Rio Yokota - RankMe

157 papers A* 13A 4B 7C 10Journal 96Unranked 24

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Evolutionary Context Search for Automated Skill Acquisition. CoRR Qi Sun, Stefan Nielsen, Rio Yokota, Yujin Tang
2026	A*	`conf`	FedPM: Federated Learning Using Second-order Optimization with Preconditioned Mixing of Local Parameters. AAAI Hiro Ishii, Kenta Niwa, Hiroshi Sawada, Akinori Fujino, Noboru Harada, Rio Yokota
2026	J	`jnl`	On the Optimal Reasoning Length for RL-Trained Language Models. CoRR Daisuke Nohara, Taishi Nakamura, Rio Yokota
2026	J	`jnl`	Takeuchi's Information Criteria as Generalization Measures for DNNs Close to NTK Regime. CoRR Hiroki Naganuma, Taiji Suzuki, Rio Yokota, Masahiro Nomura, Kohta Ishikawa, Ikuro Sato
2026	—	`conf`	Tensor-Core-Optimized Strategies for BLR × Tall-Skinny Matrix Multiplication in BEM. SCA/HPC Asia Akihiro Ida, Kazuya Goto, Rio Yokota, Tasuku Hiraishi, Toshihiro Hanawa, Takeshi Iwashita, Masatoshi Kawai, Satoshi Ohshima, Tetsuya Hoshino
2025	B	`conf`	A General and Scalable GCN Training Framework on CPU Supercomputers. PPoPP Chen Zhuang, Peng Chen, Xin Liu, Rio Yokota, Nikoli Dryden, Lingqi Zhang, Toshio Endo, Satoshi Matsuoka, Mohamed Wahib
2025	—	`conf`	A Study on the Performance and Usability of Managed Memory and Unified Memory for Accelerating Numerical Calculation Program. MCSoC Satoshi Ohshima, Akihiro Ida, Masatoshi Kawai, Takeshi Fukaya, Rio Yokota
2025	J	`jnl`	An inherently parallel H2-ULV factorization for solving dense linear systems on GPUs. CoRR Qianxiang Ma, Rio Yokota
2025	—	`conf`	Aurora-M: Open Source Continual Pre-training for Multilingual Language and Code. COLING (Industry) 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	J	`jnl`	Building Instruction-Tuning Datasets from Human-Written Instructions with Open-Weight Large Language Models. CoRR Youmi Ma, Sakae Mizuki, Kazuki Fujii, Taishi Nakamura, Masanari Ohi, Hinari Shimada, Taihei Shiotani, Koshiro Saito, Koki Maeda, Kakeru Hattori, Takumi Okamoto, Shigeki Ishida, Rio Yokota, Hiroya Takamura, Naoaki Okazaki
2025	A*	`conf`	Drop-Upcycling: Training Sparse Mixture of Experts with Partial Re-initialization. ICLR Taishi Nakamura, Takuya Akiba, Kazuki Fujii, Yusuke Oda, Rio Yokota, Jun Suzuki
2025	J	`jnl`	Drop-Upcycling: Training Sparse Mixture of Experts with Partial Re-initialization. CoRR Taishi Nakamura, Takuya Akiba, Kazuki Fujii, Yusuke Oda, Rio Yokota, Jun Suzuki
2025	J	`jnl`	FedPM: Federated Learning Using Second-order Optimization with Preconditioned Mixing of Local Parameters. CoRR Hiro Ishii, Kenta Niwa, Hiroshi Sawada, Akinori Fujino, Noboru Harada, Rio Yokota
2025	J	`jnl`	Improving LoRA with Variational Learning. CoRR Bai Cong, Nico Daheim, Yuesong Shen, Rio Yokota, Mohammad Emtiyaz Khan, Thomas Möllenhoff
2025	—	`conf`	Leveraging High-Resource English Corpora for Cross-lingual Domain Adaptation in Low-Resource Japanese Medicine via Continued Pre-training. EMNLP (Findings) 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	A*	`conf`	Local Loss Optimization in the Infinite Width: Stable Parameterization of Predictive Coding Networks and Target Propagation. ICLR Satoki Ishikawa, Rio Yokota, Ryo Karakida
2025	J	`jnl`	Masked Gated Linear Unit. CoRR Yukito Tajima, Nakamasa Inoue, Yusuke Sekikawa, Ikuro Sato, Rio Yokota
2025	J	`jnl`	NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA. Trans. Mach. Learn. Res. Marlon Tobaben, Mohamed Ali Souibgui, Rubèn Tito, Khanh Nguyen, Raouf Kerkouche, Kangsoo Jung, Joonas Jälkö, Lei Kang, Andrey Barsky, Vincent Poulain D'Andecy, Aurélie Joseph, Aashiq Muhamed, Kevin Kuo, Virginia Smith, Yusuke Yamasaki, Takumi Fukami, Kenta Niwa, Iifan Tyou, Hiro Ishii, Rio Yokota, Ragul N, Rintu Kutum, Josep Lladós, Ernest Valveny, Antti Honkela, Mario Fritz, Dimosthenis Karatzas
