Jing Jiang

Artificial Intelligence · International Joint Conference on Artificial Intelligence · 2017–2022

A researcher in the Science corpus: 15 papers; trusts 657 researchers and is trusted by 39 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 440 citations into it; #5,916 of 35,345 by trust here, #3,757 by citations
• Computer Vision and Pattern Recognition — 171 citations into it; #16,213 of 21,575 by trust here, #18,782 by citations
• Natural Language Processing — 148 citations into it; #4,687 of 5,688 by trust here, #2,307 by citations
• Representation & Self-Supervised Learning — 88 citations into it
• Graph Learning — 69 citations into it
• Statistical and Nonlinear Physics — 65 citations into it; #3,814 of 10,127 by trust here, #2,316 by citations
• Model Architectures — 63 citations into it; #1,076 of 2,278 by trust here, #652 by citations
• Federated & Distributed Learning — 60 citations into it

Whom Jing Jiang trusts overall

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

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

Who trusts Jing Jiang overall

1. Guodong Long
2. Zezhi Shao
3. Shirui Pan
4. Yue Tan
5. Michael Hahn
6. Tianyi Zhou
7. Yixin Liu
8. Zonghan Wu
9. Zhao Zhang
10. Tao Shen

Papers from Jing Jiang's perspective

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

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

Their papers

• Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling (2018) — 148 citations
• A Universal Representation Transformer Layer for Few-Shot Image Classification (2020) — 144 citations
• DiSAN: Directional Self-Attention Network for RNN/CNN-free Language Understanding (2017) — 787 citations
• Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling (2018) — 153 citations
• Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks (2020) — 2,163 citations
• MGAE: Marginalized Graph Autoencoder for Graph Clustering (2017) — 521 citations
• Adversarially Regularized Graph Autoencoder (2018) — 489 citations
• Federated Learning from Pre-Trained Models: A Contrastive Learning Approach (2022) — 296 citations
• FedProto: Federated Prototype Learning across Heterogeneous Clients (2021) — 940 citations
• Federated Learning for Open Banking (2021) — 370 citations
• Multi-center federated learning: clients clustering for better personalization (2020) — 379 citations
• Attributed Graph Clustering: A Deep Attentional Embedding Approach (2019) — 645 citations
• Learning Graph Embedding With Adversarial Training Methods (2019) — 347 citations
• Graph WaveNet for Deep Spatial-Temporal Graph Modeling (2019) — 3,288 citations
• Fast Directional Self-Attention Mechanism (2018) — 5 citations