
Research
Publications
Category
Research Area
Year
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Quantization-Aware Diffusion Models For Maximum Likelihood Training
Shohei Taniguchi, Masahiro Suzuki, Yutaka Matsuo
International Conference on Learning Representations 2026 (ICLR2026), April 2026
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One-shot Portrait Stylizaiton via Geometric Alignment
Xinrui Wang, Zilin Guo, Zhuoru Li, Jinze Yu, Heng Zhang, Yusuke Iwasawa, Yutaka Matsuo, Jiaxian Guo
Proceedings of The IEEE/CVF Winter Conference on Applications of Computer Vision 2026(WACV2026)
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Pairwise Optimal Transports for Training All-to-All Flow-Based Condition Transfer Model
Kotaro Ikeda, Masanori Koyama, Jinzhe Zhang, Kohei Hayashi, Kenji Fukumizu
Advances in Neural Information Processing Systems 38 (NeurIPS 2025), December 2025.
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Improving Reference Image Replication for Tuning-Free Personalized Image Generation
大坂洋豊, 鈴木雅大, 松尾豊
情報処理学会論文誌
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Efficient Object-Centric Representation Learning using Masked Generative Modeling
Akihiro Nakano, Masahiro Suzuki, Yutaka Matsuo
Transactions on Machine Learning Research (TMLR), 2025
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The Geometry of Phase Transitions in Diffusion Models: Tubular Neighbourhoods and Singularities
Manato Yaguchi*, Kotaro Sakamoto*, Ryosuke Sakamoto*, Masato Tanabe*, Masatomo Akagawa*, Yusuke Hayashi*, Masahiro Suzuki, Yutaka Matsuo (*Equal Contribution)
Transactions on Machine Learning Research (TMLR), 2025
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Image Referenced Sketch Colorization Based on Animation Creation Workflow
Dingkun Yan*, Xinrui Wang*, Zhuoru Li, Suguru Saito, Yusuke Iwasawa, Yutaka Matsuo, Jiaxian Guo
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2025)
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End-to-end Training of Deep Boltzmann Machines by Unbiased Contrastive Divergence with Local Mode Initialization
Shohei Taniguchi, Masahiro Suzuki, Yusuke Iwasawa, Yutaka Matsuo
International Conference on Machine Learning (ICML 2023) July 2023.
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Interaction-Based Disentanglement of Entities for Object-Centric World Models
Akihiro Nakano, Masahiro Suzuki, Yutaka Matsuo.
“InteractioInternational Conference on Learning Representations (ICLR2023)
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Langevin Autoencoders for Learning Deep Latent Variable Models
Shohei Taniguchi, Yusuke Iwasawa, Wataru Kumagai, Yutaka Matsuo
Advances in Neural Information Processing Systems 35 (NeurIPS 2022).