Sotabase

Career

· Postdoctoral Researcher, University of California, Berkeley2020–
· Research Scientist, Petuum, Inc.2018–2020
· Computer Vision Deep Learning Research Intern, Adobe2017–2017
· Research Assistant, Carnegie Mellon University2013–2018
· Research Assistant, Peking University2010–2013

Publications (106)

Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
AAAI Conference on Artificial Intelligence · 2020
6,368
cited
Adversarial Multiple Source Domain Adaptation
Neural Information Processing Systems · 2018
587
cited
Topology adaptive graph convolutional networks
arXiv.org · 2017
365
cited
Instance Adaptive Self-Training for Unsupervised Domain Adaptation
European Conference on Computer Vision · 2020
337
cited
Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting
International Conference on Learning Representations · 2022
332
cited
A Review of Single-Source Deep Unsupervised Visual Domain Adaptation
IEEE Transactions on Neural Networks and Learning Systems · 2020
316
cited
Differentiable Spike: Rethinking Gradient-Descent for Training Spiking Neural Networks
Neural Information Processing Systems · 2021
249
cited
Q-Diffusion: Quantizing Diffusion Models
IEEE International Conference on Computer Vision · 2023
244
cited
Multi-source Distilling Domain Adaptation
AAAI Conference on Artificial Intelligence · 2019
243
cited
PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning
IEEE International Conference on Computer Vision · 2022
223
cited
FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras
IEEE International Conference on Computer Vision · 2017
216
cited
Outlier Suppression: Pushing the Limit of Low-bit Transformer Language Models
Neural Information Processing Systems · 2022
199
cited
Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptation
Computer Vision and Pattern Recognition · 2021
186
cited
Understanding Traffic Density from Large-Scale Web Camera Data
Computer Vision and Pattern Recognition · 2017
142
cited
Self-Supervised Pretraining Improves Self-Supervised Pretraining
IEEE Workshop/Winter Conference on Applications of Computer Vision · 2021
129
cited
Delving Deep into the Generalization of Vision Transformers under Distribution Shifts
Computer Vision and Pattern Recognition · 2021
128
cited
A Deep Learning Approach to IoT Authentication
2018 IEEE International Conference on Communications (ICC) · 2018
121
cited
Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation
Computer Vision and Pattern Recognition · 2020
113
cited
PiMAE: Point Cloud and Image Interactive Masked Autoencoders for 3D Object Detection
Computer Vision and Pattern Recognition · 2023
95
cited
Margin-Based Few-Shot Class-Incremental Learning with Class-Level Overfitting Mitigation
Neural Information Processing Systems · 2022
92
cited
Sotabase
Shanghang Zhang | Researcher Profile | Sotabase | Sotabase