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Tailin Wu
Tailin Wu
Postdoctoral Scholar at Stanford University
Verified email at cs.stanford.edu - Homepage
Title
Cited by
Cited by
Year
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
1202017
Toward an artificial intelligence physicist for unsupervised learning
T Wu, M Tegmark
Physical Review E 100 (3), 033311, 2019
772019
Toward an AI physicist for unsupervised learning
T Wu, M Tegmark
Physical Review E 100 (3), 033311, 2018
77*2018
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
602020
Graph Information Bottleneck
T Wu, H Ren, P Li, J Leskovec
Neural Information Processing Systems (NeurIPS 2020), https://arxiv.org/abs …, 2020
582020
Pathway-Based Mean-Field Model for Escherichia coli Chemotaxis
G Si, T Wu, Q Ouyang, Y Tu
Physical review letters 109 (4), 048101, 2012
532012
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
512012
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
382015
Learnability for the Information Bottleneck
T Wu, I Fischer, I Chuang, M Tegmark
Conference on Uncertainty in Artificial Intelligence (UAI 2019), arXiv …, 2019
282019
Meta-learning autoencoders for few-shot prediction
T Wu, J Peurifoy, IL Chuang, M Tegmark
arXiv preprint arXiv:1807.09912, 2018
202018
Phase transitions for the Information Bottleneck in representation learning
T Wu, I Fischer
International Conference on Learning Representations (ICLR 2020), arXiv:2001 …, 2020
172020
Pareto-optimal data compression for binary classification tasks
M Tegmark, T Wu
Entropy 2020 22 (1), 7, 2019
142019
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
142016
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
122020
A population-level model from the microscopic dynamics in Escherichia coli chemotaxis via Langevin approximation
ZR He, TL Wu, Q Ouyang, YH Tu
Chinese Physics B 21 (9), 098701, 2012
32012
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 ...
NeurIPS 2022, arXiv preprint arXiv:2206.15049, 2022
2*2022
Learning to Accelerate Partial Differential Equations via Latent Global Evolution
T Wu, T Maruyama, J Leskovec
NeurIPS 2022, arXiv preprint arXiv:2206.07681, 2022
12022
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
12022
ViRel: Unsupervised Visual Relations Discovery with Graph-level Analogy
D Zeng, T Wu, J Leskovec
ICML Beyond Bayes: Paths Towards Universal Reasoning Systems Workshop, arXiv …, 2022
2022
Toward a more accurate 3D atlas of C. elegans neurons
M Skuhersky, T Wu, E Yemini, A Nejatbakhsh, E Boyden, M Tegmark
BMC bioinformatics 23 (1), 1-18, 2022
2022
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Articles 1–20