabona : https://arxiv.org/abs/1912.13213
#MachineLearning #Optimization #Control