2025	C	`conf`	On the Interplay Between Precision, Rank and Admissibility for Iterative Hierarchical Low-Rank Matrix Solvers. ISC Thomas Spendlhofer, Qianxiang Ma, Yasuhiro Matsumoto, Rio Yokota
2025	J	`jnl`	On the Relationship Between Double Descent of CNNs and Shape/Texture Bias Under Learning Process. CoRR Shun Iwase, Shuya Takahashi, Nakamasa Inoue, Rio Yokota, Ryo Nakamura, Hirokatsu Kataoka
2025	J	`jnl`	Optimal Sparsity of Mixture-of-Experts Language Models for Reasoning Tasks. CoRR Taishi Nakamura, Satoki Ishikawa, Masaki Kawamura, Takumi Okamoto, Daisuke Nohara, Jun Suzuki, Rio Yokota
2025	J	`jnl`	PowerCLIP: Powerset Alignment for Contrastive Pre-Training. CoRR Masaki Kawamura, Nakamasa Inoue, Rintaro Yanagi, Hirokatsu Kataoka, Rio Yokota
2025	J	`jnl`	Rewriting Pre-Training Data Boosts LLM Performance in Math and Code. CoRR Kazuki Fujii, Yukito Tajima, Sakae Mizuki, Hinari Shimada, Taihei Shiotani, Koshiro Saito, Masanari Ohi, Masaki Kawamura, Taishi Nakamura, Takumi Okamoto, Shigeki Ishida, Kakeru Hattori, Youmi Ma, Hiroya Takamura, Rio Yokota, Naoaki Okazaki
2025	A	`conf`	Scaling Large-scale GNN Training to Thousands of Processors on CPU-based Supercomputers. ICS Chen Zhuang, Lingqi Zhang, Du Wu, Peng Chen, Jiajun Huang, Xin Liu, Rio Yokota, Nikoli Dryden, Toshio Endo, Satoshi Matsuoka, Mohamed Wahib
2025	J	`jnl`	Variational Learning Finds Flatter Solutions at the Edge of Stability. CoRR Avrajit Ghosh, Bai Cong, Rio Yokota, Saiprasad Ravishankar, Rongrong Wang, Molei Tao, Mohammad Emtiyaz Khan, Thomas Möllenhoff
2024	J	`jnl`	Accelerating Large Language Model Training with 4D Parallelism and Memory Consumption Estimator. CoRR Kazuki Fujii, Kohei Watanabe, Rio Yokota
2024	J	`jnl`	An inherently parallel ℋ Int. J. High Perform. Comput. Appl. Qianxiang Ma, Rio Yokota
2024	J	`jnl`	Aurora-M: The First Open Source Multilingual Language Model Red-teamed according to the U.S. Executive Order. CoRR 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, Alpay Ariyak, Aleksandr Drozd, Jordan Clive, Kshitij Gupta, Liangyu Chen, Qi Sun, Ken Tsui, Noah Persaud, Nour Moustafa-Fahmy, Tianlong Chen, Mohit Bansal, Nicolo Monti, Tai Dang, Ziyang Luo, Tien-Tung Bui, Roberto Navigli, Virendra Mehta, Matthew Blumberg, Victor May, Huu Nguyen, Sampo Pyysalo
2024	J	`jnl`	Balancing Speed and Stability: The Trade-offs of FP8 vs. BF16 Training in LLMs. CoRR Kazuki Fujii, Taishi Nakamura, Rio Yokota
2024	J	`jnl`	Building a Large Japanese Web Corpus for Large Language Models. CoRR Naoaki Okazaki, Kakeru Hattori, Hirai Shota, Hiroki Iida, Masanari Ohi, Kazuki Fujii, Taishi Nakamura, Mengsay Loem, Rio Yokota, Sakae Mizuki
2024	J	`jnl`	Continual Pre-Training for Cross-Lingual LLM Adaptation: Enhancing Japanese Language Capabilities. CoRR Kazuki Fujii, Taishi Nakamura, Mengsay Loem, Hiroki Iida, Masanari Ohi, Kakeru Hattori, Hirai Shota, Sakae Mizuki, Rio Yokota, Naoaki Okazaki
2024	J	`jnl`	DGEMM on integer matrix multiplication unit. Int. J. High Perform. Comput. Appl. Hiroyuki Ootomo, Katsuhisa Ozaki, Rio Yokota
2024	—	`conf`	Formula-Supervised Visual-Geometric Pre-training. ECCV (22) Ryosuke Yamada, Kensho Hara, Hirokatsu Kataoka, Koshi Makihara, Nakamasa Inoue, Rio Yokota, Yutaka Satoh
2024	J	`jnl`	Formula-Supervised Visual-Geometric Pre-training. CoRR Ryosuke Yamada, Kensho Hara, Hirokatsu Kataoka, Koshi Makihara, Nakamasa Inoue, Rio Yokota, Yutaka Satoh
