Chieh-Hsin Lai

According to our database, Chieh-Hsin Lai authored at least 18 papers between 2020 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

On the Language Encoder of Contrastive Cross-modal Models.

Mengjie Zhao, Junya Ono, Zhi Zhong, Chieh-Hsin Lai, Yuhta Takida, Naoki Murata, Wei-Hsiang Liao, Takashi Shibuya, Hiromi Wakaki, Yuki Mitsufuji

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

VRDMG: Vocal Restoration via Diffusion Posterior Sampling with Multiple Guidance.

Carlos Hernandez-Olivan, Koichi Saito, Naoki Murata, Chieh-Hsin Lai, Marco A. Martínez Ramírez, Wei-Hsiang Liao, Yuki Mitsufuji

CoRR, 2023

The Sound Demixing Challenge 2023 - Music Demixing Track.

Giorgio Fabbro, Stefan Uhlich, Chieh-Hsin Lai, Woosung Choi, Marco A. Martínez Ramírez, Wei-Hsiang Liao, Igor Gadelha, Geraldo Ramos, Eddie Hsu, Hugo Rodrigues, Fabian-Robert Stöter, Alexandre Défossez, Yi Luo, Jianwei Yu, Dipam Chakraborty, Sharada P. Mohanty, Roman A. Solovyev, Alexander L. Stempkovskiy, Tatiana Habruseva, Nabarun Goswami, Tatsuya Harada, Minseok Kim, Jun Hyung Lee, Yuanliang Dong, Xinran Zhang, Jiafeng Liu, Yuki Mitsufuji

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

Robust Variational Autoencoding with Wasserstein Penalty for Novelty Detection.

Chieh-Hsin Lai, Dongmian Zou, Gilad Lerman

Proceedings of the International Conference on Artificial Intelligence and Statistics, 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

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

2020

Novelty Detection via Robust Variational Autoencoding.

Chieh-Hsin Lai, Dongmian Zou, Gilad Lerman

CoRR, 2020

Inverse Problems, Deep Learning, and Symmetry Breaking.

Kshitij Tayal, Chieh-Hsin Lai, Vipin Kumar, Ju Sun

CoRR, 2020

Robust Subspace Recovery Layer for Unsupervised Anomaly Detection.

Chieh-Hsin Lai, Dongmian Zou, Gilad Lerman

Proceedings of the 8th International Conference on Learning Representations, 2020