Calculation of isotropic turbulence using a pure Lagrangian vortex method. - Rio Yokota; Tarun Kumar Sheel; Shinnosuke Obi (J. Comput. Phys., 2007)

42 TFlops hierarchical N-body simulations on GPUs with applications in both astrophysics and turbulence. - Tsuyoshi Hamada; Tetsu Narumi; Rio Yokota; Kenji Yasuoka; Keigo Nitadori; Makoto Taiji (SC, 2009)

PetRBF–A parallel O(N) algorithm for radial basis function interpolation - Rio Yokota; Lorena A. Barba; Matthew G. Knepley (CoRR, 2009)

Fast multipole methods on a cluster of GPUs for the meshless simulation of turbulence. - Rio Yokota; Tetsu Narumi; Ryuji Sakamaki; Shun Kameoka; Shinnosuke Obi; Kenji Yasuoka (Comput. Phys. Commun., 2009)

Biomolecular Electrostatics Simulation by an FMM-based BEM on 512 GPUs - Rio Yokota; Tsuyoshi Hamada; Jaydeep P. Bardhan; Matthew G. Knepley; Lorena A. Barba (CoRR, 2010)

Petascale turbulence simulation using a highly parallel fast multipole method - Rio Yokota; Tetsu Narumi; Lorena A. Barba; Kenji Yasuoka (CoRR, 2011)

Fast N-body Simulations on GPUs - Rio Yokota; Lorena A. Barba (CoRR, 2011)

Fast Multipole Method vs. Spectral Method for the Simulation of Isotropic Turbulence on GPUs - Rio Yokota; Lorena A. Barba (CoRR, 2011)

Biomolecular electrostatics using a fast multipole BEM on up to 512 gpus and a billion unknowns. - Rio Yokota; Jaydeep P. Bardhan; Matthew G. Knepley; Lorena A. Barba; Tsuyoshi Hamada (Comput. Phys. Commun., 2011)

Scalable Force Directed Graph Layout Algorithms Using Fast Multipole Methods. - Enas Yunis; Rio Yokota; Aron J. Ahmadia (ISPDC, 2012)

Abstract: Scalable Fast Multipole Methods for Vortex Element Methods. - Qi Hu; Nail A. Gumerov; Rio Yokota; Lorena A. Barba; Ramani Duraiswami (SC Companion, 2012)

Poster: Scalable Fast Multipole Methods for Vortex Element Methods. - Qi Hu; Nail A. Gumerov; Rio Yokota; Lorena A. Barba; Ramani Duraiswami (SC Companion, 2012)

A Task Parallel Implementation of Fast Multipole Methods. - Kenjiro Taura; Jun Nakashima; Rio Yokota; Naoya Maruyama (SC Companion, 2012)

An FMM Based on Dual Tree Traversal for Many-core Architectures - Rio Yokota (CoRR, 2012)

Hierarchical N-body Simulations with Autotuning for Heterogeneous Systems. - Rio Yokota; Lorena A. Barba (Comput. Sci. Eng., 2012)

A tuned and scalable fast multipole method as a preeminent algorithm for exascale systems. - Rio Yokota; Lorena A. Barba (Int. J. High Perform. Comput. Appl., 2012)

Fork-Join and Data-Driven Execution Models on Multi-core Architectures: Case Study of the FMM. - Abdelhalim Amer; Naoya Maruyama; Miquel Pericàs; Kenjiro Taura; Rio Yokota; Satoshi Matsuoka (ISC, 2013)

Petascale turbulence simulation using a highly parallel fast multipole method on GPUs. - Rio Yokota; Lorena A. Barba; Tetsu Narumi; Kenji Yasuoka (Comput. Phys. Commun., 2013)

Scalable Fast Multipole Accelerated Vortex Methods. - Qi Hu; Nail A. Gumerov; Rio Yokota; Lorena A. Barba; Ramani Duraiswami (IPDPS Workshops, 2014)

Data-driven execution of fast multipole methods. - Hatem Ltaief; Rio Yokota (Concurr. Comput. Pract. Exp., 2014)