2024	J	`jnl`	LLM-jp: A Cross-organizational Project for the Research and Development of Fully Open Japanese LLMs. CoRR Akiko Aizawa, Eiji Aramaki, Bowen Chen, Fei Cheng, Hiroyuki Deguchi, Rintaro Enomoto, Kazuki Fujii, Kensuke Fukumoto, Takuya Fukushima, Namgi Han, Yuto Harada, Chikara Hashimoto, Tatsuya Hiraoka, Shohei Hisada, Sosuke Hosokawa, Lu Jie, Keisuke Kamata, Teruhito Kanazawa, Hiroki Kanezashi, Hiroshi Kataoka, Satoru Katsumata, Daisuke Kawahara, Seiya Kawano, Atsushi Keyaki, Keisuke Kiryu, Hirokazu Kiyomaru, Takashi Kodama, Takahiro Kubo, Yohei Kuga, Ryoma Kumon, Shuhei Kurita, Sadao Kurohashi, Conglong Li, Taiki Maekawa, Hiroshi Matsuda, Yusuke Miyao, Kentaro Mizuki, Sakae Mizuki, Yugo Murawaki, Ryo Nakamura, Taishi Nakamura, Kouta Nakayama, Tomoka Nakazato, Takuro Niitsuma, Jiro Nishitoba, Yusuke Oda, Hayato Ogawa, Takumi Okamoto, Naoaki Okazaki, Yohei Oseki, Shintaro Ozaki, Koki Ryu, Rafal Rzepka, Keisuke Sakaguchi, Shota Sasaki, Satoshi Sekine, Kohei Suda, Saku Sugawara, Issa Sugiura, Hiroaki Sugiyama, Hisami Suzuki, Jun Suzuki, Toyotaro Suzumura, Kensuke Tachibana, Yu Takagi, Kyosuke Takami, Koichi Takeda, Masashi Takeshita, Masahiro Tanaka, Kenjiro Taura, Arseny Tolmachev, Nobuhiro Ueda, Zhen Wan, Shuntaro Yada, Sakiko Yahata, Yuya Yamamoto, Yusuke Yamauchi, Hitomi Yanaka, Rio Yokota, Koichiro Yoshino
2024	J	`jnl`	Lion Cub: Minimizing Communication Overhead in Distributed Lion. CoRR Satoki Ishikawa, Tal Ben-Nun, Brian Van Essen, Rio Yokota, Nikoli Dryden
2024	J	`jnl`	Local Loss Optimization in the Infinite Width: Stable Parameterization of Predictive Coding Networks and Target Propagation. CoRR Satoki Ishikawa, Rio Yokota, Ryo Karakida
2024	J	`jnl`	Natural Gradient Primal-Dual Method for Decentralized Learning. IEEE Trans. Signal Inf. Process. over Networks Kenta Niwa, Hiro Ishii, Hiroshi Sawada, Akinori Fujino, Noboru Harada, Rio Yokota
2024	J	`jnl`	NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA. CoRR Marlon Tobaben, Mohamed Ali Souibgui, Rubèn Tito, Khanh Nguyen, Raouf Kerkouche, Kangsoo Jung, Joonas Jälkö, Lei Kang, Andrey Barsky, Vincent Poulain D'Andecy, Aurélie Joseph, Aashiq Muhamed, Kevin Kuo, Virginia Smith, Yusuke Yamasaki, Takumi Fukami, Kenta Niwa, Iifan Tyou, Hiro Ishii, Rio Yokota, Ragul N, Rintu Kutum, Josep Lladós, Ernest Valveny, Antti Honkela, Mario Fritz, Dimosthenis Karatzas
2024	—	`conf`	On the Relationship Between Double Descent of CNNs and Shape/Texture Bias Under Learning Process. ICPR (25) Shun Iwase, Shuya Takahashi, Nakamasa Inoue, Rio Yokota, Ryo Nakamura, Hirokatsu Kataoka, Eisaku Maeda
2024	—	`conf`	Rethinking Image Super-Resolution from Training Data Perspectives. ECCV (17) Go Ohtani, Ryu Tadokoro, Ryosuke Yamada, Yuki M. Asano, Iro Laina, Christian Rupprecht, Nakamasa Inoue, Rio Yokota, Hirokatsu Kataoka, Yoshimitsu Aoki
2024	J	`jnl`	Rethinking Image Super-Resolution from Training Data Perspectives. CoRR Go Ohtani, Ryu Tadokoro, Ryosuke Yamada, Yuki M. Asano, Iro Laina, Christian Rupprecht, Nakamasa Inoue, Rio Yokota, Hirokatsu Kataoka, Yoshimitsu Aoki
2024	—	`conf`	Scaling Backwards: Minimal Synthetic Pre-Training? ECCV (15) Ryo Nakamura, Ryu Tadokoro, Ryosuke Yamada, Yuki M. Asano, Iro Laina, Christian Rupprecht, Nakamasa Inoue, Rio Yokota, Hirokatsu Kataoka
2024	J	`jnl`	Scaling Backwards: Minimal Synthetic Pre-training? CoRR Ryo Nakamura, Ryu Tadokoro, Ryosuke Yamada, Yuki M. Asano, Iro Laina, Christian Rupprecht, Nakamasa Inoue, Rio Yokota, Hirokatsu Kataoka
2024	J	`jnl`	SuperGCN: General and Scalable Framework for GCN Training on CPU-powered Supercomputers. CoRR Chen Zhuang, Peng Chen, Xin Liu, Rio Yokota, Nikoli Dryden, Toshio Endo, Satoshi Matsuoka, Mohamed Wahib
2024	A*	`conf`	Variational Learning is Effective for Large Deep Networks. ICML Yuesong Shen, Nico Daheim, Bai Cong, Peter Nickl, Gian Maria Marconi, Clement Bazan, Rio Yokota, Iryna Gurevych, Daniel Cremers, Mohammad Emtiyaz Khan, Thomas Möllenhoff
2024	J	`jnl`	Variational Learning is Effective for Large Deep Networks. CoRR Yuesong Shen, Nico Daheim, Bai Cong, Peter Nickl, Gian Maria Marconi, Clement Bazan, Rio Yokota, Iryna Gurevych, Daniel Cremers, Mohammad Emtiyaz Khan, Thomas Möllenhoff
