Rio Yokota
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
38
Venues
22
Active years
2009–2026
Best venue rank
A*
Where they publish
Papers
38 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2026 | AAAI | FedPM: Federated Learning Using Second-order Optimization with Preconditioned Mixing of Local Parameters. | Hiro Ishii, Kenta Niwa, Hiroshi Sawada, Akinori Fujino, Noboru Harada, Rio Yokota |
| 2026 | LREC | Building Effective Japanese Medical LLMs with an Open Recipe for Domain Adaptation through Continued Pre-training. | Akiko Aizawa, Yuki Arase, Fei Cheng, Jiahao Huang, Zhiyi Huang, Junfeng Jiang, Teruhito Kanazawa, Daisuke Kawahara, Kazuma Kobayashi, Takashi Kodama, Sadao Kurohashi, Yusuke Oda, Tsuta Yuma, Zhen Wan, Zhishen Yang, Rio Yokota |
| 2025 | COLING | Aurora-M: Open Source Continual Pre-training for Multilingual Language and Code. | Taishi Nakamura, Mayank Mishra, Simone Tedeschi, Yekun Chai, Jason T. Stillerman, Felix Friedrich, Prateek Yadav, Tanmay Laud, Minh Chien Vu, Terry Yue Zhuo, Diganta Misra, Ben Bogin, Xuan-Son Vu, Marzena Karpinska, Arnav Varma Dantuluri, Wojciech Kusa, Tommaso Furlanello, Rio Yokota, Niklas Muennighoff, Suhas Pai, Tosin P. Adewumi, Veronika Laippala, Xiaozhe Yao, Adalberto Barbosa Junior, Aleksandr Drozd, Jordan Clive, Kshitij Gupta, Liangyu Chen, Qi Sun, Ken Tsui, Nour Moustafa-Fahmy, Nicolo Monti, Tai Dang, Ziyang Luo, Tien-Tung Bui, Roberto Navigli, Virendra Mehta, Matthew Blumberg, Victor May, Hiep Nguyen, Sampo Pyysalo |
| 2025 | EMNLP | Leveraging High-Resource English Corpora for Cross-lingual Domain Adaptation in Low-Resource Japanese Medicine via Continued Pre-training. | Kazuma Kobayashi, Zhen Wan, Fei Cheng, Tsuta Yuma, Xin Zhao, Junfeng Jiang, Jiahao Huang, Zhiyi Huang, Yusuke Oda, Rio Yokota, Yuki Arase, Daisuke Kawahara, Akiko Aizawa, Sadao Kurohashi |
| 2025 | ICLR | Local Loss Optimization in the Infinite Width: Stable Parameterization of Predictive Coding Networks and Target Propagation. | Satoki Ishikawa, Rio Yokota, Ryo Karakida |
| 2025 | ICLR | Drop-Upcycling: Training Sparse Mixture of Experts with Partial Re-initialization. | Taishi Nakamura, Takuya Akiba, Kazuki Fujii, Yusuke Oda, Rio Yokota, Jun Suzuki |
| 2025 | ICS | Scaling Large-scale GNN Training to Thousands of Processors on CPU-based Supercomputers. | Chen Zhuang, Lingqi Zhang, Du Wu, Peng Chen, Jiajun Huang, Xin Liu, Rio Yokota, Nikoli Dryden, Toshio Endo, Satoshi Matsuoka, Mohamed Wahib |
| 2025 | PPoPP | A General and Scalable GCN Training Framework on CPU Supercomputers. | Chen Zhuang, Peng Chen, Xin Liu, Rio Yokota, Nikoli Dryden, Lingqi Zhang, Toshio Endo, Satoshi Matsuoka, Mohamed Wahib |
| 2024 | ECCV | Scaling Backwards: Minimal Synthetic Pre-Training? | Ryo Nakamura, Ryu Tadokoro, Ryosuke Yamada, Yuki M. Asano, Iro Laina, Christian Rupprecht, Nakamasa Inoue, Rio Yokota, Hirokatsu Kataoka |
| 2024 | ECCV | Rethinking Image Super-Resolution from Training Data Perspectives. | Go Ohtani, Ryu Tadokoro, Ryosuke Yamada, Yuki M. Asano, Iro Laina, Christian Rupprecht, Nakamasa Inoue, Rio Yokota, Hirokatsu Kataoka, Yoshimitsu Aoki |
| 2024 | ECCV | Formula-Supervised Visual-Geometric Pre-training. | Ryosuke Yamada, Kensho Hara, Hirokatsu Kataoka, Koshi Makihara, Nakamasa Inoue, Rio Yokota, Yutaka Satoh |
| 2024 | ICLR | When Does Second-Order Optimization Speed Up Training? | Satoki Ishikawa, Rio Yokota |
| 2024 | ICML | Variational Learning is Effective for Large Deep Networks. | Yuesong Shen, Nico Daheim, Bai Cong, Peter Nickl, Gian Maria Marconi, Clement Bazan, Rio Yokota, Iryna Gurevych, Daniel Cremers, Mohammad Emtiyaz Khan, Thomas Mllenhoff |
| 2024 | ICPR | On the Relationship Between Double Descent of CNNs and Shape/Texture Bias Under Learning Process. | Shun Iwase, Shuya Takahashi, Nakamasa Inoue, Rio Yokota, Ryo Nakamura, Hirokatsu Kataoka, Eisaku Maeda |
