Toshimitsu Uesaka

According to our database, Toshimitsu Uesaka authored at least 15 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

HQ-VAE: Hierarchical Discrete Representation Learning with Variational Bayes.

Yuhta Takida, Yukara Ikemiya, Takashi Shibuya, Kazuki Shimada, Woosung Choi, Chieh-Hsin Lai, Naoki Murata, Toshimitsu Uesaka, Kengo Uchida, Wei-Hsiang Liao, Yuki Mitsufuji

CoRR, 2024

2023

Manifold Preserving Guided Diffusion.

Yutong He, Naoki Murata, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Dongjun Kim, Wei-Hsiang Liao, Yuki Mitsufuji, J. Zico Kolter, Ruslan Salakhutdinov, Stefano Ermon

CoRR, 2023

Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion.

Dongjun Kim, Chieh-Hsin Lai, Wei-Hsiang Liao, Naoki Murata, Yuhta Takida, Toshimitsu Uesaka, Yutong He, Yuki Mitsufuji, Stefano Ermon

CoRR, 2023

On the Equivalence of Consistency-Type Models: Consistency Models, Consistent Diffusion Models, and Fokker-Planck Regularization.

Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Naoki Murata, Yuki Mitsufuji, Stefano Ermon

CoRR, 2023

Adversarially Slicing Generative Networks: Discriminator Slices Feature for One-Dimensional Optimal Transport.

Yuhta Takida, Masaaki Imaizumi, Chieh-Hsin Lai, Toshimitsu Uesaka, Naoki Murata, Yuki Mitsufuji

CoRR, 2023

GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration.

Naoki Murata, Koichi Saito, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon

Proceedings of the International Conference on Machine Learning, 2023

FP-Diffusion: Improving Score-based Diffusion Models by Enforcing the Underlying Score Fokker-Planck Equation.

Chieh-Hsin Lai, Yuhta Takida, Naoki Murata, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon

Proceedings of the International Conference on Machine Learning, 2023

Unsupervised Vocal Dereverberation with Diffusion-Based Generative Models.

Koichi Saito, Naoki Murata, Toshimitsu Uesaka, Chieh-Hsin Lai, Yuhta Takida, Takao Fukui, Yuki Mitsufuji

Proceedings of the IEEE International Conference on Acoustics, 2023

Diffroll: Diffusion-Based Generative Music Transcription with Unsupervised Pretraining Capability.

Kin Wai Cheuk, Ryosuke Sawata, Toshimitsu Uesaka, Naoki Murata, Naoya Takahashi, Shusuke Takahashi, Dorien Herremans, Yuki Mitsufuji

Proceedings of the IEEE International Conference on Acoustics, 2023

2022

Preventing oversmoothing in VAE via generalized variance parameterization.

Yuhta Takida, Wei-Hsiang Liao, Chieh-Hsin Lai, Toshimitsu Uesaka, Shusuke Takahashi, Yuki Mitsufuji

Neurocomputing, 2022

A Versatile Diffusion-based Generative Refiner for Speech Enhancement.

Ryosuke Sawata, Naoki Murata, Yuhta Takida, Toshimitsu Uesaka, Takashi Shibuya, Shusuke Takahashi, Yuki Mitsufuji

CoRR, 2022

Regularizing Score-based Models with Score Fokker-Planck Equations.

Chieh-Hsin Lai, Yuhta Takida, Naoki Murata, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon

CoRR, 2022

SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization.

Yuhta Takida, Takashi Shibuya, Wei-Hsiang Liao, Chieh-Hsin Lai, Junki Ohmura, Toshimitsu Uesaka, Naoki Murata, Shusuke Takahashi, Toshiyuki Kumakura, Yuki Mitsufuji

Proceedings of the International Conference on Machine Learning, 2022

2021

Preventing Posterior Collapse Induced by Oversmoothing in Gaussian VAE.

Yuhta Takida, Wei-Hsiang Liao, Toshimitsu Uesaka, Shusuke Takahashi, Yuki Mitsufuji

CoRR, 2021

2017

Multi-view Learning over Retinal Thickness and Visual Sensitivity on Glaucomatous Eyes.

Toshimitsu Uesaka, Kai Morino, Hiroki Sugiura, Taichi Kiwaki, Hiroshi Murata, Ryo Asaoka, Kenji Yamanishi

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13, 2017