2024	J	`jnl`	Variational Low-Rank Adaptation Using IVON. CoRR Bai Cong, Nico Daheim, Yuesong Shen, Daniel Cremers, Rio Yokota, Mohammad Emtiyaz Khan, Thomas Möllenhoff
2024	—	`conf`	When Does Second-Order Optimization Speed Up Training? Tiny Papers @ ICLR Satoki Ishikawa, Rio Yokota
2024	J	`jnl`	Why We Build Local Large Language Models: An Observational Analysis from 35 Japanese and Multilingual LLMs. CoRR Koshiro Saito, Sakae Mizuki, Masanari Ohi, Taishi Nakamura, Taihei Shiotani, Koki Maeda, Youmi Ma, Kakeru Hattori, Kazuki Fujii, Takumi Okamoto, Shigeki Ishida, Hiroya Takamura, Rio Yokota, Naoaki Okazaki
2023	J	`jnl`	ASDL: A Unified Interface for Gradient Preconditioning in PyTorch. CoRR Kazuki Osawa, Satoki Ishikawa, Rio Yokota, Shigang Li, Torsten Hoefler
2023	J	`jnl`	Cache Optimization and Performance Modeling of Batched, Small, and Rectangular Matrix Multiplication on Intel, AMD, and Fujitsu Processors. CoRR Sameer Deshmukh, Rio Yokota, George Bosilca
2023	J	`jnl`	Cache Optimization and Performance Modeling of Batched, Small, and Rectangular Matrix Multiplication on Intel, AMD, and Fujitsu Processors. ACM Trans. Math. Softw. Sameer Deshmukh, Rio Yokota, George Bosilca
2023	J	`jnl`	Computing the k-th Eigenvalue of Symmetric H CoRR M. Ridwan Apriansyah, Rio Yokota
2023	B	`conf`	Computing the k-th Eigenvalue of Symmetric H2-Matrices. ICPP M. Ridwan Apriansyah, Rio Yokota
2023	J	`jnl`	DGEMM on Integer Matrix Multiplication Unit. CoRR Hiroyuki Ootomo, Katsuhisa Ozaki, Rio Yokota
2023	J	`jnl`	Empirical Study on Optimizer Selection for Out-of-Distribution Generalization. Trans. Mach. Learn. Res. Hiroki Naganuma, Kartik Ahuja, Shiro Takagi, Tetsuya Motokawa, Rio Yokota, Kohta Ishikawa, Ikuro Sato, Ioannis Mitliagkas
2023	B	`conf`	Fast Symmetric Eigenvalue Decomposition via WY Representation on Tensor Core. PPoPP Shaoshuai Zhang, Ruchi Shah, Hiroyuki Ootomo, Rio Yokota, Panruo Wu
2023	J	`jnl`	Improving Continual Learning by Accurate Gradient Reconstructions of the Past. Trans. Mach. Learn. Res. Erik A. Daxberger, Siddharth Swaroop, Kazuki Osawa, Rio Yokota, Richard E. Turner, José Miguel Hernández-Lobato, Mohammad Emtiyaz Khan
2023	—	`conf`	Mixed-Precision Random Projection for RandNLA on Tensor Cores. PASC Hiroyuki Ootomo, Rio Yokota
2023	J	`jnl`	Mixed-Precision Random Projection for RandNLA on Tensor Cores. CoRR Hiroyuki Ootomo, Rio Yokota
2023	B	`conf`	O(N) distributed direct factorization of structured dense matrices using runtime systems. ICPP Sameer Deshmukh, Rio Yokota, George Bosilca, Qianxiang Ma
2023	J	`jnl`	O(N) distributed direct factorization of structured dense matrices using runtime systems. CoRR Sameer Deshmukh, Qinxiang Ma, Rio Yokota, George Bosilca
2023	—	`conf`	Pixel-level Contrastive Learning of Driving Videos with Optical Flow. CVPR Workshops Tomoya Takahashi, Shingo Yashima, Kohta Ishikawa, Ikuro Sato, Rio Yokota
2023	A*	`conf`	Pre-training Vision Transformers with Very Limited Synthesized Images. ICCV Ryo Nakamura, Hirokatsu Kataoka, Sora Takashima, Edgar Josafat Martinez-Noriega, Rio Yokota, Nakamasa Inoue
2023	J	`jnl`	Pre-training Vision Transformers with Very Limited Synthesized Images. CoRR Ryo Nakamura, Hirokatsu Kataoka, Sora Takashima, Edgar Josafat Martinez-Noriega, Rio Yokota, Nakamasa Inoue
2023	C	`conf`	Quantum Circuit Simulation by SGEMM Emulation on Tensor Cores and Automatic Precision Selection. ISC Hiroyuki Ootomo, Hidetaka Manabe, Kenji Harada, Rio Yokota
2023	J	`jnl`	Quantum Circuit Simulation by SGEMM Emulation on Tensor Cores and Automatic Precision Selection. CoRR Hiroyuki Ootomo, Hidetaka Manabe, Kenji Harada, Rio Yokota
2023	C	`conf`	Reducing shared memory footprint to leverage high throughput on Tensor Cores and its flexible API extension library. HPC Asia Hiroyuki Ootomo, Rio Yokota
