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ORCIDTailin Wu
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Journal Articles
- 2022
[j3]Michael Skuhersky, Tailin Wu, Eviatar Yemini, Amin Nejatbakhsh, Edward S. Boyden, Max Tegmark:
Toward a more accurate 3D atlas of C. elegans neurons. BMC Bioinform. 23(1): 195 (2022)- 2020
- [j2]Max Tegmark
, Tailin Wu:
Pareto-Optimal Data Compression for Binary Classification Tasks. Entropy 22(1): 7 (2020) - 2019
- [j1]Tailin Wu, Ian S. Fischer, Isaac L. Chuang, Max Tegmark:
Learnability for the Information Bottleneck. Entropy 21(10): 924 (2019)
Conference and Workshop Papers
- 2024
- [c15]Tailin Wu, Willie Neiswanger, Hongtao Zheng, Stefano Ermon, Jure Leskovec:
Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution. AAAI 2024: 320-328 - [c14]Haixin Wang, Jiaxin Li, Anubhav Dwivedi, Kentaro Hara, Tailin Wu:
BENO: Boundary-embedded Neural Operators for Elliptic PDEs. ICLR 2024 - [c13]Tailin Wu, Takashi Maruyama, Long Wei, Tao Zhang, Yilun Du, Gianluca Iaccarino, Jure Leskovec:
Compositional Generative Inverse Design. ICLR 2024 - 2023
- [c12]Tailin Wu, Takashi Maruyama, Qingqing Zhao, Gordon Wetzstein, Jure Leskovec:
Learning Controllable Adaptive Simulation for Multi-resolution Physics. ICLR 2023 - [c11]Yefeng Liang, Shibo Li, Xingyu Li, Yucheng He, Ying Hu, Tailin Wu, Huiren Tao:
Robust X-ray Image Stitching Algorithm Based on Refining Matching Results of Feature Descriptors. ISBI 2023: 1-5 - 2022
- [c10]Jinhang Li, Shibo Li, Zili Yang, Tailin Wu, Ying Hu:
An Automatic Scoliosis Diagnosis Platform Based on Deep Learning Approach. APIT 2022: 215-223 - [c9]Tailin Wu, Qinchen Wang, Yinan Zhang, Rex Ying, Kaidi Cao, Rok Sosic, Ridwan Jalali, Hassan Hamam, Marko Maucec, Jure Leskovec:
Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator. KDD 2022: 4184-4194 - [c8]Tailin Wu, Takashi Maruyama, Jure Leskovec:
Learning to Accelerate Partial Differential Equations via Latent Global Evolution. NeurIPS 2022 - [c7]Tailin Wu, Megan Tjandrasuwita, Zhengxuan Wu, Xuelin Yang, Kevin Liu, Rok Sosic, Jure Leskovec:
ZeroC: A Neuro-Symbolic Model for Zero-shot Concept Recognition and Acquisition at Inference Time. NeurIPS 2022 - 2020
- [c6]Tailin Wu, Ian S. Fischer:
Phase Transitions for the Information Bottleneck in Representation Learning. ICLR 2020 - [c5]Yanying Lin, Kejiang Ye, Ming Chen, Naitian Deng, Tailin Wu, Cheng-Zhong Xu:
LBNN: Perceiving the State Changes of a Core Telecommunications Network via Linear Bayesian Neural Network. ICPADS 2020: 72-80 - [c4]Silviu-Marian Udrescu, Andrew K. Tan, Jiahai Feng, Orisvaldo Neto, Tailin Wu, Max Tegmark:
AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity. NeurIPS 2020 - [c3]Tailin Wu, Hongyu Ren, Pan Li, Jure Leskovec:
Graph Information Bottleneck. NeurIPS 2020 - 2019
- [c2]Tailin Wu, Ian S. Fischer, Isaac L. Chuang, Max Tegmark:
Learnability for the Information Bottleneck. UAI 2019: 1050-1060 - 2017
- [c1]Curtis G. Northcutt, Tailin Wu, Isaac L. Chuang:
Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels. UAI 2017
Informal and Other Publications
- 2024
- [i25]Haixin Wang, Jiaxin Li, Anubhav Dwivedi, Kentaro Hara, Tailin Wu:
BENO: Boundary-embedded Neural Operators for Elliptic PDEs. CoRR abs/2401.09323 (2024) - [i24]Tailin Wu, Takashi Maruyama, Long Wei, Tao Zhang, Yilun Du, Gianluca Iaccarino, Jure Leskovec:
Compositional Generative Inverse Design. CoRR abs/2401.13171 (2024) - [i23]Tailin Wu, Willie Neiswanger, Hongtao Zheng, Stefano Ermon, Jure Leskovec:
Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution. CoRR abs/2402.08383 (2024) - [i22]Qianru Zhang, Haixin Wang, Cheng Long, Liangcai Su, Xingwei He, Jianlong Chang, Tailin Wu, Hongzhi Yin, Siu-Ming Yiu, Qi Tian, Christian S. Jensen:
