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2020 – today
- 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)- [c8]Jinhang Li, Shibo Li, Zili Yang, Tailin Wu, Ying Hu:
An Automatic Scoliosis Diagnosis Platform Based on Deep Learning Approach. APIT 2022: 215-223 - [c7]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 - [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
- [j2]Max Tegmark
, Tailin Wu:
Pareto-Optimal Data Compression for Binary Classification Tasks. Entropy 22(1): 7 (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 - [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)
2010 – 2019
- 2019
- [j1]Tailin Wu, Ian S. Fischer, Isaac L. Chuang, Max Tegmark:
Learnability for the Information Bottleneck. Entropy 21(10): 924 (2019) - [c2]Tailin Wu, Ian S. Fischer, Isaac L. Chuang, Max Tegmark:
Learnability for the Information Bottleneck. UAI 2019: 1050-1060 - [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
- [c1]Curtis G. Northcutt, Tailin Wu, Isaac L. Chuang:
Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels. UAI 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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