2025

Q-PART: Quasi-Periodic Adaptive Regression with Test-time Training for Pediatric Left Ventricular Ejection Fraction Regression
Q-PART: Quasi-Periodic Adaptive Regression with Test-time Training for Pediatric Left Ventricular Ejection Fraction Regression

Jie Liu, Tiexin Qin, Hui Liu, Yilei Shi, Lichao Mou, Xiao Xiang Zhu, Shiqi Wang, Haoliang Li

The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) 2025

[Paper]

Q-PART: Quasi-Periodic Adaptive Regression with Test-time Training for Pediatric Left Ventricular Ejection Fraction Regression
Q-PART: Quasi-Periodic Adaptive Regression with Test-time Training for Pediatric Left Ventricular Ejection Fraction Regression

Jie Liu, Tiexin Qin, Hui Liu, Yilei Shi, Lichao Mou, Xiao Xiang Zhu, Shiqi Wang, Haoliang Li

The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) 2025

[Paper]

Test-time Adaptation for Image Compression with Distribution Regularization
Test-time Adaptation for Image Compression with Distribution Regularization

Kecheng Chen, Pingping Zhang, Tiexin Qin, Shiqi Wang, Hong Yan, Haoliang Li

International Conference on Learning Representations (ICLR) 2025

[Paper] [Code]

Test-time Adaptation for Image Compression with Distribution Regularization
Test-time Adaptation for Image Compression with Distribution Regularization

Kecheng Chen, Pingping Zhang, Tiexin Qin, Shiqi Wang, Hong Yan, Haoliang Li

International Conference on Learning Representations (ICLR) 2025

[Paper] [Code]

Deep Signature: Characterization of Large-Scale Molecular Dynamics
Deep Signature: Characterization of Large-Scale Molecular Dynamics

Tiexin Qin, Mengxu Zhu, Chunyang Li, Terry Lyons, Hong Yan, Haoliang Li

International Conference on Learning Representations (ICLR) 2025

[Paper] [Code]

Deep Signature: Characterization of Large-Scale Molecular Dynamics
Deep Signature: Characterization of Large-Scale Molecular Dynamics

Tiexin Qin, Mengxu Zhu, Chunyang Li, Terry Lyons, Hong Yan, Haoliang Li

International Conference on Learning Representations (ICLR) 2025

[Paper] [Code]

2024

Learning Robust Shape Regularization for Generalizable Medical Image Segmentation
Learning Robust Shape Regularization for Generalizable Medical Image Segmentation

Kecheng Chen, Tiexin Qin, Victor Ho-Fun Lee, Hong Yan, Haoliang Li

IEEE Transactions on Medical Imaging (TMI) 2024

[Paper] [Code]

Learning Robust Shape Regularization for Generalizable Medical Image Segmentation
Learning Robust Shape Regularization for Generalizable Medical Image Segmentation

Kecheng Chen, Tiexin Qin, Victor Ho-Fun Lee, Hong Yan, Haoliang Li

IEEE Transactions on Medical Imaging (TMI) 2024

[Paper] [Code]

Log Neural Controlled Differential Equations: The Lie Brackets Make a Difference
Log Neural Controlled Differential Equations: The Lie Brackets Make a Difference

Benjamin Walker, Andrew D McLeod, Tiexin Qin, Yichuan Cheng, Haoliang Li, Terry Lyons

International Conference on Machine Learning (ICML) 2024

[Paper] [Code]

Log Neural Controlled Differential Equations: The Lie Brackets Make a Difference
Log Neural Controlled Differential Equations: The Lie Brackets Make a Difference

Benjamin Walker, Andrew D McLeod, Tiexin Qin, Yichuan Cheng, Haoliang Li, Terry Lyons

International Conference on Machine Learning (ICML) 2024

[Paper] [Code]

2023

Generalizing to New Dynamical Systems via Frequency Domain Adaptation
Generalizing to New Dynamical Systems via Frequency Domain Adaptation

Tiexin Qin, Hong Yan, Haoliang Li

OpenReview 2023

[Paper]

Generalizing to New Dynamical Systems via Frequency Domain Adaptation
Generalizing to New Dynamical Systems via Frequency Domain Adaptation

Tiexin Qin, Hong Yan, Haoliang Li

OpenReview 2023

[Paper]

LibFewShot: A Comprehensive Library for Few-shot Learning
LibFewShot: A Comprehensive Library for Few-shot Learning

Wenbin Li, Chuanqi Dong, Pinzhuo Tian, Tiexin Qin, Xuesong Yang, Ziyi Wang, Jing Huo, Yinghuan Shi, Lei Wang, Yang Gao, Jiebo Luo

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2023

[Paper] [Code]

LibFewShot: A Comprehensive Library for Few-shot Learning
LibFewShot: A Comprehensive Library for Few-shot Learning