Asynchronous Execution of the Fast Multipole Method Using Charm++. - Mustafa Abdul Jabbar; Rio Yokota; David E. Keyes (CoRR, 2014)

A Performance Model for the Communication in Fast Multipole Methods on HPC Platforms. - Huda Ibeid; Rio Yokota; David E. Keyes (CoRR, 2014)

Petascale molecular dynamics simulation using the fast multipole method on K computer. - Yousuke Ohno; Rio Yokota; Hiroshi Koyama; Gentaro Morimoto; Aki Hasegawa; Gen Masumoto; Noriaki Okimoto; Yoshinori Hirano; Huda Ibeid; Tetsu Narumi; Makoto Taiji (Comput. Phys. Commun., 2014)

Communication Complexity of the Fast Multipole Method and its Algebraic Variants. - Rio Yokota; George Turkiyyah; David E. Keyes (Supercomput. Front. Innov., 2014)

Tapas: An Implicitly Parallel Programming Framework for Hierarchical N-Body Algorithms. - Keisuke Fukuda; Motohiko Matsuda; Naoya Maruyama; Rio Yokota; Kenjiro Taura; Satoshi Matsuoka (ICPADS, 2016)

Scaling FMM with Data-Driven OpenMP Tasks on Multicore Architectures. - Abdelhalim Amer; Satoshi Matsuoka; Miquel Pericàs; Naoya Maruyama; Kenjiro Taura; Rio Yokota; Pavan Balaji (IWOMP, 2016)

A Matrix-free Preconditioner for the Helmholtz Equation based on the Fast Multipole Method. - Huda Ibeid; Rio Yokota; David E. Keyes (CoRR, 2016)

Fast Multipole Method as a Matrix-Free Hierarchical Low-Rank Approximation. - Rio Yokota; Huda Ibeid; David E. Keyes (CoRR, 2016)

A performance model for the communication in fast multipole methods on high-performance computing platforms. - Huda Ibeid; Rio Yokota; David E. Keyes (Int. J. High Perform. Comput. Appl., 2016)

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 (Euro-Par, 2017)

Evaluating the Compression Efficiency of the Filters in Convolutional Neural Networks. - Kazuki Osawa; Rio Yokota (ICANN (2), 2017)

Accelerating Matrix Multiplication in Deep Learning by Using Low-Rank Approximation. - Kazuki Osawa; Akira Sekiya; Hiroki Naganuma; Rio Yokota (HPCS, 2017)

Communication Reducing Algorithms for Distributed Hierarchical N-Body Problems with Boundary Distributions. - Mustafa Abdul Jabbar; George S. Markomanolis; Huda Ibeid; Rio Yokota; David E. Keyes (ISC, 2017)

Performance of Hierarchical-matrix BiCGStab Solver on GPU Clusters. - Ichitaro Yamazaki; Ahmad Abdelfattah; Akihiro Ida; Satoshi Ohshima; Stanimire Tomov; Rio Yokota; Jack J. Dongarra (IPDPS, 2018)

Optimization of Hierarchical Matrix Computation on GPU. - Satoshi Ohshima; Ichitaro Yamazaki; Akihiro Ida; Rio Yokota (SCFA, 2018)

Fast multipole preconditioners for sparse matrices arising from elliptic equations. - Huda Ibeid; Rio Yokota; Jennifer Pestana; David E. Keyes (Comput. Vis. Sci., 2018)

Second-order Optimization Method for Large Mini-batch: Training ResNet-50 on ImageNet in 35 Epochs. - Kazuki Osawa; Yohei Tsuji; Yuichiro Ueno; Akira Naruse; Rio Yokota; Satoshi Matsuoka (CoRR, 2018)

A Performance Improvement Approach for Second-Order Optimization in Large Mini-batch Training. - Hiroki Naganuma; Rio Yokota (CCGRID, 2019)

Exhaustive Study of Hierarchical AllReduce Patterns for Large Messages Between GPUs. - Yuichiro Ueno; Rio Yokota (CCGRID, 2019)

