Minkai Xu

According to our database, Minkai Xu authored at least 32 papers between 2020 and 2024.

Collaborative distances:

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

2024

Cross-Modal Contextualized Diffusion Models for Text-Guided Visual Generation and Editing.

Ling Yang, Zhilong Zhang, Zhaochen Yu, Jingwei Liu, Minkai Xu, Stefano Ermon, Bin Cui

CoRR, 2024

RealCompo: Dynamic Equilibrium between Realism and Compositionality Improves Text-to-Image Diffusion Models.

Xinchen Zhang, Ling Yang, Yaqi Cai, Zhaochen Yu, Jiake Xie, Ye Tian, Minkai Xu, Yong Tang, Yujiu Yang, Bin Cui

CoRR, 2024

Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs.

Ling Yang, Zhaochen Yu, Chenlin Meng, Minkai Xu, Stefano Ermon, Bin Cui

CoRR, 2024

Equivariant Graph Neural Operator for Modeling 3D Dynamics.

Minkai Xu, Jiaqi Han, Aaron Lou, Jean Kossaifi, Arvind Ramanathan, Kamyar Azizzadenesheli, Jure Leskovec, Stefano Ermon, Anima Anandkumar

CoRR, 2024

2023

Equivariant Flow Matching with Hybrid Probability Transport.

Yuxuan Song, Jingjing Gong, Minkai Xu, Ziyao Cao, Yanyan Lan, Stefano Ermon, Hao Zhou, Wei-Ying Ma

CoRR, 2023

RetroDiff: Retrosynthesis as Multi-stage Distribution Interpolation.

Yiming Wang, Yuxuan Song, Minkai Xu, Rui Wang, Hao Zhou, Weiying Ma

CoRR, 2023

Scaling Riemannian Diffusion Models.

Aaron Lou, Minkai Xu, Stefano Ermon

CoRR, 2023

VQGraph: Graph Vector-Quantization for Bridging GNNs and MLPs.

Ling Yang, Ye Tian, Minkai Xu, Zhongyi Liu, Shenda Hong, Wei Qu, Wentao Zhang, Bin Cui, Muhan Zhang, Jure Leskovec

CoRR, 2023

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems.

Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo, Cong Fu, Yaochen Xie, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Haiyang Yu, Yuqing Xie, Xiang Fu, Alex Strasser, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alán Aspuru-Guzik, Erik J. Bekkers, Michael M. Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi S. Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess E. Smidt, Shuiwang Ji

CoRR, 2023

MADiff: Offline Multi-agent Learning with Diffusion Models.

Zhengbang Zhu, Minghuan Liu, Liyuan Mao, Bingyi Kang, Minkai Xu, Yong Yu, Stefano Ermon, Weinan Zhang

CoRR, 2023

MUDiff: Unified Diffusion for Complete Molecule Generation.

Chenqing Hua, Sitao Luan, Minkai Xu, Rex Ying, Jie Fu, Stefano Ermon, Doina Precup

CoRR, 2023

Equivariant Flow Matching with Hybrid Probability Transport for 3D Molecule Generation.

Yuxuan Song, Jingjing Gong, Minkai Xu, Ziyao Cao, Yanyan Lan, Stefano Ermon, Hao Zhou, Wei-Ying Ma

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

When Do Graph Neural Networks Help with Node Classification? Investigating the Homophily Principle on Node Distinguishability.

Sitao Luan, Chenqing Hua, Minkai Xu, Qincheng Lu, Jiaqi Zhu, Xiao-Wen Chang, Jie Fu, Jure Leskovec, Doina Precup

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

Scaling Riemannian Diffusion Models.

Aaron Lou, Minkai Xu, Adam Farris, Stefano Ermon

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

Graph and Geometry Generative Modeling for Drug Discovery.

Minkai Xu, Meng Liu, Wengong Jin, Shuiwang Ji, Jure Leskovec, Stefano Ermon

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Geometric Latent Diffusion Models for 3D Molecule Generation.

Minkai Xu, Alexander S. Powers, Ron O. Dror, Stefano Ermon, Jure Leskovec

Proceedings of the International Conference on Machine Learning, 2023

Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3D.

Bo Qiang, Yuxuan Song, Minkai Xu, Jingjing Gong, Bowen Gao, Hao Zhou, Wei-Ying Ma, Yanyan Lan

Proceedings of the International Conference on Machine Learning, 2023

FusionRetro: Molecule Representation Fusion via In-Context Learning for Retrosynthetic Planning.

Songtao Liu, Zhengkai Tu, Minkai Xu, Zuobai Zhang, Lu Lin, Rex Ying, Jian Tang, Peilin Zhao, Dinghao Wu

Proceedings of the International Conference on Machine Learning, 2023

2022

Generative Coarse-Graining of Molecular Conformations.

Wujie Wang, Minkai Xu, Chen Cai, Benjamin Kurt Miller, Tess E. Smidt, Yusu Wang, Jian Tang, Rafael Gómez-Bombarelli

Proceedings of the International Conference on Machine Learning, 2022

GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation.

Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, Jian Tang

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Predicting Molecular Conformation via Dynamic Graph Score Matching.

Shitong Luo, Chence Shi, Minkai Xu, Jian Tang

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming.

Minkai Xu, Wujie Wang, Shitong Luo, Chence Shi, Yoshua Bengio, Rafael Gómez-Bombarelli, Jian Tang

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

Learning Gradient Fields for Molecular Conformation Generation.

Chence Shi, Shitong Luo, Minkai Xu, Jian Tang

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

Learning Neural Generative Dynamics for Molecular Conformation Generation.

Minkai Xu, Shitong Luo, Yoshua Bengio, Jian Peng, Jian Tang

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

Energy-Based Imitation Learning.

Minghuan Liu, Tairan He, Minkai Xu, Weinan Zhang

Proceedings of the AAMAS '21: 20th International Conference on Autonomous Agents and Multiagent Systems, 2021

Towards Generalized Implementation of Wasserstein Distance in GANs.

Minkai Xu

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Sobolev Wasserstein GAN.

Minkai Xu, Zhiming Zhou, Guansong Lu, Jian Tang, Weinan Zhang, Yong Yu

CoRR, 2020

Reciprocal Supervised Learning Improves Neural Machine Translation.

Minkai Xu, Mingxuan Wang, Zhouhan Lin, Hao Zhou, Weinan Zhang, Lei Li

CoRR, 2020

Discriminator Contrastive Divergence: Semi-Amortized Generative Modeling by Exploring Energy of the Discriminator.

Yuxuan Song, Qiwei Ye, Minkai Xu, Tie-Yan Liu

CoRR, 2020

A Graph to Graphs Framework for Retrosynthesis Prediction.

Chence Shi, Minkai Xu, Hongyu Guo, Ming Zhang, Jian Tang

Proceedings of the 37th International Conference on Machine Learning, 2020

GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation.

Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, Jian Tang

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

Infomax Neural Joint Source-Channel Coding via Adversarial Bit Flip.

Yuxuan Song, Minkai Xu, Lantao Yu, Hao Zhou, Shuo Shao, Yong Yu

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020