Shu Wu

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 9 papers; trusts 585 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 153 citations into it; #32,237 of 35,345 by trust here, #10,430 by citations
• Computer Vision and Pattern Recognition — 130 citations into it
• Representation & Self-Supervised Learning — 68 citations into it
• Information Systems — 56 citations into it; #2,961 of 8,771 by trust here, #1,010 by citations
• Graph Learning — 46 citations into it
• Natural Language Processing — 36 citations into it
• Statistical and Nonlinear Physics — 28 citations into it
• Multimodal Learning — 24 citations into it

Whom Shu Wu trusts overall

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

1. Geoffrey E. Hinton
2. Tomas Mikolov
3. Yoshua Bengio
4. Diederik P. Kingma
5. Ruslan Salakhutdinov
6. Alex Krizhevsky
7. Ilya Sutskever
8. Kaiming He
9. Yann LeCun
10. Steffen Rendle

Who trusts Shu Wu overall

1. Shoujin Wang
2. Yupeng Hou
3. Chen Gao
4. Shiwen Wu
5. Wayne Xin Zhao
6. Hiromi Nakagawa
7. Kun Zhou
8. Ruihong Qiu
9. Xin Xia
10. Fei Sun

Papers from Shu Wu's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• A Comprehensive Survey on Cross-modal Retrieval (2016) — 332 citations
• A Dynamic Recurrent Model for Next Basket Recommendation (2016) — 514 citations
• A Convolutional Approach for Misinformation Identification (2017) — 508 citations
• Mining Latent Structures for Multimedia Recommendation (2021) — 428 citations
• Graph Contrastive Learning with Adaptive Augmentation (2020) — 1,475 citations
• Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks (2020) — 337 citations
• Deep Graph Contrastive Representation Learning (2020) — 1,103 citations
• Session-based Recommendation with Graph Neural Networks (2018) — 1,914 citations
• Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts (2016) — 1,026 citations