Search Results for author: Rio Yokota

Found 10 papers, 8 papers with code

Takeuchi's Information Criteria as Generalization Measures for DNNs Close to NTK Regime

no code implementations29 Sep 2021 Hiroki Naganuma, Taiji Suzuki, Rio Yokota, Masahiro Nomura, Kohta Ishikawa, Ikuro Sato

Generalization measures are intensively studied in the machine learning community for better modeling generalization gaps.

Hyperparameter Optimization

OPIRL: Sample Efficient Off-Policy Inverse Reinforcement Learning via Distribution Matching

1 code implementation9 Sep 2021 Hana Hoshino, Kei Ota, Asako Kanezaki, Rio Yokota

Inverse Reinforcement Learning (IRL) is attractive in scenarios where reward engineering can be tedious.


RePOSE: Fast 6D Object Pose Refinement via Deep Texture Rendering

1 code implementation ICCV 2021 Shun Iwase, Xingyu Liu, Rawal Khirodkar, Rio Yokota, Kris M. Kitani

Furthermore, we utilize differentiable Levenberg-Marquardt (LM) optimization to refine a pose fast and accurately by minimizing the feature-metric error between the input and rendered image representations without the need of zooming in.

6D Pose Estimation 6D Pose Estimation using RGB

Epipolar-Guided Deep Object Matching for Scene Change Detection

no code implementations30 Jul 2020 Kento Doi, Ryuhei Hamaguchi, Shun Iwase, Rio Yokota, Yutaka Matsuo, Ken Sakurada

To cope with the difficulty, we introduce a deep graph matching network that establishes object correspondence between an image pair.

Change Detection Graph Matching +1

Scalable and Practical Natural Gradient for Large-Scale Deep Learning

1 code implementation13 Feb 2020 Kazuki Osawa, Yohei Tsuji, Yuichiro Ueno, Akira Naruse, Chuan-Sheng Foo, Rio Yokota

Large-scale distributed training of deep neural networks results in models with worse generalization performance as a result of the increase in the effective mini-batch size.

Image Classification

Effect of Mixed Precision Computing on H-Matrix Vector Multiplication in BEM Analysis

1 code implementation30 Oct 2019 Rise Ooi, Takeshi Iwashita, Takeshi Fukaya, Akihiro Ida, Rio Yokota

Hierarchical Matrix (H-matrix) is an approximation technique which splits a target dense matrix into multiple submatrices, and where a selected portion of submatrices are low-rank approximated.

Mathematical Software Distributed, Parallel, and Cluster Computing

Practical Deep Learning with Bayesian Principles

1 code implementation NeurIPS 2019 Kazuki Osawa, Siddharth Swaroop, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota, Mohammad Emtiyaz Khan

Importantly, the benefits of Bayesian principles are preserved: predictive probabilities are well-calibrated, uncertainties on out-of-distribution data are improved, and continual-learning performance is boosted.

Continual Learning Data Augmentation +1

Extreme Scale FMM-Accelerated Boundary Integral Equation Solver for Wave Scattering

1 code implementation27 Mar 2018 Mustafa Abduljabbar, Mohammed Al Farhan, Noha Al-Harthi, Rui Chen, Rio Yokota, Hakan Bagci, David Keyes

With distributed memory optimizations, on the other hand, we report near-optimal efficiency in the weak scalability study with respect to both the logarithmic communication complexity as well as the theoretical scaling complexity of FMM.

Performance Computational Engineering, Finance, and Science Mathematical Software

Asynchronous Execution of the Fast Multipole Method Using Charm++

1 code implementation29 May 2014 Mustafa AbdulJabbar, Rio Yokota, David Keyes

Fast multipole methods (FMM) on distributed mem- ory have traditionally used a bulk-synchronous model of com- municating the local essential tree (LET) and overlapping it with computation of the local data.

Distributed, Parallel, and Cluster Computing 70F10 D.1.2; D.1.3; G.1.0; G.1.2

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