| Graph Information Bottleneck T Wu, H Ren, P Li, J Leskovec Neural Information Processing Systems (NeurIPS 2020), https://arxiv.org/abs …, 2020 | 189 | 2020 |
| AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity SM Udrescu, A Tan, J Feng, Orisvaldo Neto, T Wu, M Tegmark Neural Information Processing Systems (NeurIPS 2020) Oral, arXiv preprint …, 2020 | 185 | 2020 |
| Learning with confident examples: Rank pruning for robust classification with noisy labels CG Northcutt, T Wu, IL Chuang Conference on Uncertainty in Artificial Intelligence (UAI 2017), 2017 | 180 | 2017 |
| Toward an artificial intelligence physicist for unsupervised learning T Wu, M Tegmark Physical Review E 100 (3), 033311, 2019 | 125 | 2019 |
| Toward an AI physicist for unsupervised learning T Wu, M Tegmark Physical Review E 100 (3), 033311, 2018 | 125* | 2018 |
| Pathway-Based Mean-Field Model for Escherichia coli Chemotaxis G Si, T Wu, Q Ouyang, Y Tu Physical review letters 109 (4), 048101, 2012 | 56 | 2012 |
| Frequency-Dependent Escherichia coli Chemotaxis Behavior X Zhu, G Si, N Deng, Q Ouyang, T Wu, Z He, L Jiang, C Luo, Y Tu Physical review letters 108 (12), 128101, 2012 | 55 | 2012 |
| Preventing and reversing vacuum-induced optical losses in high-finesse tantalum (V) oxide mirror coatings D Gangloff, M Shi, T Wu, A Bylinskii, B Braverman, M Gutierrez, R Nichols, ... Optics express 23 (14), 18014-18028, 2015 | 49 | 2015 |
| Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems X Zhang, L Wang, J Helwig, Y Luo, C Fu, Y Xie, M Liu, Y Lin, Z Xu, K Yan, ... https://arxiv.org/abs/2307.08423, 2023 | 45 | 2023 |
| Learnability for the Information Bottleneck T Wu, I Fischer, I Chuang, M Tegmark Conference on Uncertainty in Artificial Intelligence (UAI 2019), arXiv …, 2019 | 38 | 2019 |
| Discovering Nonlinear Relations with Minimum Predictive Information Regularization T Wu, T Breuel, M Skuhersky, J Kautz ICML 2019 Time Series Workshop; arXiv preprint arXiv:2001.01885, 2020 | 29 | 2020 |
| Phase transitions for the Information Bottleneck in representation learning T Wu, I Fischer International Conference on Learning Representations (ICLR 2020), arXiv:2001 …, 2020 | 26 | 2020 |
| Learning to Accelerate Partial Differential Equations via Latent Global Evolution T Wu, T Maruyama, J Leskovec Neural Information Processing Systems (NeurIPS 2022), arXiv preprint arXiv …, 2022 | 23 | 2022 |
| Meta-learning autoencoders for few-shot prediction T Wu, J Peurifoy, IL Chuang, M Tegmark arXiv preprint arXiv:1807.09912, 2018 | 23 | 2018 |
| ZeroC: A Neuro-Symbolic Model for Zero-shot Concept Recognition and Acquisition at Inference Time JL Tailin Wu, Megan Tjandrasuwita, Zhengxuan Wu, Xuelin Yang, Kevin Liu, Rok ... Neural Information Processing Systems (NeurIPS 2022), arXiv preprint arXiv …, 2022 | 20* | 2022 |
| Iterative precision measurement of branching ratios applied to 5P states in 88Sr+ H Zhang, M Gutierrez, GH Low, R Rines, J Stuart, T Wu, I Chuang New Journal of Physics 18 (12), 123021, 2016 | 19 | 2016 |
| Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator T Wu, Q Wang, Y Zhang, R Ying, K Cao, R Sosič, R Jalali, H Hamam, ... 28th ACM SIGKDD Conference (KDD'22), 2022 | 16 | 2022 |
| Pareto-optimal data compression for binary classification tasks M Tegmark, T Wu Entropy 2020 22 (1), 7, 2019 | 15 | 2019 |
| Learning Controllable Adaptive Simulation for Multi-resolution Physics T Wu, T Maruyama, Q Zhao, G Wetzstein, J Leskovec International Conference on Learning Representations (ICLR 2023), spotlight …, 2023 | 8 | 2023 |
| Ai feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity (2020) SM Udrescu, A Tan, J Feng, O Neto, T Wu, M Tegmark arXiv preprint arXiv:2006.10782, 2006 | 6 | 2006 |
Jure LeskovecProfessor of Computer Science, Stanford UniversityZweryfikowany adres z cs.stanford.edu