2023	J	`jnl`	Reducing shared memory footprint to leverage high throughput on Tensor Cores and its flexible API extension library. CoRR Hiroyuki Ootomo, Rio Yokota
2023	A*	`conf`	SegRCDB: Semantic Segmentation via Formula-Driven Supervised Learning. ICCV Risa Shinoda, Ryo Hayamizu, Kodai Nakashima, Nakamasa Inoue, Rio Yokota, Hirokatsu Kataoka
2023	J	`jnl`	SegRCDB: Semantic Segmentation via Formula-Driven Supervised Learning. CoRR Risa Shinoda, Ryo Hayamizu, Kodai Nakashima, Nakamasa Inoue, Rio Yokota, Hirokatsu Kataoka
2023	J	`jnl`	The 2023 Society for Industrial and Applied Mathematics Conference on Computational Science and Engineering. Comput. Sci. Eng. Damian W. I. Rouson, Konrad Hinsen, Jeffrey C. Carver, Irina Tezaur, John Shalf, Rio Yokota, Anshu Dubey
2023	—	`conf`	Towards real-time formula driven dataset feed for large scale deep learning training. High Performance Computing for Imaging Edgar Josafat Martinez-Noriega, Rio Yokota
2023	A*	`conf`	Visual Atoms: Pre-Training Vision Transformers with Sinusoidal Waves. CVPR Sora Takashima, Ryo Hayamizu, Nakamasa Inoue, Hirokatsu Kataoka, Rio Yokota
2023	J	`jnl`	Visual Atoms: Pre-training Vision Transformers with Sinusoidal Waves. CoRR Sora Takashima, Ryo Hayamizu, Nakamasa Inoue, Hirokatsu Kataoka, Rio Yokota
2022	J	`jnl`	Empirical Study on Optimizer Selection for Out-of-Distribution Generalization. CoRR Hiroki Naganuma, Kartik Ahuja, Shiro Takagi, Tetsuya Motokawa, Rio Yokota, Kohta Ishikawa, Ikuro Sato, Ioannis Mitliagkas
2022	—	`conf`	Informative Sample-Aware Proxy for Deep Metric Learning. MMAsia Aoyu Li, Ikuro Sato, Kohta Ishikawa, Rei Kawakami, Rio Yokota
2022	J	`jnl`	Informative Sample-Aware Proxy for Deep Metric Learning. CoRR Aoyu Li, Ikuro Sato, Kohta Ishikawa, Rei Kawakami, Rio Yokota
2022	A*	`conf`	OPIRL: Sample Efficient Off-Policy Inverse Reinforcement Learning via Distribution Matching. ICRA Hana Hoshino, Kei Ota, Asako Kanezaki, Rio Yokota
2022	J	`jnl`	Parallel QR Factorization of Block Low-Rank Matrices. CoRR M. Ridwan Apriansyah, Rio Yokota
2022	J	`jnl`	Parallel QR Factorization of Block Low-rank Matrices. ACM Trans. Math. Softw. M. Ridwan Apriansyah, Rio Yokota
2022	C	`conf`	QR Factorization of Block Low-Rank Matrices on Multi-instance GPU. PDCAT Satoshi Ohshima, Akihiro Ida, Rio Yokota, Ichitaro Yamazaki
2022	J	`jnl`	Recovering single precision accuracy from Tensor Cores while surpassing the FP32 theoretical peak performance. CoRR Hiroyuki Ootomo, Rio Yokota
2022	J	`jnl`	Recovering single precision accuracy from Tensor Cores while surpassing the FP32 theoretical peak performance. Int. J. High Perform. Comput. Appl. Hiroyuki Ootomo, Rio Yokota
2022	A*	`conf`	Replacing Labeled Real-image Datasets with Auto-generated Contours. CVPR Hirokatsu Kataoka, Ryo Hayamizu, Ryosuke Yamada, Kodai Nakashima, Sora Takashima, Xinyu Zhang, Edgar Josafat Martinez-Noriega, Nakamasa Inoue, Rio Yokota
2022	J	`jnl`	Replacing Labeled Real-image Datasets with Auto-generated Contours. CoRR Hirokatsu Kataoka, Ryo Hayamizu, Ryosuke Yamada, Kodai Nakashima, Sora Takashima, Xinyu Zhang, Edgar Josafat Martinez-Noriega, Nakamasa Inoue, Rio Yokota
2022	J	`jnl`	Scalable Linear Time Dense Direct Solver for 3-D Problems Without Trailing Sub-Matrix Dependencies. CoRR Qianxiang Ma, Sameer Deshmukh, Rio Yokota
2022	A	`conf`	Scalable Linear Time Dense Direct Solver for 3-D Problems without Trailing Sub-Matrix Dependencies. SC Qianxiang Ma, Sameer Deshmukh, Rio Yokota
2022	J	`jnl`	Scalable and Practical Natural Gradient for Large-Scale Deep Learning. IEEE Trans. Pattern Anal. Mach. Intell. Kazuki Osawa, Yohei Tsuji, Yuichiro Ueno, Akira Naruse, Chuan-Sheng Foo, Rio Yokota
2021	J	`jnl`	ExaFMM: a high-performance fast multipole method library with C++ and Python interfaces. J. Open Source Softw. Tingyu Wang, Rio Yokota, Lorena A. Barba