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 (CVPR, 2019)

Performance Optimizations and Analysis of Distributed Deep Learning with Approximated Second-Order Optimization Method. - Yohei Tsuji; Kazuki Osawa; Yuichiro Ueno; Akira Naruse; Rio Yokota; Satoshi Matsuoka (ICPP Workshops, 2019)

Optimization of Numerous Small Dense-Matrix-Vector Multiplications in H-Matrix Arithmetic on GPU. - Satoshi Ohshima; Ichitaro Yamazaki; Akihiro Ida; Rio Yokota (MCSoC, 2019)

Practical Deep Learning with Bayesian Principles. - Kazuki Osawa; Siddharth Swaroop; Mohammad Emtiyaz Khan; Anirudh Jain; Runa Eschenhagen; Richard E. Turner; Rio Yokota (NeurIPS, 2019)

Distributed-memory lattice H-matrix factorization. - Ichitaro Yamazaki; Akihiro Ida; Rio Yokota; Jack J. Dongarra (Int. J. High Perform. Comput. Appl., 2019)

QR Factorization of Block Low-rank Matrices with Weak Admissibility Condition. - Akihiro Ida; Hiroshi Nakashima; Tasuku Hiraishi; Ichitaro Yamazaki; Rio Yokota; Takeshi Iwashita (J. Inf. Process., 2019)

Extreme Scale FMM-Accelerated Boundary Integral Equation Solver for Wave Scattering. - Mustafa Abdul Jabbar; Mohammed A. Al Farhan; Noha Al-Harthi; Rui Chen; Rio Yokota; Hakan Bagci 001; David E. Keyes (SIAM J. Sci. Comput., 2019)

Effect of Mixed Precision Computing on H-Matrix Vector Multiplication in BEM Analysis. - Rise Ooi; Takeshi Iwashita; Takeshi Fukaya; Akihiro Ida; Rio Yokota (HPC Asia, 2020)

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 (KDD, 2020)

Epipolar-Guided Deep Object Matching for Scene Change Detection. - Kento Doi; Ryuhei Hamaguchi; Shun Iwase; Rio Yokota; Yutaka Matsuo; Ken Sakurada (CoRR, 2020)

RePOSE: Fast 6D Object Pose Refinement via Deep Texture Rendering. - Shun Iwase; Xingyu Liu 001; Rawal Khirodkar; Rio Yokota; Kris M. Kitani (ICCV, 2021)

RePOSE: Real-Time Iterative Rendering and Refinement for 6D Object Pose Estimation. - Shun Iwase; Xingyu Liu 001; Rawal Khirodkar; Rio Yokota; Kris M. Kitani (CoRR, 2021)

ExaFMM: a high-performance fast multipole method library with C++ and Python interfaces. - Tingyu Wang; Rio Yokota; Lorena A. Barba (J. Open Source Softw., 2021)

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 (CVPR, 2022)

OPIRL: Sample Efficient Off-Policy Inverse Reinforcement Learning via Distribution Matching. - Hana Hoshino; Kei Ota; Asako Kanezaki; Rio Yokota (ICRA, 2022)

Informative Sample-Aware Proxy for Deep Metric Learning. - Aoyu Li; Ikuro Sato; Kohta Ishikawa; Rei Kawakami; Rio Yokota (MMAsia, 2022)

QR Factorization of Block Low-Rank Matrices on Multi-instance GPU. - Satoshi Ohshima; Akihiro Ida; Rio Yokota; Ichitaro Yamazaki (PDCAT, 2022)

Scalable Linear Time Dense Direct Solver for 3-D Problems without Trailing Sub-Matrix Dependencies. - Qianxiang Ma; Sameer Deshmukh; Rio Yokota (SC, 2022)

Recovering single precision accuracy from Tensor Cores while surpassing the FP32 theoretical peak performance. - Hiroyuki Ootomo; Rio Yokota (Int. J. High Perform. Comput. Appl., 2022)