Wenbin Li, Chuanqi Dong, Pinzhuo Tian, Tiexin Qin, Xuesong Yang, Ziyi Wang, Jing Huo, Yinghuan Shi, Lei Wang, Yang Gao, Jiebo Luo

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2023

[Paper] [Code]

Evolving Domain Generalization via Latent Structure-Aware Sequential Autoencoder
Evolving Domain Generalization via Latent Structure-Aware Sequential Autoencoder

Tiexin Qin, Shiqi Wang, Haoliang Li

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2023

[Paper] [Code]

Evolving Domain Generalization via Latent Structure-Aware Sequential Autoencoder
Evolving Domain Generalization via Latent Structure-Aware Sequential Autoencoder

Tiexin Qin, Shiqi Wang, Haoliang Li

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2023

[Paper] [Code]

Learning dynamic graph embeddings with neural controlled differential equations
Learning dynamic graph embeddings with neural controlled differential equations

Tiexin Qin, Benjamin Walker, Terry Lyons, Hong Yan, Haoliang Li

arXiv (Preprint) 2023

[Paper]

Learning dynamic graph embeddings with neural controlled differential equations
Learning dynamic graph embeddings with neural controlled differential equations

Tiexin Qin, Benjamin Walker, Terry Lyons, Hong Yan, Haoliang Li

arXiv (Preprint) 2023

[Paper]

2022

Generalizing to Evolving Domains with Latent Structure-Aware Sequential Autoencoder
Generalizing to Evolving Domains with Latent Structure-Aware Sequential Autoencoder

Tiexin Qin, Shiqi Wang, Haoliang Li

International Conference on Machine Learning (ICML) 2022

[Paper] [Code]

Generalizing to Evolving Domains with Latent Structure-Aware Sequential Autoencoder
Generalizing to Evolving Domains with Latent Structure-Aware Sequential Autoencoder

Tiexin Qin, Shiqi Wang, Haoliang Li

International Conference on Machine Learning (ICML) 2022

[Paper] [Code]

2021

Machine learning on properties of multiscale multisource hydroxyapatite nanoparticles datasets with different morphologies and sizes
Machine learning on properties of multiscale multisource hydroxyapatite nanoparticles datasets with different morphologies and sizes

Ziteng Liu, Yinghuan Shi, Hongwei Chen, Tiexin Qin, Xuejie Zhou, Jun Huo, Hao Dong, Xiao Yang, Xiangdong Zhu, Xuening Chen, Li Zhang, Mingli Yang, Yang Gao, Jing Ma

npj Computational Materials 2021

[Paper]

Machine learning on properties of multiscale multisource hydroxyapatite nanoparticles datasets with different morphologies and sizes
Machine learning on properties of multiscale multisource hydroxyapatite nanoparticles datasets with different morphologies and sizes

Ziteng Liu, Yinghuan Shi, Hongwei Chen, Tiexin Qin, Xuejie Zhou, Jun Huo, Hao Dong, Xiao Yang, Xiangdong Zhu, Xuening Chen, Li Zhang, Mingli Yang, Yang Gao, Jing Ma

npj Computational Materials 2021

[Paper]

2020

Automatic data augmentation via deep reinforcement learning for effective kidney tumor segmentation
Automatic data augmentation via deep reinforcement learning for effective kidney tumor segmentation

Tiexin Qin, Ziyuan Wang, Kelei He, Yinghuan Shi, Yang Gao, Dinggang Shen

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2020

[Paper]

Automatic data augmentation via deep reinforcement learning for effective kidney tumor segmentation
Automatic data augmentation via deep reinforcement learning for effective kidney tumor segmentation

Tiexin Qin, Ziyuan Wang, Kelei He, Yinghuan Shi, Yang Gao, Dinggang Shen

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2020

[Paper]

Diversity helps: Unsupervised few-shot learning via distribution shift-based data augmentation
Diversity helps: Unsupervised few-shot learning via distribution shift-based data augmentation

Tiexin Qin, Wenbin Li, Yinghuan Shi, Yang Gao

arXiv (Preprint) 2020

[Paper] [Code]

Diversity helps: Unsupervised few-shot learning via distribution shift-based data augmentation
Diversity helps: Unsupervised few-shot learning via distribution shift-based data augmentation

Tiexin Qin, Wenbin Li, Yinghuan Shi, Yang Gao

arXiv (Preprint) 2020

[Paper] [Code]

2019

Automatic data augmentation by learning the deterministic policy
Automatic data augmentation by learning the deterministic policy

Yinghuan Shi, Tiexin Qin, Yong Liu, Jiwen Lu, Yang Gao, Dinggang Shen

arXiv (Preprint) 2020

[Paper] [Demo]

Automatic data augmentation by learning the deterministic policy
Automatic data augmentation by learning the deterministic policy

Yinghuan Shi, Tiexin Qin, Yong Liu, Jiwen Lu, Yang Gao, Dinggang Shen

arXiv (Preprint) 2020

[Paper] [Demo]