Lei Xu
Lei Xu
EECS, MIT
Verified email at mit.edu - Homepage
Title
Cited by
Cited by
Year
Joint learning of character and word embeddings
X Chen, L Xu, Z Liu, M Sun, H Luan
Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015
2502015
Input convex neural networks
B Amos, L Xu, JZ Kolter
International Conference on Machine Learning, 146-155, 2017
1002017
Modeling tabular data using conditional gan
L Xu, M Skoularidou, A Cuesta-Infante, K Veeramachaneni
Advances in Neural Information Processing Systems, 7335-7345, 2019
452019
Synthesizing tabular data using generative adversarial networks
L Xu, K Veeramachaneni
arXiv preprint arXiv:1811.11264, 2018
222018
SteganoGAN: High capacity image steganography with GANs
KA Zhang, A Cuesta-Infante, L Xu, K Veeramachaneni
arXiv preprint arXiv:1901.03892, 2019
212019
Robust invisible video watermarking with attention
KA Zhang, L Xu, A Cuesta-Infante, K Veeramachaneni
arXiv preprint arXiv:1909.01285, 2019
32019
Topic sensitive neural headline generation
L Xu, Z Wang, Z Liu, M Sun
arXiv preprint arXiv:1608.05777, 2016
32016
MLFriend: Interactive Prediction Task Recommendation for Event-Driven Time-Series Data
L Xu, SKK Santu, K Veeramachaneni
arXiv preprint arXiv:1906.12348, 2019
22019
Synthesizing Tabular Data using Conditional GAN
L Xu
Massachusetts Institute of Technology, 2020
12020
Rewriting Meaningful Sentences via Conditional BERT Sampling and an application on fooling text classifiers
L Xu, I Ramirez, K Veeramachaneni
arXiv preprint arXiv:2010.11869, 2020
2020
A Level-wise Taxonomic Perspective on Automated Machine Learning to Date and Beyond: Challenges and Opportunities
M Hassan, MJ Smith, L Xu, CX Zhai, K Veeramachaneni
arXiv preprint arXiv:2010.10777, 2020
2020
A Level-wise Taxonomic Perspective on Automated Machine Learning to Date and Beyond: Challenges and Opportunities
M Mahadi Hassan, MJ Smith, L Xu, CX Zhai, K Veeramachaneni
arXiv e-prints, arXiv: 2010.10777, 2020
2020
A Level-wise Taxonomic Perspective on Automated Machine Learning to Date and Beyond: Challenges and Opportunities
MJ SMITH, LEI XU, K VEERAMACHANENI
2018
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