Yuanqi Du

According to our database, Yuanqi Du authored at least 65 papers between 2020 and 2024.

Collaborative distances:

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

2024

Controllable Data Generation by Deep Learning: A Review.

Shiyu Wang, Yuanqi Du, Xiaojie Guo, Bo Pan, Zhaohui S. Qin, Liang Zhao

ACM Comput. Surv., September, 2024

Molecular Contrastive Pretraining with Collaborative Featurizations.

Yanqiao Zhu, Dingshuo Chen, Yuanqi Du, Yingze Wang, Qiang Liu, Shu Wu

J. Chem. Inf. Model., February, 2024

MUBen: Benchmarking the Uncertainty of Molecular Representation Models.

Yinghao Li, Lingkai Kong, Yuanqi Du, Yue Yu, Yuchen Zhuang, Wenhao Mu, Chao Zhang

Trans. Mach. Learn. Res., 2024

Machine learning-aided generative molecular design.

Yuanqi Du, Arian R. Jamasb, Jeff Guo, Tianfan Fu, Charles Harris, Yingheng Wang, Chenru Duan, Pietro Liò, Philippe Schwaller, Tom L. Blundell

Nat. Mac. Intell., 2024

Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling.

Yuanqi Du, Michael Plainer, Rob Brekelmans, Chenru Duan, Frank Noé, Carla P. Gomes, Alán Aspuru-Guzik, Kirill Neklyudov

CoRR, 2024

Efficient Evolutionary Search Over Chemical Space with Large Language Models.

Haorui Wang, Marta Skreta, Cher-Tian Ser, Wenhao Gao, Lingkai Kong, Felix Streith-Kalthoff, Chenru Duan, Yuchen Zhuang, Yue Yu, Yanqiao Zhu, Yuanqi Du, Alán Aspuru-Guzik, Kirill Neklyudov, Chao Zhang

CoRR, 2024

Aligning Large Language Models with Representation Editing: A Control Perspective.

Lingkai Kong, Haorui Wang, Wenhao Mu, Yuanqi Du, Yuchen Zhuang, Yifei Zhou, Yue Song, Rongzhi Zhang, Kai Wang, Chao Zhang

CoRR, 2024

Navigating Chemical Space with Latent Flows.

Guanghao Wei, Yining Huang, Chenru Duan, Yue Song, Yuanqi Du

CoRR, 2024

React-OT: Optimal Transport for Generating Transition State in Chemical Reactions.

Chenru Duan, Guan-Horng Liu, Yuanqi Du, Tianrong Chen, Qiyuan Zhao, Haojun Jia, Carla P. Gomes, Evangelos A. Theodorou, Heather J. Kulik

CoRR, 2024

Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints.

Lingkai Kong, Yuanqi Du, Wenhao Mu, Kirill Neklyudov, Valentin De Bortol, Haorui Wang, Dongxia Wu, Aaron Ferber, Yi-An Ma, Carla P. Gomes, Chao Zhang

CoRR, 2024

Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks.

Yanqiao Zhu, Jeehyun Hwang, Keir Adams, Zhen Liu, Bozhao Nan, Brock Stenfors, Yuanqi Du, Jatin Chauhan, Olaf Wiest, Olexandr Isayev, Connor W. Coley, Yizhou Sun, Wei Wang

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

ChemSpacE: Interpretable and Interactive Chemical Space Exploration.

Yuanqi Du, Xian Liu, Nilay Mahesh Shah, Shengchao Liu, Jieyu Zhang, Bolei Zhou

Trans. Mach. Learn. Res., 2023

Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model.

Chenru Duan, Yuanqi Du, Haojun Jia, Heather J. Kulik

Nat. Comput. Sci., 2023

Scientific discovery in the age of artificial intelligence.

Hanchen Wang, Tianfan Fu, Yuanqi Du, Wenhao Gao, Kexin Huang, Ziming Liu, Payal Chandak, Shengchao Liu, Peter Van Katwyk, Andreea Deac, Anima Anandkumar, Karianne Bergen, Carla P. Gomes, Shirley Ho, Pushmeet Kohli, Joan Lasenby, Jure Leskovec, Tie-Yan Liu, Arjun Manrai, Debora S. Marks, Bharath Ramsundar, Le Song, Jimeng Sun, Jian Tang, Petar Velickovic, Max Welling, Linfeng Zhang, Connor W. Coley, Yoshua Bengio, Marinka Zitnik

Nat., 2023

Uncovering Neural Scaling Laws in Molecular Representation Learning.

