Aidong Zhang

Artificial Intelligence · IEEE Transactions on Knowledge and Data Engineering · 2004–2020

A researcher in the Science corpus: 7 papers; trusts 100 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 106 citations into it; #24,640 of 35,345 by trust here, #9,870 by citations
• Statistics and Probability — 85 citations into it
• Causal Inference & Discovery — 43 citations into it
• Natural Language Processing — 32 citations into it
• Molecular Biology — 31 citations into it; #12,959 of 17,773 by trust here, #11,019 by citations
• Representation & Self-Supervised Learning — 31 citations into it
• Probabilistic & Bayesian Methods — 29 citations into it
• Computer Vision and Pattern Recognition — 20 citations into it

Whom Aidong Zhang trusts overall

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

1. Geoffrey E. Hinton
2. Donald B. Rubin
3. Ilya Sutskever
4. Yoshua Bengio
5. Ruslan Salakhutdinov
6. Diederik P. Kingma
7. Alex Krizhevsky
8. Yann LeCun
9. Tomas Mikolov
10. Radford M. Neal

Who trusts Aidong Zhang overall

1. Patrik D'haeseleer

Papers from Aidong Zhang's perspective

Ordered by how strongly the researchers Aidong Zhang 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A Survey on Context Learning (2017) — 1,654 citations
• On handling negative transfer and imbalanced distributions in multiple source transfer learning (2014) — 131 citations
• LRBM: A Restricted Boltzmann Machine Based Approach for Representation Learning on Linked Data (2014) — 29 citations
• Cluster analysis for gene expression data: a survey (2004) — 1,349 citations
• A Survey on Causal Inference (2020) — 690 citations
• Representation Learning for Treatment Effect Estimation from Observational Data (2018) — 348 citations
• A Deep Learning Approach to Link Prediction in Dynamic Networks (2014) — 163 citations