Rio Yokota - Concepedia

Author

Rio Yokota

Also Known As

Chen, Peng, Ishikawa, Kohta, R. Yokota, Rio YOKOTA, Rio Yokota, Rio Yokota, Werner, Matthias, Yanaka, Hitomi, Yokota, Rio

200

Publications

1.8K

Citations

H-Index

Concepts

All Affiliations

Tokyo Institute of Technology King Abdullah University of Science and Technology Boston University Keio University National Institute of Advanced Industrial Science and Technology

About

Rio Yokota is an author at Tokyo Institute of Technology specializing in engineering, computer science, and parallel programming.

Top Concepts

#	Concept	H-Index	Publications	Citations
1	Engineering	20	31	1.2K
2	Natural Sciences	5	5	290
3	Applied Physics	2	2	79
4	Biomedical Engineering	1	1	84
5	Chemistry	1	1	19

Rio Yokota

Publications

1.5K

Citations

H-Index

Page 1

	Year	Citations
42 TFlops hierarchical <i>N</i> -body simulations on GPUs with applications in both astrophysics and turbulence Tsuyoshi Hamada, Tetsu Narumi, Rio Yokota, Vortex ParticlesEngineeringGpu BenchmarkingComputer ArchitectureParticle Method	2009	116
Practical Deep Learning with Bayesian Principles Kazuki Osawa, Siddharth Swaroop, Mohammad Emtiyaz Khan, arXiv (Cornell University) Artificial IntelligenceBayesian PrinciplesConvolutional Neural NetworkDeep Neural NetworksEngineering	2019	93
Biomolecular electrostatics using a fast multipole BEM on up to 512 gpus and a billion unknowns Rio Yokota, Jaydeep P. Bardhan, Matthew G. Knepley, Computer Physics Communications ElectrohydrodynamicsEngineeringPhysicsBioelectronicsBiomolecular Electrostatics	2011	84
Large-Scale Distributed Second-Order Optimization Using Kronecker-Factored Approximate Curvature for Deep Convolutional Neural Networks Kazuki Osawa, Yohei Tsuji, Yuichiro Ueno, Large-scale Global OptimizationConvolutional Neural NetworkEngineeringMachine LearningBatch Normalization	2019	82
RePOSE: Fast 6D Object Pose Refinement via Deep Texture Rendering Shun Iwase, Xingyu Liu, Rawal Khirodkar, 2021 IEEE/CVF International Conference on Computer Vision (ICCV) 3D Computer VisionConvolutional Neural NetworkMachine VisionMachine LearningImage Analysis	2021	77
Petascale turbulence simulation using a highly parallel fast multipole method on GPUs Rio Yokota, Lorena A. Barba, Tetsu Narumi, Computer Physics Communications EngineeringPhysicsAerospace EngineeringNatural SciencesFluid Mechanics	2012	75
PetRBF — A parallel O(N) algorithm for radial basis function interpolation with Gaussians Rio Yokota, Lorena A. Barba, Matthew G. Knepley Computer Methods in Applied Mechanics and Engineering Numerical AnalysisGeometric InterpolationNumerical ComputationEngineeringInterpolation Space	2010	71
A tuned and scalable fast multipole method as a preeminent algorithm for exascale systems Rio Yokota, Lorena A. Barba The International Journal of High Performance Computing Applications	2012	65
Petascale molecular dynamics simulation using the fast multipole method on K computer Yousuke Ohno, Rio Yokota, Hiroshi Koyama, Computer Physics Communications Computational BiophysicsEngineeringFast Multipole MethodPhysicsNatural Sciences	2014	60
Fast multipole methods on a cluster of GPUs for the meshless simulation of turbulence Rio Yokota, Taro Narumi, Ryuji Sakamaki, Computer Physics Communications Numerical AnalysisAeroacousticsMesh OptimizationEngineeringPhysics	2009	57

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