Scalable and Practical Natural Gradient for Large-Scale Deep Learning. - Kazuki Osawa; Yohei Tsuji; Yuichiro Ueno; Akira Naruse; Chuan-Sheng Foo; Rio Yokota (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Parallel QR Factorization of Block Low-rank Matrices. - M. Ridwan Apriansyah; Rio Yokota (ACM Trans. Math. Softw., 2022)

Pixel-level Contrastive Learning of Driving Videos with Optical Flow. - Tomoya Takahashi; Shingo Yashima; Kohta Ishikawa; Ikuro Sato; Rio Yokota (CVPR Workshops, 2023)

Visual Atoms: Pre-Training Vision Transformers with Sinusoidal Waves. - Sora Takashima; Ryo Hayamizu; Nakamasa Inoue; Hirokatsu Kataoka; Rio Yokota (CVPR, 2023)

Towards real-time formula driven dataset feed for large scale deep learning training. - Edgar Josafat Martinez-Noriega; Rio Yokota (High Performance Computing for Imaging, 2023)

Reducing shared memory footprint to leverage high throughput on Tensor Cores and its flexible API extension library. - Hiroyuki Ootomo; Rio Yokota (HPC Asia, 2023)

Pre-training Vision Transformers with Very Limited Synthesized Images. - Ryo Nakamura; Hirokatsu Kataoka; Sora Takashima; Edgar Josafat Martinez-Noriega; Rio Yokota; Nakamasa Inoue (ICCV, 2023)

SegRCDB: Semantic Segmentation via Formula-Driven Supervised Learning. - Risa Shinoda; Ryo Hayamizu; Kodai Nakashima; Nakamasa Inoue; Rio Yokota; Hirokatsu Kataoka (ICCV, 2023)

Computing the k-th Eigenvalue of Symmetric H2-Matrices. - M. Ridwan Apriansyah; Rio Yokota (ICPP, 2023)

O(N) distributed direct factorization of structured dense matrices using runtime systems. - Sameer Deshmukh; Rio Yokota; George Bosilca; Qianxiang Ma (ICPP, 2023)

Mixed-Precision Random Projection for RandNLA on Tensor Cores. - Hiroyuki Ootomo; Rio Yokota (PASC, 2023)

Fast Symmetric Eigenvalue Decomposition via WY Representation on Tensor Core. - Shaoshuai Zhang; Ruchi Shah; Hiroyuki Ootomo; Rio Yokota; Panruo Wu (PPoPP, 2023)

Quantum Circuit Simulation by SGEMM Emulation on Tensor Cores and Automatic Precision Selection. - Hiroyuki Ootomo; Hidetaka Manabe; Kenji Harada; Rio Yokota (ISC, 2023)

ASDL: A Unified Interface for Gradient Preconditioning in PyTorch. - Kazuki Osawa; Satoki Ishikawa; Rio Yokota; Shigang Li 002; Torsten Hoefler (CoRR, 2023)

O(N) distributed direct factorization of structured dense matrices using runtime systems. - Sameer Deshmukh; Qinxiang Ma; Rio Yokota; George Bosilca (CoRR, 2023)

Computing the k-th Eigenvalue of Symmetric H2-Matrices. - M. Ridwan Apriansyah; Rio Yokota (CoRR, 2023)

The 2023 Society for Industrial and Applied Mathematics Conference on Computational Science and Engineering. - Damian W. I. Rouson; Konrad Hinsen; Jeffrey C. Carver; Irina Tezaur; John Shalf; Rio Yokota; Anshu Dubey (Comput. Sci. Eng., 2023)

Improving Continual Learning by Accurate Gradient Reconstructions of the Past. - Erik A. Daxberger; Siddharth Swaroop; Kazuki Osawa; Rio Yokota; Richard E. Turner; José Miguel Hernández-Lobato; Mohammad Emtiyaz Khan (Trans. Mach. Learn. Res., 2023)

Empirical Study on Optimizer Selection for Out-of-Distribution Generalization. - Hiroki Naganuma; Kartik Ahuja; Shiro Takagi; Tetsuya Motokawa; Rio Yokota; Kohta Ishikawa; Ikuro Sato; Ioannis Mitliagkas (Trans. Mach. Learn. Res., 2023)