2021	J	`jnl`	OPIRL: Sample Efficient Off-Policy Inverse Reinforcement Learning via Distribution Matching. CoRR Hana Hoshino, Kei Ota, Asako Kanezaki, Rio Yokota
2021	A*	`conf`	RePOSE: Fast 6D Object Pose Refinement via Deep Texture Rendering. ICCV Shun Iwase, Xingyu Liu, Rawal Khirodkar, Rio Yokota, Kris M. Kitani
2021	J	`jnl`	RePOSE: Real-Time Iterative Rendering and Refinement for 6D Object Pose Estimation. CoRR Shun Iwase, Xingyu Liu, Rawal Khirodkar, Rio Yokota, Kris M. Kitani
2020	C	`conf`	Effect of Mixed Precision Computing on H-Matrix Vector Multiplication in BEM Analysis. HPC Asia Rise Ooi, Takeshi Iwashita, Takeshi Fukaya, Akihiro Ida, Rio Yokota
2020	J	`jnl`	Epipolar-Guided Deep Object Matching for Scene Change Detection. CoRR Kento Doi, Ryuhei Hamaguchi, Shun Iwase, Rio Yokota, Yutaka Matsuo, Ken Sakurada
2020	A*	`conf`	Rich Information is Affordable: A Systematic Performance Analysis of Second-order Optimization Using K-FAC. KDD Yuichiro Ueno, Kazuki Osawa, Yohei Tsuji, Akira Naruse, Rio Yokota
2020	J	`jnl`	Scalable and Practical Natural Gradient for Large-Scale Deep Learning. CoRR Kazuki Osawa, Yohei Tsuji, Yuichiro Ueno, Akira Naruse, Chuan-Sheng Foo, Rio Yokota
2019	B	`conf`	A Performance Improvement Approach for Second-Order Optimization in Large Mini-batch Training. CCGRID Hiroki Naganuma, Rio Yokota
2019	J	`jnl`	Distributed-memory lattice H-matrix factorization. Int. J. High Perform. Comput. Appl. Ichitaro Yamazaki, Akihiro Ida, Rio Yokota, Jack J. Dongarra
2019	J	`jnl`	Effect of Mixed Precision Computing on H-Matrix Vector Multiplication in BEM Analysis. CoRR Rise Ooi, Takeshi Iwashita, Takeshi Fukaya, Akihiro Ida, Rio Yokota
2019	B	`conf`	Exhaustive Study of Hierarchical AllReduce Patterns for Large Messages Between GPUs. CCGRID Yuichiro Ueno, Rio Yokota
2019	J	`jnl`	Extreme Scale FMM-Accelerated Boundary Integral Equation Solver for Wave Scattering. SIAM J. Sci. Comput. Mustafa Abdul Jabbar, Mohammed A. Al Farhan, Noha Al-Harthi, Rui Chen, Rio Yokota, Hakan Bagci, David E. Keyes
2019	A*	`conf`	Large-Scale Distributed Second-Order Optimization Using Kronecker-Factored Approximate Curvature for Deep Convolutional Neural Networks. CVPR Kazuki Osawa, Yohei Tsuji, Yuichiro Ueno, Akira Naruse, Rio Yokota, Satoshi Matsuoka
2019	—	`conf`	Optimization of Numerous Small Dense-Matrix-Vector Multiplications in H-Matrix Arithmetic on GPU. MCSoC Satoshi Ohshima, Ichitaro Yamazaki, Akihiro Ida, Rio Yokota
2019	—	`conf`	Performance Optimizations and Analysis of Distributed Deep Learning with Approximated Second-Order Optimization Method. ICPP Workshops Yohei Tsuji, Kazuki Osawa, Yuichiro Ueno, Akira Naruse, Rio Yokota, Satoshi Matsuoka
2019	A*	`conf`	Practical Deep Learning with Bayesian Principles. NeurIPS Kazuki Osawa, Siddharth Swaroop, Mohammad Emtiyaz Khan, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota
2019	J	`jnl`	Practical Deep Learning with Bayesian Principles. CoRR Kazuki Osawa, Siddharth Swaroop, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota, Mohammad Emtiyaz Khan
2019	J	`jnl`	QR Factorization of Block Low-rank Matrices with Weak Admissibility Condition. J. Inf. Process. Akihiro Ida, Hiroshi Nakashima, Tasuku Hiraishi, Ichitaro Yamazaki, Rio Yokota, Takeshi Iwashita
2018	J	`jnl`	Extreme Scale FMM-Accelerated Boundary Integral Equation Solver for Wave Scattering. CoRR Mustafa Abdul Jabbar, Mohammed A. Al Farhan, Noha Al-Harthi, Rui Chen, Rio Yokota, Hakan Bagci, David E. Keyes
2018	J	`jnl`	Fast multipole preconditioners for sparse matrices arising from elliptic equations. Comput. Vis. Sci. Huda Ibeid, Rio Yokota, Jennifer Pestana, David E. Keyes
2018	C	`ed.`	High Performance Computing - 33rd International Conference, ISC High Performance 2018, Frankfurt, Germany, June 24-28, 2018, Proceedings ISC Rio Yokota, Michèle Weiland, David E. Keyes, Carsten Trinitis
