Yokota, Rio

20 publications

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

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

NeurIPS 2025 Masked Gated Linear Unit Yukito Tajima, Nakamasa Inoue, Yusuke Sekikawa, Ikuro Sato, Rio Yokota

TMLR 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, Kevin Kuo, Virginia Smith, Yusuke Yamasaki, Takumi Fukami, Kenta Niwa, Iifan Tyou, Hiro Ishii, Rio Yokota, Ragul N, Rintu Kutum, Josep Llados, Ernest Valveny, Antti Honkela, Mario Fritz, Dimosthenis Karatzas

NeurIPS 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

ECCV 2024 Formula-Supervised Visual-Geometric Pre-Training Ryosuke Yamada, Kensho Hara, Hirokatsu Kataoka, Koshi Makihara, Nakamasa Inoue, Rio Yokota, Yutaka Satoh

ECCV 2024 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

ECCV 2024 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

ICML 2024 Variational Learning Is Effective for Large Deep Networks Yuesong Shen, Nico Daheim, Bai Cong, Peter Nickl, Gian Maria Marconi, Bazan Clement Emile Marcel Raoul, Rio Yokota, Iryna Gurevych, Daniel Cremers, Mohammad Emtiyaz Khan, Thomas Möllenhoff

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

TMLR 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

TMLR 2023 Improving Continual Learning by Accurate Gradient Reconstructions of the past Erik Daxberger, Siddharth Swaroop, Kazuki Osawa, Rio Yokota, Richard E Turner, José Miguel Hernández-Lobato, Mohammad Emtiyaz Khan

CVPRW 2023 Pixel-Level Contrastive Learning of Driving Videos with Optical Flow Tomoya Takahashi, Shingo Yashima, Kohta Ishikawa, Ikuro Sato, Rio Yokota

ICCV 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

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

NeurIPSW 2022 Empirical Study on Optimizer Selection for Out-of-Distribution Generalization Hiroki Naganuma, Kartik Ahuja, Ioannis Mitliagkas, Shiro Takagi, Tetsuya Motokawa, Rio Yokota, Kohta Ishikawa, Ikuro Sato

CVPR 2022 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

ICCV 2021 RePOSE: Fast 6d Object Pose Refinement via Deep Texture Rendering Shun Iwase, Xingyu Liu, Rawal Khirodkar, Rio Yokota, Kris M. Kitani

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