Cache Optimization and Performance Modeling of Batched, Small, and Rectangular Matrix Multiplication on Intel, AMD, and Fujitsu Processors. - Sameer Deshmukh; Rio Yokota; George Bosilca (ACM Trans. Math. Softw., 2023)

Scaling Backwards: Minimal Synthetic Pre-Training? - Ryo Nakamura; Ryu Tadokoro; Ryosuke Yamada; Yuki M. Asano; Iro Laina; Christian Rupprecht 001; Nakamasa Inoue; Rio Yokota; Hirokatsu Kataoka (ECCV (15), 2024)

Rethinking Image Super-Resolution from Training Data Perspectives. - Go Ohtani; Ryu Tadokoro; Ryosuke Yamada; Yuki M. Asano; Iro Laina; Christian Rupprecht 001; Nakamasa Inoue; Rio Yokota; Hirokatsu Kataoka; Yoshimitsu Aoki (ECCV (17), 2024)

Formula-Supervised Visual-Geometric Pre-training. - Ryosuke Yamada; Kensho Hara; Hirokatsu Kataoka; Koshi Makihara; Nakamasa Inoue; Rio Yokota; Yutaka Satoh (ECCV (22), 2024)

When Does Second-Order Optimization Speed Up Training? - Satoki Ishikawa; Rio Yokota (Tiny Papers @ ICLR, 2024)

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 Möllenhoff (ICML, 2024)

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 (ICPR (25), 2024)

Aurora-M: The First Open Source Multilingual Language Model Red-teamed according to the U.S. Executive Order. - 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; et al. (CoRR, 2024)

Building a Large Japanese Web Corpus for Large Language Models. - Naoaki Okazaki; Kakeru Hattori; Hirai Shota; Hiroki Iida; Masanari Ohi; Kazuki Fujii; Taishi Nakamura; Mengsay Loem; Rio Yokota; Sakae Mizuki (CoRR, 2024)

Continual Pre-Training for Cross-Lingual LLM Adaptation: Enhancing Japanese Language Capabilities. - Kazuki Fujii; Taishi Nakamura; Mengsay Loem; Hiroki Iida; Masanari Ohi; Kakeru Hattori; Hirai Shota; Sakae Mizuki; Rio Yokota; Naoaki Okazaki (CoRR, 2024)

LLM-jp: A Cross-organizational Project for the Research and Development of Fully Open Japanese LLMs. - Akiko Aizawa; Eiji Aramaki; Bowen Chen; Fei Cheng 002; Hiroyuki Deguchi 002; Rintaro Enomoto; Kazuki Fujii; Kensuke Fukumoto; Takuya Fukushima; Namgi Han; Yuto Harada; Chikara Hashimoto; et al. (CoRR, 2024)

Variational Low-Rank Adaptation Using IVON. - Bai Cong; Nico Daheim; Yuesong Shen; Daniel Cremers; Rio Yokota; Mohammad Emtiyaz Khan; Thomas Möllenhoff (CoRR, 2024)

Accelerating Large Language Model Training with 4D Parallelism and Memory Consumption Estimator. - Kazuki Fujii; Kohei Watanabe; Rio Yokota (CoRR, 2024)

Balancing Speed and Stability: The Trade-offs of FP8 vs. BF16 Training in LLMs. - Kazuki Fujii; Taishi Nakamura; Rio Yokota (CoRR, 2024)

SuperGCN: General and Scalable Framework for GCN Training on CPU-powered Supercomputers. - Chen Zhuang; Peng Chen 035; Xin Liu 020; Rio Yokota; Nikoli Dryden; Toshio Endo; Satoshi Matsuoka; Mohamed Wahib (CoRR, 2024)

Lion Cub: Minimizing Communication Overhead in Distributed Lion. - Satoki Ishikawa; Tal Ben-Nun; Brian Van Essen; Rio Yokota; Nikoli Dryden (CoRR, 2024)

