Tailin Wu

According to our database, Tailin Wu authored at least 24 papers between 2017 and 2024.

Collaborative distances:

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

2024

Compositional Generative Inverse Design.

Tailin Wu, Takashi Maruyama, Long Wei, Tao Zhang, Yilun Du, Gianluca Iaccarino, Jure Leskovec

CoRR, 2024

BENO: Boundary-embedded Neural Operators for Elliptic PDEs.

Haixin Wang, Jiaxin Li, Anubhav Dwivedi, Kentaro Hara, Tailin Wu

CoRR, 2024

Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution.

Tailin Wu, Willie Neiswanger, Hongtao Zheng, Stefano Ermon, Jure Leskovec

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation.

Zhongyi Han, Guanglin Zhou, Rundong He, Jindong Wang, Tailin Wu, Yilong Yin, Salman H. Khan, Lina Yao, Tongliang Liu, Kun Zhang

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

Robust X-ray Image Stitching Algorithm Based on Refining Matching Results of Feature Descriptors.

Yefeng Liang, Shibo Li, Xingyu Li, Yucheng He, Ying Hu, Tailin Wu, Huiren Tao

Proceedings of the 20th IEEE International Symposium on Biomedical Imaging, 2023

Learning Controllable Adaptive Simulation for Multi-resolution Physics.

Tailin Wu, Takashi Maruyama, Qingqing Zhao, Gordon Wetzstein, Jure Leskovec

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

ViRel: Unsupervised Visual Relations Discovery with Graph-level Analogy.

Daniel Zeng, Tailin Wu, Jure Leskovec

CoRR, 2022

Toward a more accurate 3D atlas of C. elegans neurons.

Michael Skuhersky, Tailin Wu, Eviatar Yemini, Amin Nejatbakhsh, Edward S. Boyden, Max Tegmark

BMC Bioinform., 2022

ZeroC: A Neuro-Symbolic Model for Zero-shot Concept Recognition and Acquisition at Inference Time.

Tailin Wu, Megan Tjandrasuwita, Zhengxuan Wu, Xuelin Yang, Kevin Liu, Rok Sosic, Jure Leskovec

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

Learning to Accelerate Partial Differential Equations via Latent Global Evolution.

Tailin Wu, Takashi Maruyama, Jure Leskovec

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

Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator.

Tailin Wu, Qinchen Wang, Yinan Zhang, Rex Ying, Kaidi Cao, Rok Sosic, Ridwan Jalali, Hassan Hamam, Marko Maucec, Jure Leskovec

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

An Automatic Scoliosis Diagnosis Platform Based on Deep Learning Approach.

Jinhang Li, Shibo Li, Zili Yang, Tailin Wu, Ying Hu

Proceedings of the APIT 2022: 4th Asia Pacific Information Technology Conference, Virtual Event, Thailand, January 14, 2022

2020

Pareto-Optimal Data Compression for Binary Classification Tasks.

Max Tegmark, Tailin Wu

Entropy, 2020

Intelligence, physics and information - the tradeoff between accuracy and simplicity in machine learning.

Tailin Wu

CoRR, 2020

Discovering Nonlinear Relations with Minimum Predictive Information Regularization.

Tailin Wu, Thomas M. Breuel, Michael Skuhersky, Jan Kautz

CoRR, 2020

Graph Information Bottleneck.

Tailin Wu, Hongyu Ren, Pan Li, Jure Leskovec

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity.

Silviu-Marian Udrescu, Andrew K. Tan, Jiahai Feng, Orisvaldo Neto, Tailin Wu, Max Tegmark

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

LBNN: Perceiving the State Changes of a Core Telecommunications Network via Linear Bayesian Neural Network.

Yanying Lin, Kejiang Ye, Ming Chen, Naitian Deng, Tailin Wu, Cheng-Zhong Xu

Proceedings of the 26th IEEE International Conference on Parallel and Distributed Systems, 2020

Phase Transitions for the Information Bottleneck in Representation Learning.

Tailin Wu, Ian S. Fischer

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

2019

Learnability for the Information Bottleneck.

Tailin Wu, Ian S. Fischer, Isaac L. Chuang, Max Tegmark

Entropy, 2019

2018

Toward an AI Physicist for Unsupervised Learning.

Tailin Wu, Max Tegmark

CoRR, 2018

Meta-learning autoencoders for few-shot prediction.

Tailin Wu, John Peurifoy, Isaac L. Chuang, Max Tegmark

CoRR, 2018

2017

Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels.

Curtis G. Northcutt, Tailin Wu, Isaac L. Chuang

Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017