Qiang Xu

Signal Processing · AAAI Conference on Artificial Intelligence · 2018–2023

A researcher in the Science corpus: 4 papers; trusts 41 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Signal Processing — 41 citations into it; #2,666 of 7,572 by trust here, #2,589 by citations
• Artificial Intelligence — 40 citations into it; #19,626 of 35,345 by trust here, #15,542 by citations
• Management Science and Operations Research — 32 citations into it
• Model Architectures — 19 citations into it
• Computer Vision and Pattern Recognition — 16 citations into it
• Time-Series & Forecasting — 16 citations into it
• Electrical and Electronic Engineering — 13 citations into it
• Building and Construction — 7 citations into it

Whom Qiang Xu trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Haixu Wu
2. Yoshua Bengio
3. Yann LeCun
4. Sujit Kumar Bose
5. Geoffrey E. Hinton
6. Dzmitry Bahdanau
7. Tian Zhou
8. Jiehui Xu
9. Boris N. Oreshkin
10. Alex Krizhevsky

Who trusts Qiang Xu overall

1. Donghao Luo
2. Xue Wang
3. Junchi Yan
4. Ailing Zeng
5. Zhijian Xu
6. Yunhao Zhang
7. Shiyu Wang
8. Nam H. Nguyen
9. Jayant R. Kalagnanam
10. Phanwadee Sinthong

Papers from Qiang Xu's perspective

Ordered by how strongly the researchers Qiang Xu trusts endorse each paper. Same corpus, a different order for every researcher.

1. Attention is All you Need (Neural Information Processing Systems)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
8. Gradient-based learning applied to document recognition
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. ImageNet: A large-scale hierarchical image database

Their papers

• SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction (2021) — 851 citations
• FITS: Modeling Time Series with 10k Parameters (2023) — 306 citations
• Are Transformers Effective for Time Series Forecasting? (2022) — 4,138 citations
• I Know What You See: Power Side-Channel Attack on Convolutional Neural Network Accelerators (2018) — 232 citations