Dingshuo Chen, Yanqiao Zhu, Jieyu Zhang, Yuanqi Du, Zhixun Li, Qiang Liu, Shu Wu, Liang Wang

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

MUBen: Benchmarking the Uncertainty of Pre-Trained Models for Molecular Property Prediction.

Yinghao Li, Lingkai Kong, Yuanqi Du, Yue Yu, Yuchen Zhuang, Wenhao Mu, Chao Zhang

CoRR, 2023

Xtal2DoS: Attention-based Crystal to Sequence Learning for Density of States Prediction.

Junwen Bai, Yuanqi Du, Yingheng Wang, Shufeng Kong, John M. Gregoire, Carla Gomes

CoRR, 2023

Pik-Fix: Restoring and Colorizing Old Photos.

Runsheng Xu, Zhengzhong Tu, Yuanqi Du, Xiaoyu Dong, Jinlong Li, Zibo Meng, Jiaqi Ma, Alan C. Bovik, Hongkai Yu

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023

Stochastic Optimal Control for Collective Variable Free Sampling of Molecular Transition Paths.

Lars Holdijk, Yuanqi Du, Ferry Hooft, Priyank Jaini, Bernd Ensing, Max Welling

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

GAUCHE: A Library for Gaussian Processes in Chemistry.

Ryan-Rhys Griffiths, Leo Klarner, Henry B. Moss, Aditya Ravuri, Sang Truong, Yuanqi Du, Samuel Stanton, Gary Tom, Bojana Rankovic, Arian Rokkum Jamasb, Aryan Deshwal, Julius Schwartz, Austin Tripp, Gregory Kell, Simon Frieder, Anthony Bourached, Alex Chan, Jacob Moss, Chengzhi Guo, Johannes Peter Dürholt, Saudamini Chaurasia, Ji Won Park, Felix Strieth-Kalthoff, Alpha A. Lee, Bingqing Cheng, Alán Aspuru-Guzik, Philippe Schwaller, Jian Tang

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

M²Hub: Unlocking the Potential of Machine Learning for Materials Discovery.

Yuanqi Du, Yingheng Wang, Yining Huang, Jianan Canal Li, Yanqiao Zhu, Tian Xie, Chenru Duan, John M. Gregoire, Carla Pedro Gomes

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

A new perspective on building efficient and expressive 3D equivariant graph neural networks.

Weitao Du, Yuanqi Du, Limei Wang, Dieqiao Feng, Guifeng Wang, Shuiwang Ji, Carla P. Gomes, Zhi-Ming Ma

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

On Separate Normalization in Self-supervised Transformers.

Xiaohui Chen, Yinkai Wang, Yuanqi Du, Soha Hassoun, Liping Liu

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

Uncovering Neural Scaling Laws in Molecular Representation Learning.

Dingshuo Chen, Yanqiao Zhu, Jieyu Zhang, Yuanqi Du, Zhixun Li, Qiang Liu, Shu Wu, Liang Wang

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

The Second Learning on Graphs Conference: Preface.

Soledad Villar, Benjamin Paul Chamberlain, Yuanqi Du, Hannes Stärk, Chaitanya K. Joshi, Andreea Deac, Iulia Duta, Joshua Robinson, Yanqiao Zhu, Kexin Huang, Michelle M. Li, Sofia Bourhim, Ilia Igashov, Alexandre Duval, Mathieu Alain, Dominique Beaini, Xinyu Yuan

Proceedings of the Learning on Graphs Conference, 27-30 November 2023, Virtual Event., 2023

A Systematic Survey of Chemical Pre-trained Models.

Jun Xia, Yanqiao Zhu, Yuanqi Du, Stan Z. Li

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

A Flexible Diffusion Model.

Weitao Du, He Zhang, Tao Yang, Yuanqi Du

Proceedings of the International Conference on Machine Learning, 2023

Weighted Sampling without Replacement for Deep Top-k Classification.

Dieqiao Feng, Yuanqi Du, Carla P. Gomes, Bart Selman

Proceedings of the International Conference on Machine Learning, 2023

2022

GAUCHE: A Library for Gaussian Processes in Chemistry.

