Shanghang Zhang

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Jose M. F. MouraPhilip L. and Marsha Dowd University Professor, Carnegie Mellon UniversityVerified email at ece.cmu.edu
Kurt KeutzerProfessor of the Graduate School, EECS, University of California, BerkeleyVerified email at berkeley.edu
Jianxin LiSchool of Computer Science & Engineering, Beihang UniversityVerified email at act.buaa.edu.cn
Haoyi ZhouAssistant Professor, Beihang UniversityVerified email at buaa.edu.cn
Guanhang WuGoogle Inc / CMU RoboticsVerified email at google.com
Han ZhaoAssistant Professor of Computer Science, University of Illinois at Urbana-ChampaignVerified email at illinois.edu
Xiangyu YueEECS, University of California, BerkeleyVerified email at eecs.berkeley.edu
Sicheng ZhaoTsinghua UniversityVerified email at tsinghua.edu.cn
Trevor DarrellProfessor of Computer Science, U.C. BerkeleyVerified email at eecs.berkeley.edu
Geoff GordonProfessor of Machine Learning, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Jian DuTikTok, US (was CMU (was McGill (was HKU)))Verified email at andrew.cmu.edu
Alberto Sangiovanni VincentelliProfessor of EECS, University of California, BerkeleyVerified email at eecs.berkeley.edu
Xuezhe MaInformation Sciences Institute, University of Southern CaliforniaVerified email at usc.edu
Soummya KarElectrical and Computer Engineering, Carnegie Mellon UniversityVerified email at andrew.cmu.edu
Zhe L. LinSenior Principal Scientist, Adobe ResearchVerified email at adobe.com
Radomir MechAdobe SystemsVerified email at adobe.com
Swarun KumarAssociate Professor, CMUVerified email at cmu.edu
Satwik KotturResearch Scientist, Facebook AIVerified email at fb.com
Xiaohui ShenByteDance ResearchVerified email at bytedance.com

Shanghang Zhang

EECS, UC Berkeley

Verified email at eecs.berkeley.edu - Homepage

Machine Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Informer: Beyond efficient transformer for long sequence time-series forecasting H Zhou, S Zhang, J Peng, S Zhang, J Li, H Xiong, W Zhang Proceedings of the AAAI conference on artificial intelligence 35 (12), 11106 …, 2021	852	2021
COVID-CT-Dataset: a CT scan dataset about COVID-19 X Yang, X He, J Zhao, Y Zhang, S Zhang, P Xie arXiv preprint arXiv:2003.13865, 2020	780*	2020
Adversarial multiple source domain adaptation H Zhao, S Zhang, G Wu, JMF Moura, JP Costeira, GJ Gordon Advances in neural information processing systems 31, 8559-8570, 2018	410	2018
Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans X He, X Yang, S Zhang*, J Zhao, Y Zhang, E Xing, P Xie medRxiv, 2020	341	2020
FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras S Zhang, G Wu, JP Costeira, JMF Moura International Conference on Computer Vision (ICCV), 2017	209	2017
Topology adaptive graph convolutional networks J Du, S Zhang, G Wu, JMF Moura, S Kar arXiv:1710.10370, 2017	190	2017
Universal Self-Training for Unsupervised Domain Adaptation K Mei, C Zhu, J Zou, S Zhang European Conference on Computer Vision (ECCV), 2020	163*	2020
Understanding Traffic Density from Large-Scale Web Camera Data S Zhang, G Wu, JP Costeira, JMF Moura IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017	140	2017
Multi-source Distilling Domain Adaptation S Zhao, G Wang, S Zhang*, Y Gu, Y Li, Z Song, P Xu, R Hu, H Chai, ... Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020	134	2020
A review of single-source deep unsupervised visual domain adaptation S Zhao, X Yue, S Zhang, B Li, H Zhao, B Wu, R Krishna, JE Gonzalez, ... IEEE Transactions on Neural Networks and Learning Systems, 2020	123	2020
Deep Reinforcement Learning H Dong, Z Ding, S Zhang Springer Singapore, 2020	118	2020
A Deep Learning Approach to IoT Authentication R Das, A Gadre, S Zhang, S Kumar, J Moura IEEE ICC 2018 Communication and Information Systems Security Symposium, 2018	90	2018
Dual adversarial semantics-consistent network for generalized zero-shot learning J Ni, S Zhang, H Xie Advances in Neural Information Processing Systems 32, 2019	68	2019
Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptation X Yue, Z Zheng, S Zhang, Y Gao, T Darrell, K Keutzer, AS Vincentelli CVPR 2021, 2021	66	2021
Self-supervised pretraining improves self-supervised pretraining CJ Reed, X Yue, A Nrusimha, S Ebrahimi, V Vijaykumar, R Mao, B Li, ... Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2022	50	2022
Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation B Li#, Y Wang#, S Zhang#, D Li, T Darrell, K Keutzer, H Zhao CVPR 2021, 2021	49	2021
Multiple source domain adaptation with adversarial learning H Zhao, S Zhang, G Wu, GJ Gordon	49	2018
Differentiable spike: Rethinking gradient-descent for training spiking neural networks Y Li, Y Guo, S Zhang, S Deng, Y Hai, S Gu Advances in Neural Information Processing Systems 34, 23426-23439, 2021	46	2021
Macow: Masked convolutional generative flow X Ma, X Kong, S Zhang, E Hovy arXiv preprint arXiv:1902.04208, 2019	44	2019
Temporal efficient training of spiking neural network via gradient re-weighting S Deng, Y Li, S Zhang, S Gu arXiv preprint arXiv:2202.11946, 2022	41	2022

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