A Survey of Generative Techniques for Spatial-Temporal Data Mining. CoRR abs/2405.09592 (2024) - [i21]Bryan E. Kaiser, Tailin Wu, Maike Sonnewald, Colin Thackray, Skylar Callis:
A Moonshot for AI Oracles in the Sciences. CoRR abs/2406.17836 (2024) - [i20]Long Wei, Peiyan Hu, Ruiqi Feng, Haodong Feng, Yixuan Du, Tao Zhang, Rui Wang, Yue Wang, Zhi-Ming Ma, Tailin Wu:
A Generative Approach to Control Complex Physical Systems. CoRR abs/2407.06494 (2024) - [i19]Long Wei, Haodong Feng, Peiyan Hu, Tao Zhang, Yuchen Yang, Xiang Zheng, Ruiqi Feng, Dixia Fan, Tailin Wu:
Closed-loop Diffusion Control of Complex Physical Systems. CoRR abs/2408.03124 (2024) - [i18]Haixin Wang, Yadi Cao, Zijie Huang, Yuxuan Liu, Peiyan Hu, Xiao Luo, Zezheng Song, Wanjia Zhao, Jilin Liu, Jinan Sun, Shikun Zhang, Long Wei, Yue Wang, Tailin Wu, Zhi-Ming Ma, Yizhou Sun:
Recent Advances on Machine Learning for Computational Fluid Dynamics: A Survey. CoRR abs/2408.12171 (2024) - 2023
- [i17]Tailin Wu, Takashi Maruyama, Qingqing Zhao, Gordon Wetzstein, Jure Leskovec:
Learning Controllable Adaptive Simulation for Multi-resolution Physics. CoRR abs/2305.01122 (2023) - [i16]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:
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. CoRR abs/2307.08423 (2023) - [i15]Zhongyi Han, Guanglin Zhou, Rundong He, Jindong Wang, Tailin Wu, Yilong Yin, Salman H. Khan, Lina Yao, Tongliang Liu, Kun Zhang:
How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation. CoRR abs/2312.07424 (2023) - 2022
- [i14]Tailin Wu, Qinchen Wang, Yinan Zhang, Rex Ying, Kaidi Cao, Rok Sosic, Ridwan Jalali, Hassan Hamam, Marko Maucec, Jure Leskovec:
Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator. CoRR abs/2206.07680 (2022) - [i13]Tailin Wu, Takashi Maruyama, Jure Leskovec:
Learning to Accelerate Partial Differential Equations via Latent Global Evolution. CoRR abs/2206.07681 (2022) - [i12]Tailin Wu, Megan Tjandrasuwita, Zhengxuan Wu, Xuelin Yang, Kevin Liu, Rok Sosic, Jure Leskovec:
ZeroC: A Neuro-Symbolic Model for Zero-shot Concept Recognition and Acquisition at Inference Time. CoRR abs/2206.15049 (2022) - [i11]Daniel Zeng, Tailin Wu, Jure Leskovec:
ViRel: Unsupervised Visual Relations Discovery with Graph-level Analogy. CoRR abs/2207.00590 (2022) - 2020
- [i10]Tailin Wu, Ian S. Fischer:
Phase Transitions for the Information Bottleneck in Representation Learning. CoRR abs/2001.01878 (2020) - [i9]Tailin Wu, Thomas M. Breuel, Michael Skuhersky, Jan Kautz:
Discovering Nonlinear Relations with Minimum Predictive Information Regularization. CoRR abs/2001.01885 (2020) - [i8]Tailin Wu:
Intelligence, physics and information - the tradeoff between accuracy and simplicity in machine learning. CoRR abs/2001.03780 (2020) - [i7]Silviu-Marian Udrescu, Andrew K. Tan, Jiahai Feng, Orisvaldo Neto, Tailin Wu, Max Tegmark:
AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity. CoRR abs/2006.10782 (2020) - [i6]Tailin Wu, Hongyu Ren, Pan Li, Jure Leskovec:
Graph Information Bottleneck. CoRR abs/2010.12811 (2020) - 2019
- [i5]Tailin Wu, Ian S. Fischer, Isaac L. Chuang, Max Tegmark:
Learnability for the Information Bottleneck. CoRR abs/1907.07331 (2019) - [i4]Max Tegmark, Tailin Wu:
Pareto-optimal data compression for binary classification tasks. CoRR abs/1908.08961 (2019) - 2018
- [i3]Tailin Wu, John Peurifoy, Isaac L. Chuang, Max Tegmark:
Meta-learning autoencoders for few-shot prediction. CoRR abs/1807.09912 (2018) - [i2]Tailin Wu, Max Tegmark:
Toward an AI Physicist for Unsupervised Learning. CoRR abs/1810.10525 (2018) - 2017
- [i1]Curtis G. Northcutt, Tailin Wu, Isaac L. Chuang:
Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels. CoRR abs/1705.01936 (2017)
Coauthor Index
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