2018	—	`ed.`	High Performance Computing - ISC High Performance 2018 International Workshops, Frankfurt/Main, Germany, June 28, 2018, Revised Selected Papers ISC Workshops Rio Yokota, Michèle Weiland, John Shalf, Sadaf R. Alam
2018	—	`conf`	Optimization of Hierarchical Matrix Computation on GPU. SCFA Satoshi Ohshima, Ichitaro Yamazaki, Akihiro Ida, Rio Yokota
2018	A	`conf`	Performance of Hierarchical-matrix BiCGStab Solver on GPU Clusters. IPDPS Ichitaro Yamazaki, Ahmad Abdelfattah, Akihiro Ida, Satoshi Ohshima, Stanimire Tomov, Rio Yokota, Jack J. Dongarra
2018	J	`jnl`	Second-order Optimization Method for Large Mini-batch: Training ResNet-50 on ImageNet in 35 Epochs. CoRR Kazuki Osawa, Yohei Tsuji, Yuichiro Ueno, Akira Naruse, Rio Yokota, Satoshi Matsuoka
2018	—	`ed.`	Supercomputing Frontiers - 4th Asian Conference, SCFA 2018, Singapore, March 26-29, 2018, Proceedings SCFA Rio Yokota, Weigang Wu
2017	—	`conf`	Accelerating Matrix Multiplication in Deep Learning by Using Low-Rank Approximation. HPCS Kazuki Osawa, Akira Sekiya, Hiroki Naganuma, Rio Yokota
2017	C	`conf`	Communication Reducing Algorithms for Distributed Hierarchical N-Body Problems with Boundary Distributions. ISC Mustafa Abdul Jabbar, George S. Markomanolis, Huda Ibeid, Rio Yokota, David E. Keyes
2017	J	`jnl`	Communication Reducing Algorithms for Distributed Hierarchical N-Body Problems with Boundary Distributions. CoRR Mustafa Abdul Jabbar, George S. Markomanolis, Huda Ibeid, Rio Yokota, David E. Keyes
2017	—	`conf`	Evaluating the Compression Efficiency of the Filters in Convolutional Neural Networks. ICANN (2) Kazuki Osawa, Rio Yokota
2017	C	`ed.`	High Performance Computing - 32nd International Conference, ISC High Performance 2017, Frankfurt, Germany, June 18-22, 2017, Proceedings ISC Julian M. Kunkel, Rio Yokota, Pavan Balaji, David E. Keyes
2017	—	`ed.`	High Performance Computing - ISC High Performance 2017 International Workshops, DRBSD, ExaComm, HCPM, HPC-IODC, IWOPH, IXPUG, P^3MA, VHPC, Visualization at Scale, WOPSSS, Frankfurt, Germany, June 18-22, 2017, Revised Selected Papers ISC Workshops Julian M. Kunkel, Rio Yokota, Michela Taufer, John Shalf
2017	—	`conf`	Performance Evaluation of Computation and Communication Kernels of the Fast Multipole Method on Intel Manycore Architecture. Euro-Par Mustafa Abdul Jabbar, Mohammed A. Al Farhan, Rio Yokota, David E. Keyes
2016	J	`jnl`	A Matrix-free Preconditioner for the Helmholtz Equation based on the Fast Multipole Method. CoRR Huda Ibeid, Rio Yokota, David E. Keyes
2016	J	`jnl`	A performance model for the communication in fast multipole methods on high-performance computing platforms. Int. J. High Perform. Comput. Appl. Huda Ibeid, Rio Yokota, David E. Keyes
2016	J	`jnl`	Fast Multipole Method as a Matrix-Free Hierarchical Low-Rank Approximation. CoRR Rio Yokota, Huda Ibeid, David E. Keyes
2016	—	`conf`	Scaling FMM with Data-Driven OpenMP Tasks on Multicore Architectures. IWOMP Abdelhalim Amer, Satoshi Matsuoka, Miquel Pericàs, Naoya Maruyama, Kenjiro Taura, Rio Yokota, Pavan Balaji
2016	B	`conf`	Tapas: An Implicitly Parallel Programming Framework for Hierarchical N-Body Algorithms. ICPADS Keisuke Fukuda, Motohiko Matsuda, Naoya Maruyama, Rio Yokota, Kenjiro Taura, Satoshi Matsuoka
2014	J	`jnl`	A Performance Model for the Communication in Fast Multipole Methods on HPC Platforms. CoRR Huda Ibeid, Rio Yokota, David E. Keyes
2014	J	`jnl`	Asynchronous Execution of the Fast Multipole Method Using Charm++. CoRR Mustafa Abdul Jabbar, Rio Yokota, David E. Keyes
2014	J	`jnl`	Communication Complexity of the Fast Multipole Method and its Algebraic Variants. CoRR Rio Yokota, George Turkiyyah, David E. Keyes