Ryan-Rhys Griffiths, Leo Klarner, Henry B. Moss, Aditya Ravuri, Sang Truong, Bojana Rankovic, Yuanqi Du, Arian R. Jamasb, Julius Schwartz, Austin Tripp, Gregory Kell, Anthony Bourached, Alex Chan, Jacob Moss, Chengzhi Guo, Alpha A. Lee, Philippe Schwaller, Jian Tang

CoRR, 2022

A Systematic Survey of Molecular Pre-trained Models.

Jun Xia, Yanqiao Zhu, Yuanqi Du, Yue Liu, Stan Z. Li

CoRR, 2022

Structure-based Drug Design with Equivariant Diffusion Models.

Arne Schneuing, Yuanqi Du, Charles Harris, Arian R. Jamasb, Ilia Igashov, Weitao Du, Tom L. Blundell, Pietro Lió, Carla Gomes, Max Welling, Michael M. Bronstein, Bruno E. Correia

CoRR, 2022

Improving Molecular Pretraining with Complementary Featurizations.

Yanqiao Zhu, Dingshuo Chen, Yuanqi Du, Yingze Wang, Qiang Liu, Shu Wu

CoRR, 2022

Controllable Data Generation by Deep Learning: A Review.

Shiyu Wang, Yuanqi Du, Xiaojie Guo, Bo Pan, Liang Zhao

CoRR, 2022

Path Integral Stochastic Optimal Control for Sampling Transition Paths.

Lars Holdijk, Yuanqi Du, Ferry Hooft, Priyank Jaini, Bernd Ensing, Max Welling

CoRR, 2022

MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design.

Yuanqi Du, Tianfan Fu, Jimeng Sun, Shengchao Liu

CoRR, 2022

Recovering medical images from CT film photos.

Quan Quan, Qiyuan Wang, Yuanqi Du, Liu Li, S. Kevin Zhou

CoRR, 2022

A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications.

Jun Xia, Yanqiao Zhu, Yuanqi Du, Stan Z. Li

CoRR, 2022

Small molecule generation via disentangled representation learning.

Yuanqi Du, Xiaojie Guo, Yinkai Wang, Amarda Shehu, Liang Zhao

Bioinform., 2022

Interpretable Molecular Graph Generation via Monotonic Constraints.

Yuanqi Du, Xiaojie Guo, Amarda Shehu, Liang Zhao

Proceedings of the 2022 SIAM International Conference on Data Mining, 2022

Multi-objective Deep Data Generation with Correlated Property Control.

Shiyu Wang, Xiaojie Guo, Xuanyang Lin, Bo Pan, Yuanqi Du, Yinkai Wang, Yanfang Ye, Ashley Ann Petersen, Austin Leitgeb, Saleh AlKhalifa, Kevin Minbiole, William M. Wuest, Amarda Shehu, Liang Zhao

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

Audio-Driven Co-Speech Gesture Video Generation.

Xian Liu, Qianyi Wu, Hang Zhou, Yuanqi Du, Wayne Wu, Dahua Lin, Ziwei Liu

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

Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks.

Arian R. Jamasb, Ramón Viñas Torné, Eric Ma, Yuanqi Du, Charles Harris, Kexin Huang, Dominic Hall, Pietro Lió, Tom L. Blundell

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

The First Learning on Graphs Conference: Preface.

Bastian Rieck, Razvan Pascanu, Yuanqi Du, Hannes Stärk, Derek Lim, Chaitanya K. Joshi, Andreea Deac, Iulia Duta, Joshua Robinson, Gabriele Corso, Leonardo Cotta, Yanqiao Zhu, Kexin Huang, Michelle M. Li, Sofia Bourhim, Ilia Igashov

Proceedings of the Learning on Graphs Conference, 2022

A Survey on Deep Graph Generation: Methods and Applications.

Yanqiao Zhu, Yuanqi Du, Yinkai Wang, Yichen Xu, Jieyu Zhang, Qiang Liu, Shu Wu

Proceedings of the Learning on Graphs Conference, 2022

Semi-Supervised Pseudo-Healthy Image Synthesis via Confidence Augmentation.

Yuanqi Du, Quan Quan, Hu Han, S. Kevin Zhou

Proceedings of the 19th IEEE International Symposium on Biomedical Imaging, 2022

SE(3) Equivariant Graph Neural Networks with Complete Local Frames.

Weitao Du, He Zhang, Yuanqi Du, Qi Meng, Wei Chen, Nanning Zheng, Bin Shao, Tie-Yan Liu

Proceedings of the International Conference on Machine Learning, 2022

Property-Controllable Generation of Quaternary Ammonium Compounds.

