Rio Yokota
16 papers · 2019–2026 · 9 conferences · across top CS/AI conferences
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Conferences
CVPR (3)
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AAAI (1)
COLING (1)
EMNLP (1)
ICML (1)
NIPS (1)
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Keywords
formula-driven supervised learning
(4)
vision transformer
(3)
second-order optimization
(2)
image classification
(2)
formula-driven supervision
(2)
distributed learning
(2)
synthetic image
(2)
multilingual model
(2)
variational inference
(1)
cross-lingual transfer
(1)
continual learning
(1)
code generation
(1)
data augmentation
(1)
deep learning
(1)
self-supervised learning
(1)
semantic segmentation
(1)
continued pretraining
(1)
medical text
(1)
synthetic data generation
(1)
domain adaptation
(1)
Papers
FedPM: Federated Learning Using Second-order Optimization with Preconditioned Mixing of Local Parameters
AAAI 2026
Aurora-M: Open Source Continual Pre-training for Multilingual Language and Code
COLING 2025
Leveraging High-Resource English Corpora for Cross-lingual Domain Adaptation in Low-Resource Japanese Medicine via Continued Pre-training
EMNLP 2025
Drop-Upcycling: Training Sparse Mixture of Experts with Partial Re-initialization
ICLR 2025
Local Loss Optimization in the Infinite Width: Stable Parameterization of Predictive Coding Networks and Target Propagation
ICLR 2025
Variational Learning is Effective for Large Deep Networks
ICML 2024
Rethinking Image Super Resolution from Training Data Perspectives
ECCV 2024
Formula-Supervised Visual-Geometric Pre-training
ECCV 2024
Scaling Backwards: Minimal Synthetic Pre-training?
ECCV 2024
Visual Atoms: Pre-Training Vision Transformers With Sinusoidal Waves
CVPR 2023
Pre-training Vision Transformers with Very Limited Synthesized Images
ICCV 2023
SegRCDB: Semantic Segmentation via Formula-Driven Supervised Learning
ICCV 2023
Replacing Labeled Real-Image Datasets With Auto-Generated Contours
CVPR 2022
RePOSE: Fast 6D Object Pose Refinement via Deep Texture Rendering
ICCV 2021
Large-Scale Distributed Second-Order Optimization Using Kronecker-Factored Approximate Curvature for Deep Convolutional Neural Networks
CVPR 2019
Practical Deep Learning with Bayesian Principles
NIPS 2019