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ORCIDWenda Zhou
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Journal Articles
- 2024
[j4]Wenda Zhou
, Shirin Jalali, Arian Maleki:
Corrections to "Compressed Sensing in the Presence of Speckle Noise". IEEE Trans. Inf. Theory 70(12): 9352-9354 (2024)- 2023
- [j3]Li Dong, Lie Ju, Shiqi Hui, Lihua Luo, Xue Jiang, Zihan Nie, Ruiheng Zhang, Wenda Zhou, Heyan Li, Jost B. Jonas, Xin Wang, Xin Zhao, Chao He, Yuzhong Chen, Zhaohui Wang, Jianxiong Gao, Zongyuan Ge, Wenbin Wei, Dongmei Li:
Retinal photograph-based deep learning system for detection of hyperthyroidism: a multicenter, diagnostic study. J. Big Data 10(1): 134 (2023) - 2022
- [j2]Wenda Zhou, Li Dong, Kai Zhang, Qian Wang, Lei Shao, Qiong Yang, Yueming Liu, Li-Jian Fang, Xu-Han Shi, Chuan Zhang, Ruiheng Zhang, He-Yan Li, Hao-Tian Wu, Wen-Bin Wei:
Deep Learning for Automatic Detection of Recurrent Retinal Detachment after Surgery Using Ultra-Widefield Fundus Images: A Single-Center Study. Adv. Intell. Syst. 4(9) (2022) - [j1]Wenda Zhou, Shirin Jalali, Arian Maleki:
Compressed Sensing in the Presence of Speckle Noise. IEEE Trans. Inf. Theory 68(10): 6964-6980 (2022)
Conference and Workshop Papers
- 2022
- [c8]Ari Seff, Wenda Zhou, Nick Richardson, Ryan P. Adams:
Vitruvion: A Generative Model of Parametric CAD Sketches. ICLR 2022 - 2021
- [c7]Erik H. Thiede, Wenda Zhou, Risi Kondor:
Autobahn: Automorphism-based Graph Neural Nets. NeurIPS 2021: 29922-29934 - 2020
- [c6]Kamiar Rahnama Rad, Wenda Zhou, Arian Maleki:
Error bounds in estimating the out-of-sample prediction error using leave-one-out cross validation in high-dimensions. AISTATS 2020: 4067-4077 - 2019
- [c5]Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz:
Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data. AISTATS 2019: 1733-1742 - [c4]Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz:
Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach. ICLR (Poster) 2019 - [c3]Wenda Zhou, Shirin Jalali:
Towards theoretically-founded learning-based denoising. ISIT 2019: 2714-2718 - [c2]Ari Seff, Wenda Zhou, Farhan N. Damani, Abigail G. Doyle, Ryan P. Adams:
Discrete Object Generation with Reversible Inductive Construction. NeurIPS 2019: 10353-10363 - 2018
- [c1]Shuaiwen Wang, Wenda Zhou, Haihao Lu, Arian Maleki, Vahab S. Mirrokni:
Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions. ICML 2018: 5215-5224
Informal and Other Publications
- 2024
- [i12]Zheng Gong, Zhuo Deng, Weihao Gao, Wenda Zhou, Yuhang Yang, Hanqing Zhao, Zhiyuan Niu, Lei Shao, Wen-Bin Wei, Lan Ma:
Versatile Cataract Fundus Image Restoration Model Utilizing Unpaired Cataract and High-quality Images. CoRR abs/2411.12278 (2024) - 2021
- [i11]Erik Henning Thiede, Wenda Zhou, Risi Kondor:
Autobahn: Automorphism-based Graph Neural Nets. CoRR abs/2103.01710 (2021) - [i10]Wenda Zhou, Shirin Jalali, Arian Maleki:
Compressed sensing in the presence of speckle noise. CoRR abs/2108.00329 (2021) - [i9]Ari Seff, Wenda Zhou, Nick Richardson, Ryan P. Adams:
Vitruvion: A Generative Model of Parametric CAD Sketches. CoRR abs/2109.14124 (2021) - 2020
- [i8]Kamiar Rahnama Rad, Wenda Zhou, Arian Maleki:
Error bounds in estimating the out-of-sample prediction error using leave-one-out cross validation in high-dimensions. CoRR abs/2003.01770 (2020) - [i7]Ari Seff, Yaniv Ovadia, Wenda Zhou, Ryan P. Adams:
SketchGraphs: A Large-Scale Dataset for Modeling Relational Geometry in Computer-Aided Design. CoRR abs/2007.08506 (2020) - 2019
- [i6]Wenda Zhou, Shirin Jalali:
Denoising of structured random processes. CoRR abs/1901.05937 (2019) - [i5]Ari Seff, Wenda Zhou, Farhan N. Damani, Abigail G. Doyle, Ryan P. Adams:
Discrete Object Generation with Reversible Inductive Construction. CoRR abs/1907.08268 (2019) - 2018
- [i4]Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz:
Compressibility and Generalization in Large-Scale Deep Learning. CoRR abs/1804.05862 (2018) - [i3]Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz:
Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data. CoRR abs/1806.10701 (2018) - [i2]Shuaiwen Wang, Wenda Zhou, Haihao Lu, Arian Maleki, Vahab S. Mirrokni:
Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions. CoRR abs/1807.02694 (2018) - [i1]Shuaiwen Wang, Wenda Zhou, Arian Maleki, Haihao Lu, Vahab S. Mirrokni:
Approximate Leave-One-Out for High-Dimensional Non-Differentiable Learning Problems. CoRR abs/1810.02716 (2018)
Coauthor Index
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