Why We Build Local Large Language Models: An Observational Analysis from 35 Japanese and Multilingual LLMs. - Koshiro Saito; Sakae Mizuki; Masanari Ohi; Taishi Nakamura; Taihei Shiotani; Koki Maeda; Youmi Ma; Kakeru Hattori; Kazuki Fujii; Takumi Okamoto; Shigeki Ishida; Hiroya Takamura; et al. (CoRR, 2024)

An inherently parallel ℋ2-ULV factorization for solving dense linear systems on GPUs. - Qianxiang Ma; Rio Yokota (Int. J. High Perform. Comput. Appl., 2024)

DGEMM on integer matrix multiplication unit. - Hiroyuki Ootomo; Katsuhisa Ozaki; Rio Yokota (Int. J. High Perform. Comput. Appl., 2024)

Natural Gradient Primal-Dual Method for Decentralized Learning. - Kenta Niwa; Hiro Ishii; Hiroshi Sawada; Akinori Fujino; Noboru Harada; Rio Yokota (IEEE Trans. Signal Inf. Process. over Networks, 2024)

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; et al. (COLING (Industry), 2025)

Local Loss Optimization in the Infinite Width: Stable Parameterization of Predictive Coding Networks and Target Propagation. - Satoki Ishikawa; Rio Yokota; Ryo Karakida (ICLR, 2025)

Drop-Upcycling: Training Sparse Mixture of Experts with Partial Re-initialization. - Taishi Nakamura; Takuya Akiba; Kazuki Fujii; Yusuke Oda; Rio Yokota; Jun Suzuki 001 (ICLR, 2025)

A General and Scalable GCN Training Framework on CPU Supercomputers. - Chen Zhuang; Peng Chen 035; Xin Liu 020; Rio Yokota; Nikoli Dryden; Lingqi Zhang 001; Toshio Endo; Satoshi Matsuoka; Mohamed Wahib (PPoPP, 2025)

On the Interplay Between Precision, Rank and Admissibility for Iterative Hierarchical Low-Rank Matrix Solvers. - Thomas Spendlhofer; Qianxiang Ma; Yasuhiro Matsumoto; Rio Yokota (ISC, 2025)

NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA. - 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; et al. (Trans. Mach. Learn. Res., 2025)

An inherently parallel H2-ULV factorization for solving dense linear systems on GPUs. - Qianxiang Ma; Rio Yokota (CoRR, 2025)

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 (CoRR, 2025)

Building Instruction-Tuning Datasets from Human-Written Instructions with Open-Weight Large Language Models. - Youmi Ma; Sakae Mizuki; Kazuki Fujii; Taishi Nakamura; Masanari Ohi; Hinari Shimada; Taihei Shiotani; Koshiro Saito; Koki Maeda; Kakeru Hattori; Takumi Okamoto; Shigeki Ishida; et al. (CoRR, 2025)

Rewriting Pre-Training Data Boosts LLM Performance in Math and Code. - Kazuki Fujii; Yukito Tajima; Sakae Mizuki; Hinari Shimada; Taihei Shiotani; Koshiro Saito; Masanari Ohi; Masaki Kawamura; Taishi Nakamura; Takumi Okamoto; Shigeki Ishida; Kakeru Hattori; et al. (CoRR, 2025)

Variational Learning Finds Flatter Solutions at the Edge of Stability. - Avrajit Ghosh; Bai Cong; Rio Yokota; Saiprasad Ravishankar; Rongrong Wang; Molei Tao; Mohammad Emtiyaz Khan; Thomas Möllenhoff (CoRR, 2025)

Improving LoRA with Variational Learning. - Bai Cong; Nico Daheim; Yuesong Shen; Rio Yokota; Mohammad Emtiyaz Khan; Thomas Möllenhoff (CoRR, 2025)

Masked Gated Linear Unit. - Yukito Tajima; Nakamasa Inoue; Yusuke Sekikawa; Ikuro Sato; Rio Yokota (CoRR, 2025)