| 2023 | CVPR | Pixel-level Contrastive Learning of Driving Videos with Optical Flow. | Tomoya Takahashi, Shingo Yashima, Kohta Ishikawa, Ikuro Sato, Rio Yokota |
| 2023 | CVPR | Visual Atoms: Pre-Training Vision Transformers with Sinusoidal Waves. | Sora Takashima, Ryo Hayamizu, Nakamasa Inoue, Hirokatsu Kataoka, Rio Yokota |
| 2023 | ICCV | Pre-training Vision Transformers with Very Limited Synthesized Images. | Ryo Nakamura, Hirokatsu Kataoka, Sora Takashima, Edgar Josafat Martinez-Noriega, Rio Yokota, Nakamasa Inoue |
| 2023 | ICCV | SegRCDB: Semantic Segmentation via Formula-Driven Supervised Learning. | Risa Shinoda, Ryo Hayamizu, Kodai Nakashima, Nakamasa Inoue, Rio Yokota, Hirokatsu Kataoka |
| 2023 | ICPP | Computing the k-th Eigenvalue of Symmetric H2-Matrices. | M. Ridwan Apriansyah, Rio Yokota |
| 2023 | ICPP | O(N) distributed direct factorization of structured dense matrices using runtime systems. | Sameer Deshmukh, Rio Yokota, George Bosilca, Qianxiang Ma |
| 2023 | PPoPP | Fast Symmetric Eigenvalue Decomposition via WY Representation on Tensor Core. | Shaoshuai Zhang, Ruchi Shah, Hiroyuki Ootomo, Rio Yokota, Panruo Wu |
| 2022 | CVPR | Replacing Labeled Real-image Datasets with Auto-generated Contours. | Hirokatsu Kataoka, Ryo Hayamizu, Ryosuke Yamada, Kodai Nakashima, Sora Takashima, Xinyu Zhang, Edgar Josafat Martinez-Noriega, Nakamasa Inoue, Rio Yokota |
| 2022 | ICRA | OPIRL: Sample Efficient Off-Policy Inverse Reinforcement Learning via Distribution Matching. | Hana Hoshino, Kei Ota, Asako Kanezaki, Rio Yokota |
| 2022 | PDCAT | QR Factorization of Block Low-Rank Matrices on Multi-instance GPU. | Satoshi Ohshima, Akihiro Ida, Rio Yokota, Ichitaro Yamazaki |
| 2022 | SC | Scalable Linear Time Dense Direct Solver for 3-D Problems without Trailing Sub-Matrix Dependencies. | Qianxiang Ma, Sameer Deshmukh, Rio Yokota |
| 2021 | ICCV | RePOSE: Fast 6D Object Pose Refinement via Deep Texture Rendering. | Shun Iwase, Xingyu Liu, Rawal Khirodkar, Rio Yokota, Kris M. Kitani |
| 2020 | KDD | Rich Information is Affordable: A Systematic Performance Analysis of Second-order Optimization Using K-FAC. | Yuichiro Ueno, Kazuki Osawa, Yohei Tsuji, Akira Naruse, Rio Yokota |
| 2019 | CCGRID | A Performance Improvement Approach for Second-Order Optimization in Large Mini-batch Training. | Hiroki Naganuma, Rio Yokota |
| 2019 | CCGRID | Exhaustive Study of Hierarchical AllReduce Patterns for Large Messages Between GPUs. | Yuichiro Ueno, Rio Yokota |
| 2019 | CVPR | Large-Scale Distributed Second-Order Optimization Using Kronecker-Factored Approximate Curvature for Deep Convolutional Neural Networks. | Kazuki Osawa, Yohei Tsuji, Yuichiro Ueno, Akira Naruse, Rio Yokota, Satoshi Matsuoka |
| 2017 | EuroPar | Performance Evaluation of Computation and Communication Kernels of the Fast Multipole Method on Intel Manycore Architecture. | Mustafa Abdul Jabbar, Mohammed A. Al Farhan, Rio Yokota, David E. Keyes |
| 2017 | ICANN | Evaluating the Compression Efficiency of the Filters in Convolutional Neural Networks. | Kazuki Osawa, Rio Yokota |
| 2016 | ICPADS | Tapas: An Implicitly Parallel Programming Framework for Hierarchical N-Body Algorithms. | Keisuke Fukuda, Motohiko Matsuda, Naoya Maruyama, Rio Yokota, Kenjiro Taura, Satoshi Matsuoka |
| 2012 | ISPDC | Scalable Force Directed Graph Layout Algorithms Using Fast Multipole Methods. | Enas Yunis, Rio Yokota, Aron J. Ahmadia |
| 2012 | SC | Abstract: Scalable Fast Multipole Methods for Vortex Element Methods. | Qi Hu, Nail A. Gumerov, Rio Yokota, Lorena A. Barba, Ramani Duraiswami |
| 2012 | SC | Poster: Scalable Fast Multipole Methods for Vortex Element Methods. | Qi Hu, Nail A. Gumerov, Rio Yokota, Lorena A. Barba, Ramani Duraiswami |
| 2012 | SC | A Task Parallel Implementation of Fast Multipole Methods. | Kenjiro Taura, Jun Nakashima, Rio Yokota, Naoya Maruyama |
| 2009 | SC | 42 TFlops hierarchical | Tsuyoshi Hamada, Tetsu Narumi, Rio Yokota, Kenji Yasuoka, Keigo Nitadori, Makoto Taiji |