Bo Pan, Yinkai Wang, Xuanyang Lin, Muran Qin, Yuanqi Du, Shiva Ghaemi, Aowei Ding, Shiyu Wang, Saleh AlKhalifa, Kevin Minbiole, William M. Wuest, Ashley Ann Petersen, Austin Leitgeb, Amarda Shehu, Liang Zhao

Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2022

Disentangled Spatiotemporal Graph Generative Models.

Yuanqi Du, Xiaojie Guo, Hengning Cao, Yanfang Ye, Liang Zhao

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Dataset for Disentangled Representation Learning for Interpretable Molecule Generation.

Yuanqi Du, Xiaojie Guo, Amarda Shehu, Liang Zhao

Dataset, April, 2021

Dataset for Generative Adversarial Learning of Protein Tertiary Structures. Molecules, 2021.

Taseef Rahman, Yuanqi Du, Liang Zhao, Amarda Shehu

Dataset, February, 2021

Graph-based Ensemble Machine Learning for Student Performance Prediction.

Yinkai Wang, Aowei Ding, Kaiyi Guan, Shixi Wu, Yuanqi Du

CoRR, 2021

Equivariant vector field network for many-body system modeling.

Weitao Du, He Zhang, Yuanqi Du, Qi Meng, Wei Chen, Bin Shao, Tie-Yan Liu

CoRR, 2021

Physics-Augmented Learning: A New Paradigm Beyond Physics-Informed Learning.

Ziming Liu, Yunyue Chen, Yuanqi Du, Max Tegmark

CoRR, 2021

Where is the disease? Semi-supervised pseudo-normality synthesis from an abnormal image.

Yuanqi Du, Quan Quan, Hu Han, S. Kevin Zhou

CoRR, 2021

Deep learning to segment pelvic bones: large-scale CT datasets and baseline models.

Pengbo Liu, Hu Han, Yuanqi Du, Heqin Zhu, Yinhao Li, Feng Gu, Honghu Xiao, Jun Li, Chunpeng Zhao, Li Xiao, Xinbao Wu, S. Kevin Zhou

Int. J. Comput. Assist. Radiol. Surg., 2021

GraphGT: Machine Learning Datasets for Graph Generation and Transformation.

Yuanqi Du, Shiyu Wang, Xiaojie Guo, Hengning Cao, Shujie Hu, Junji Jiang, Aishwarya Varala, Abhinav Angirekula, Liang Zhao

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Deep Generative Models for Spatial Networks.

Xiaojie Guo, Yuanqi Du, Liang Zhao

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

Property Controllable Variational Autoencoder via Invertible Mutual Dependence.

Xiaojie Guo, Yuanqi Du, Liang Zhao

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

Graph Representation Learning for Protein Conformation Sampling.

Taseef Rahman, Yuanqi Du, Amarda Shehu

Proceedings of the Computational Advances in Bio and Medical Sciences: 11th International Conference, 2021

Ensemble Machine Learning System for Student AcademicPerformance Prediction.

Yinkai Wang, Aowei Ding, Kaiyi Guan, Yuanqi Du

Proceedings of the Joint Proceedings of the Workshops at the International Conference on Educational Data Mining 2021 co-located with 14th International Conference on Educational Data Mining (EDM 2021), 2021

Deep Latent-Variable Models for Controllable Molecule Generation.

Yuanqi Du, Yinkai Wang, Fardina Fathmiul Alam, Yuanjie Lu, Xiaojie Guo, Liang Zhao, Amarda Shehu

Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2021

2020

CT Film Recovery via Disentangling Geometric Deformation and Illumination Variation: Simulated Datasets and Deep Models.

Quan Quan, Qiyuan Wang, Liu Li, Yuanqi Du, S. Kevin Zhou

CoRR, 2020

Expressive ASL Recognition using Millimeter-wave Wireless Signals.

Panneer Selvam Santhalingam, Yuanqi Du, Riley Wilkerson, Al Amin Hosain, Ding Zhang, Parth H. Pathak, Huzefa Rangwala, Raja S. Kushalnagar

Proceedings of the 17th Annual IEEE International Conference on Sensing, 2020

American Sign Language Recognition Using an FMCW Wireless Sensor (Student Abstract).

Yuanqi Du, Nguyen Dang, Riley Wilkerson, Parth H. Pathak, Huzefa Rangwala, Jana Kosecka

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