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2020 – today
- 2023
[i5]Hang Zhou, Jonas Mueller, Mayank Kumar, Jane-Ling Wang, Jing Lei:
Detecting Errors in Numerical Data via any Regression Model. CoRR abs/2305.16583 (2023)- 2022
- [j11]Ci-Ren Jiang
, Eardi Lila, John A. D. Aston, Jane-Ling Wang:
Eigen-Adjusted Functional Principal Component Analysis. J. Comput. Graph. Stat. 31(4): 1141-1152 (2022) - 2021
- [j10]Yaqing Chen, Paromita Dubey, Hans-Georg Müller, Muriel Bruchhage, Jane-Ling Wang, Sean C. L. Deoni:
Modeling sparse longitudinal data in early neurodevelopment. NeuroImage 237: 118079 (2021) - [c4]Junwen Yao, Jonas Mueller, Jane-Ling Wang:
Deep Learning for Functional Data Analysis with Adaptive Basis Layers. ICML 2021: 11898-11908 - [c3]Qixian Zhong, Jonas Mueller, Jane-Ling Wang:
Deep Extended Hazard Models for Survival Analysis. NeurIPS 2021: 15111-15124 - [i4]Seong-Eun Moon, Chun-Jui Chen, Cho-Jui Hsieh, Jane-Ling Wang, Jong-Seok Lee:
Emotional EEG Classification using Connectivity Features and Convolutional Neural Networks. CoRR abs/2101.07069 (2021) - [i3]Junwen Yao, Jonas Mueller, Jane-Ling Wang:
Deep Learning for Functional Data Analysis with Adaptive Basis Layers. CoRR abs/2106.10414 (2021) - 2020
- [j9]Xiaoke Zhang, Qixian Zhong, Jane-Ling Wang:
A new approach to varying-coefficient additive models with longitudinal covariates. Comput. Stat. Data Anal. 145: 106912 (2020) - [j8]Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, Michael I. Jordan:
Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data. J. Mach. Learn. Res. 21: 43:1-43:36 (2020) - [j7]Seong-Eun Moon, Chun-Jui Chen, Cho-Jui Hsieh, Jane-Ling Wang, Jong-Seok Lee:
Emotional EEG classification using connectivity features and convolutional neural networks. Neural Networks 132: 96-107 (2020) - [c2]Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, Michael I. Jordan:
ML-LOO: Detecting Adversarial Examples with Feature Attribution. AAAI 2020: 6639-6647
2010 – 2019
- 2019
- [j6]Chun-Jui Chen, Jane-Ling Wang:
A New Approach for Functional Connectivity via Alignment of Blood Oxygen Level-Dependent Signals. Brain Connect. 9(6): 464-474 (2019) - [j5]Frédéric Ferraty, Piotr Kokoszka, Jane-Ling Wang, Yichao Wu:
Editorial for the special issue on High-dimensional and functional data analysis. Comput. Stat. Data Anal. 131: 10-11 (2019) - [i2]Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, Michael I. Jordan:
ML-LOO: Detecting Adversarial Examples with Feature Attribution. CoRR abs/1906.03499 (2019) - 2018
- [j4]Yang Zhou, Shu-Chin Lin, Jane-Ling Wang:
Local and global temporal correlations for longitudinal data. J. Multivar. Anal. 167: 1-14 (2018) - [i1]Puyudi Yang, Jianbo Chen, Cho-Jui Hsieh, Jane-Ling Wang, Michael I. Jordan:
Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data. CoRR abs/1805.12316 (2018) - 2014
- [j3]Jesse J. Bengson, Todd A. Kelley, Xiaoke Zhang, Jane-Ling Wang, George R. Mangun:
Spontaneous Neural Fluctuations Predict Decisions to Attend. J. Cogn. Neurosci. 26(11): 2578-2584 (2014) - [j2]Ying Yuan, John H. Gilmore, Xiujuan Geng, Martin Andreas Styner, Kehui Chen, Jane-Ling Wang, Hongtu Zhu:
FMEM: Functional mixed effects modeling for the analysis of longitudinal white matter Tract data. NeuroImage 84: 753-764 (2014) - 2013
- [c1]Ying Yuan, John H. Gilmore, Xiujuan Geng, Martin Andreas Styner, Kehui Chen, Jane-Ling Wang, Hongtu Zhu:
A Longitudinal Functional Analysis Framework for Analysis of White Matter Tract Statistics. IPMI 2013: 220-231
2000 – 2009
- 2009
- [j1]Ci-Ren Jiang, John A. D. Aston, Jane-Ling Wang:
Smoothing dynamic positron emission tomography time courses using functional principal components. NeuroImage 47(1): 184-193 (2009)
Coauthor Index
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