2014	J	`jnl`	Communication Complexity of the Fast Multipole Method and its Algebraic Variants. Supercomput. Front. Innov. Rio Yokota, George Turkiyyah, David E. Keyes
2014	J	`jnl`	Data-driven execution of fast multipole methods. Concurr. Comput. Pract. Exp. Hatem Ltaief, Rio Yokota
2014	J	`jnl`	Petascale molecular dynamics simulation using the fast multipole method on K computer. Comput. Phys. Commun. Yousuke Ohno, Rio Yokota, Hiroshi Koyama, Gentaro Morimoto, Aki Hasegawa, Gen Masumoto, Noriaki Okimoto, Yoshinori Hirano, Huda Ibeid, Tetsu Narumi, Makoto Taiji
2014	—	`conf`	Scalable Fast Multipole Accelerated Vortex Methods. IPDPS Workshops Qi Hu, Nail A. Gumerov, Rio Yokota, Lorena A. Barba, Ramani Duraiswami
2013	J	`jnl`	Fast Multipole Preconditioners for Sparse Matrices Arising from Elliptic Equations. CoRR Rio Yokota, Jennifer Pestana, Huda Ibeid, David E. Keyes
2013	C	`conf`	Fork-Join and Data-Driven Execution Models on Multi-core Architectures: Case Study of the FMM. ISC Abdelhalim Amer, Naoya Maruyama, Miquel Pericàs, Kenjiro Taura, Rio Yokota, Satoshi Matsuoka
2013	J	`jnl`	Petascale turbulence simulation using a highly parallel fast multipole method on GPUs. Comput. Phys. Commun. Rio Yokota, Lorena A. Barba, Tetsu Narumi, Kenji Yasuoka
2012	—	`conf`	A Task Parallel Implementation of Fast Multipole Methods. SC Companion Kenjiro Taura, Jun Nakashima, Rio Yokota, Naoya Maruyama
2012	J	`jnl`	A tuned and scalable fast multipole method as a preeminent algorithm for exascale systems. Int. J. High Perform. Comput. Appl. Rio Yokota, Lorena A. Barba
2012	—	`conf`	Abstract: Scalable Fast Multipole Methods for Vortex Element Methods. SC Companion Qi Hu, Nail A. Gumerov, Rio Yokota, Lorena A. Barba, Ramani Duraiswami
2012	J	`jnl`	An FMM Based on Dual Tree Traversal for Many-core Architectures CoRR Rio Yokota
2012	J	`jnl`	Data-Driven Execution of Fast Multipole Methods CoRR Hatem Ltaief, Rio Yokota
2012	J	`jnl`	Hierarchical N-body Simulations with Autotuning for Heterogeneous Systems. Comput. Sci. Eng. Rio Yokota, Lorena A. Barba
2012	—	`conf`	Poster: Scalable Fast Multipole Methods for Vortex Element Methods. SC Companion Qi Hu, Nail A. Gumerov, Rio Yokota, Lorena A. Barba, Ramani Duraiswami
2012	C	`conf`	Scalable Force Directed Graph Layout Algorithms Using Fast Multipole Methods. ISPDC Enas Yunis, Rio Yokota, Aron J. Ahmadia
2011	J	`jnl`	A Tuned and Scalable Fast Multipole Method as a Preeminent Algorithm for Exascale Systems CoRR Rio Yokota, Lorena A. Barba
2011	J	`jnl`	Biomolecular electrostatics using a fast multipole BEM on up to 512 gpus and a billion unknowns. Comput. Phys. Commun. Rio Yokota, Jaydeep P. Bardhan, Matthew G. Knepley, Lorena A. Barba, Tsuyoshi Hamada
2011	J	`jnl`	Fast Multipole Method vs. Spectral Method for the Simulation of Isotropic Turbulence on GPUs CoRR Rio Yokota, Lorena A. Barba
2011	J	`jnl`	Fast N-body Simulations on GPUs CoRR Rio Yokota, Lorena A. Barba
2011	J	`jnl`	Petascale turbulence simulation using a highly parallel fast multipole method CoRR Rio Yokota, Tetsu Narumi, Lorena A. Barba, Kenji Yasuoka
2010	J	`jnl`	Biomolecular Electrostatics Simulation by an FMM-based BEM on 512 GPUs CoRR Rio Yokota, Tsuyoshi Hamada, Jaydeep P. Bardhan, Matthew G. Knepley, Lorena A. Barba
2009	A	`conf`	42 TFlops hierarchical SC Tsuyoshi Hamada, Tetsu Narumi, Rio Yokota, Kenji Yasuoka, Keigo Nitadori, Makoto Taiji
2009	J	`jnl`	Fast multipole methods on a cluster of GPUs for the meshless simulation of turbulence. Comput. Phys. Commun. Rio Yokota, Tetsu Narumi, Ryuji Sakamaki, Shun Kameoka, Shinnosuke Obi, Kenji Yasuoka
2009	J	`jnl`	PetRBF--A parallel O(N) algorithm for radial basis function interpolation CoRR Rio Yokota, Lorena A. Barba, Matthew G. Knepley
2007	J	`jnl`	Calculation of isotropic turbulence using a pure Lagrangian vortex method. J. Comput. Phys. Rio Yokota, Tarun Kumar Sheel, Shinnosuke Obi