Chengqi Zhang
Artificial Intelligence · International Joint Conference on Artificial Intelligence · 2014–2021
A researcher in the Science corpus: 21 papers; trusts 659 researchers and is trusted by 38 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 544 citations into it; #5,703 of 35,345 by trust here, #2,033 by citations
- Computer Vision and Pattern Recognition — 205 citations into it; #18,239 of 21,575 by trust here, #3,035 by citations
- Graph Learning — 174 citations into it; #592 of 693 by trust here, #101 by citations
- Natural Language Processing — 168 citations into it; #4,688 of 5,688 by trust here, #2,307 by citations
- Statistical and Nonlinear Physics — 154 citations into it; #5,491 of 10,127 by trust here, #2,036 by citations
- Representation & Self-Supervised Learning — 119 citations into it
- Model Architectures — 75 citations into it; #1,075 of 2,278 by trust here, #652 by citations
- Information Systems — 71 citations into it; #6,984 of 8,771 by trust here, #5,942 by citations
Whom Chengqi Zhang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Diederik P. Kingma
- Alex Krizhevsky
- Ilya Sutskever
- Tomas Mikolov
- Kaiming He
- Yann LeCun
- Ruslan Salakhutdinov
- Richard Socher
Who trusts Chengqi Zhang overall
- Shirui Pan
- Guodong Long
- Zezhi Shao
- Michael Hahn
- Yixin Liu
- Zonghan Wu
- Weiwei Jiang
- Ruiqi Hu
- Tianyi Zhou
Papers from Chengqi Zhang's perspective
Ordered by how strongly the researchers Chengqi Zhang trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- A Comprehensive Survey on Graph Neural Networks (2019) — 11,551 citations
- Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling (2018) — 148 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
- Network Representation Learning: A Survey (2017) — 693 citations
- Adversarially Regularized Graph Autoencoder (2018) — 489 citations
- Homophily, Structure, and Content Augmented Network Representation Learning (2016) — 104 citations
- FedProto: Federated Prototype Learning across Heterogeneous Clients (2021) — 940 citations
- Federated Learning for Open Banking (2021) — 370 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
- Multiple Structure-View Learning for Graph Classification (2018) — 75 citations
- User Profile Preserving Social Network Embedding (2017) — 100 citations
- Joint structure feature exploration and regularization for multi-task graph classification (2016) — 55 citations
- Tri-Party Deep Network Representation (2016) — 438 citations
- Inferring Latent Network from Cascade Data for Dynamic Social Recommendation (2016) — 13 citations
- TrGraph: Cross-Network Transfer Learning via Common Signature Subgraphs (2015) — 46 citations