Yilun Xu

According to our database, Yilun Xu authored at least 32 papers between 2019 and 2024.

Collaborative distances:

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

2024

Deep learning prediction of esophageal squamous cell carcinoma invasion depth from arterial phase enhanced CT images: a binary classification approach.

Xiaoli Wu, Hao Wu, Shouliang Miao, Guoquan Cao, Huang Su, Jie Pan, Yilun Xu

BMC Medical Informatics Decis. Mak., December, 2024

A Survey on Generative Diffusion Models.

Hanqun Cao, Cheng Tan, Zhangyang Gao, Yilun Xu, Guangyong Chen, Pheng-Ann Heng, Stan Z. Li

IEEE Trans. Knowl. Data Eng., July, 2024

Think While You Generate: Discrete Diffusion with Planned Denoising.

Sulin Liu, Juno Nam, Andrew Campbell, Hannes Stärk, Yilun Xu, Tommi S. Jaakkola, Rafael Gómez-Bombarelli

CoRR, 2024

Neural Augmentation Based Panoramic High Dynamic Range Stitching.

Chaobing Zheng, Yilun Xu, Weihai Chen, Shiqian Wu, Zhengguo Li

CoRR, 2024

Heterophilous Distribution Propagation for Graph Neural Networks.

Zhuonan Zheng, Sheng Zhou, Hongjia Xu, Ming Gu, Yilun Xu, Ao Li, Yuhong Li, Jingjun Gu, Jiajun Bu

CoRR, 2024

Distributed Architecture for FPGA-based Superconducting Qubit Control.

Neelay Fruitwala, Gang Huang, Yilun Xu, Abhi D. Rajagopala, Akel Hashim, Ravi Naik, Kasra Nowrouzi, David Santiago, Irfan Siddiqi

CoRR, 2024

TITAN: A Distributed Large-Scale Trapped-Ion NISQ Computer.

Cheng Chu, Zhenxiao Fu, Yilun Xu, Gang Huang, Hausi A. Müller, Fan Chen, Lei Jiang

CoRR, 2024

QuantumLeak: Stealing Quantum Neural Networks from Cloud-based NISQ Machines.

Zhenxiao Fu, Min Yang, Cheng Chu, Yilun Xu, Gang Huang, Fan Chen

Proceedings of the International Joint Conference on Neural Networks, 2024

DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents.

Yilun Xu, Gabriele Corso, Tommi S. Jaakkola, Arash Vahdat, Karsten Kreis

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion Models.

Gabriele Corso, Yilun Xu, Valentin De Bortoli, Regina Barzilay, Tommi S. Jaakkola

Proceedings of the Twelfth International Conference on Learning Representations, 2024

TITAN: A Fast and Distributed Large-Scale Trapped-Ion NISQ Computer.

Cheng Chu, Zhenxiao Fu, Yilun Xu, Gang Huang, Hausi A. Müller, Fan Chen, Lei Jiang

Proceedings of the 61st ACM/IEEE Design Automation Conference, 2024

2023

GenPhys: From Physical Processes to Generative Models.

Ziming Liu, Di Luo, Yilun Xu, Tommi S. Jaakkola, Max Tegmark

CoRR, 2023

QubiC 2.0: A Flexible Advanced Full Stack Quantum Bit Control System.

Gang Huang, Yilun Xu, Neelay Fruitwala, Abhi D. Rajagopala, Kasra Nowrouzi, Ravi Naik, David Santiago, Irfan Siddiqi

Proceedings of the IEEE International Conference on Quantum Computing and Engineering, 2023

Restart Sampling for Improving Generative Processes.

Yilun Xu, Mingyang Deng, Xiang Cheng, Yonglong Tian, Ziming Liu, Tommi S. Jaakkola

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Physics-Driven Deep Panoramic Imaging for High Dynamic Range Scenes.

Chaobing Zheng, Yilun Xu, Weihai Chen, Shiqian Wu, Zhengguo Li

Proceedings of the 49th Annual Conference of the IEEE Industrial Electronics Society, 2023

PFGM++: Unlocking the Potential of Physics-Inspired Generative Models.

Yilun Xu, Ziming Liu, Yonglong Tian, Shangyuan Tong, Max Tegmark, Tommi S. Jaakkola

Proceedings of the International Conference on Machine Learning, 2023

Stable Target Field for Reduced Variance Score Estimation in Diffusion Models.

Yilun Xu, Shangyuan Tong, Tommi S. Jaakkola

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Deep Joint Demosaicing and High Dynamic Range Imaging Within a Single Shot.

Yilun Xu, Ziyang Liu, Xingming Wu, Weihai Chen, Changyun Wen, Zhengguo Li

IEEE Trans. Circuits Syst. Video Technol., 2022

Using gravity model to make store closing decisions: A data driven approach.

Mohsen Bahrami, Yilun Xu, Miles Tweed, Burçin Bozkaya, Alex 'Sandy' Pentland

Expert Syst. Appl., 2022

Distributed Processor for FPGA-based Superconducting Qubit Control.

Neelay Fruitwala, Yilun Xu, Ravi Naik, Kasra Nowrouzi, Gang Huang

Proceedings of the IEEE International Conference on Quantum Computing and Engineering, 2022

Poisson Flow Generative Models.

Yilun Xu, Ziming Liu, Max Tegmark, Tommi S. Jaakkola

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Controlling Directions Orthogonal to a Classifier.

Yilun Xu, Hao He, Tianxiao Shen, Tommi S. Jaakkola

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Color Mapping Functions For HDR Panorama Imaging: Weighted Histogram Averaging.

Yilun Xu, Zhengguo Li, Weihai Chen, Changyun Wen

CoRR, 2021

Learning Representations that Support Robust Transfer of Predictors.

Yilun Xu, Tommi S. Jaakkola

CoRR, 2021

Can Subnetwork Structure Be the Key to Out-of-Distribution Generalization?

Dinghuai Zhang, Kartik Ahuja, Yilun Xu, Yisen Wang, Aaron C. Courville

Proceedings of the 38th International Conference on Machine Learning, 2021

Restoration of HDR Images for SVE-Based HDRI via a Novel DCNN.

Yilun Xu, Ziyang Liu, Xingming Wu, Weihai Chen, Zhengguo Li

Proceedings of the 2021 IEEE International Conference on Multimedia and Expo, 2021

Anytime Sampling for Autoregressive Models via Ordered Autoencoding.

Yilun Xu, Yang Song, Sahaj Garg, Linyuan Gong, Rui Shu, Aditya Grover, Stefano Ermon

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

A Theory of Usable Information under Computational Constraints.

Yilun Xu, Shengjia Zhao, Jiaming Song, Russell Stewart, Stefano Ermon

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

TCGM: An Information-Theoretic Framework for Semi-supervised Multi-modality Learning.

Xinwei Sun, Yilun Xu, Peng Cao, Yuqing Kong, Lingjing Hu, Shanghang Zhang, Yizhou Wang

Proceedings of the Computer Vision - ECCV 2020, 2020

2019

L_DMI: An Information-theoretic Noise-robust Loss Function.

Yilun Xu, Peng Cao, Yuqing Kong, Yizhou Wang

CoRR, 2019

L_DMI: A Novel Information-theoretic Loss Function for Training Deep Nets Robust to Label Noise.

Yilun Xu, Peng Cao, Yuqing Kong, Yizhou Wang

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Max-MIG: an Information Theoretic Approach for Joint Learning from Crowds.

Peng Cao, Yilun Xu, Yuqing Kong, Yizhou Wang

Proceedings of the 7th International Conference on Learning Representations, 2019